WMS This resource contains satellite imagery the Marshall Islands. The imagery was collected from 2019 through 2020. More specially, this resource contains a raster file of Sentinel-2 RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The SENTINEL-2 Multispectral Instrument (MSI) samples 13 spectral bands: four bands at 10 metres, six bands at 20 metres and three bands at 60 metres spatial resolution. The acquired data, mission coverage and high revisit frequency provides for the generation of geoinformation at local, regional, national and international scales. The data is designed to be modified and adapted by users interested in thematic areas such as: • spatial planning • agro-environmental monitoring • water monitoring • forest and vegetation monitoring • land carbon, natural resource monitoring • global crop monitoring imagery earth observation Sentinel-2 EPSG:32659 159.65113827039903 172.95809186230989 4.35048564345048 15.387462713675648 pipap:World Database on Protected Areas Group The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. WDPA Protected Planet BIOPAMA PIPAP protected areas EPSG:3857 -180.00000000000003 180.00000000000003 -85.06 85.06 text/plain cok:COK_2019_DEM1arcsecNorthIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. COK_2019_DEM1arcsecNorthIslands_ASTERv3 WCS GeoTIFF ASTER DEM elevation Cook Islands EPSG:4326 CRS:84 -166.00013888888893 -156.99986111111124 -12.000138888888845 -7.99986111111111 text/plain cok:COK_2019_DEM1arcsecSouthIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. COK_2019_DEM1arcsecSouthIslands_ASTERv3 WCS GeoTIFF ASTER DEM elevation EPSG:4326 CRS:84 -160.000138888889 -156.99986111111127 -22.00013888888884 -17.9998611111111 text/plain cok:COK_2019_Hillshade1arcsecNorthIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. COK_2019_Hillshade1arcsecNorthIslands_ASTERv3 WCS GeoTIFF ASTER Cook Islands EPSG:4326 CRS:84 -166.00013888888893 -156.99986111111124 -12.000138888888845 -7.99986111111111 text/plain cok:COK_2019_Hillshade1arcsecSouthIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. COK_2019_Hillshade1arcsecSouthIslands_ASTERv3 WCS GeoTIFF ASTER Cook Islands hillshade EPSG:4326 CRS:84 -160.000138888889 -156.99986111111127 -22.00013888888884 -17.999861111111116 text/plain cok:COK_2020_ImagerySouthernIslands_Sentinel2 This resource contains satellite imagery for the Southern Islands Group in the Cook Islands. The imagery was collected on May 11, May 14, May 16, May 26, and June 30, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200511T205041_N0214_R071_T04KFC_20200512T001943 S2A_MSIL2A_20200630T205041_N0214_R071_T04KFD_20200701T003301 S2A_MSIL2A_20200514T210041_N0214_R114_T04KDE_20200515T004012 S2A_MSIL2A_20200511T205041_N0214_R071_T04KED_20200512T001943 S2A_MSIL2A_20200511T205041_N0214_R071_T04KEC_20200512T001943 S2B_MSIL2A_20200526T205039_N0214_R071_T04KDB_20200526T222952 S2B_MSIL2A_20200516T205039_N0214_R071_T04KFA_20200516T223810 S2A_MSIL2A_20200630T205041_N0214_R071_T04KEC_20200701T003301 COK_2020_ImagerySouthernIslands_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Cook Islands EPSG:4326 CRS:84 -160.509833216 -156.565817931 -21.945525092 -18.689360494 text/plain fji:FJI_2019-2020_ImageryVanuaLevuTaveuni_Sentinel2 This resource contains satellite imagery for the Vavua Levu and Taveuni islands in Fiji. The imagery was collected on June 8 , 2019, January 4, 2020, February 3, 2020, February 23, 2020, July 22, 2020, August 9, 2020, and August 21, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20190608T221949_N0212_R029_T01LAC_20190609T000519 S2B_MSIL2A_20200104T221939_N0213_R029_T60KYG_20200105T000555 S2B_MSIL2A_20200203T221929_N0213_R029_T60KXG_20200204T000453 S2B_MSIL2A_20200223T221939_N0214_R029_T01KBB_20200224T000431 S2B_MSIL2A_20200223T221939_N0214_R029_T01LBC_20200224T000431 S2B_MSIL2A_20200722T221939_N0214_R029_T60LYH_20200722T235608 S2A_MSIL2A_20200809T223021_N0214_R072_T60KXG_20200810T002802 S2B_MSIL2A_20200821T221939_N0214_R029_T60LXH_20200821T235025 FJI_2019-2020_ImageryVanuaLevuTaveuni_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Fiji EPSG:32760 CRS:84 -180.0 180.0 -17.312822267495616 -15.70327852362702 text/plain fji:FJI_2019-2020_ImageryVitiLevu_Sentinel2 This resource contains satellite imagery for Viti Levu in Fiji. The imagery was collected on September 5, 2019, September 7, 2019, March 15, 2020, and July 6, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20190904T223011_N0213_R072_T60KXF_20190905T001810 S2B_MSIL2A_20190906T221939_N0213_R029_T60KXF_20190907T002727 S2B_MSIL2A_20200314T221939_N0214_R029_T60KXE_20200315T002807 S2B_MSIL2A_20200705T223009_N0214_R072_T60KWE_20200706T001130 S2B_MSIL2A_20200705T223009_N0214_R072_T60KWF_20200706T001130 FJI_2019-2020_ImageryVitiLevu_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Fiji EPSG:32760 CRS:84 176.77719022180642 178.9897609933347 -18.636830280487185 -17.173793171008448 text/plain fji:FJI_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. FJI_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Fiji EPSG:3832 CRS:84 -180.0 180.0 -22.000354229559466 -11.999861111110276 text/plain fji:FJI_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. FJI_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF hillshade ASTER Fiji EPSG:3832 CRS:84 -180.0 180.0 -22.000354229559466 -11.99986111111039 text/plain fji:FJI_2020_ImageryKadavu_Sentinel2 This resource contains satellite imagery for Kadavu in Fiji. The imagery was collected on February 23, March 14, and August 14, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20200223T221939_N0214_R029_T60KXD_20200224T000431 S2B_MSIL2A_20200314T221939_N0214_R029_T60KXE_20200315T002807 S2B_MSIL2A_20200814T223009_N0214_R072_T60KWD_20200814T235803 FJI_2020_ImageryKadavu_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Fiji EPSG:32760 CRS:84 177.8533159928529 178.69494329254127 -19.231196050724567 -18.565705700998894 text/plain fji:FJI_2020_ImageryYasawaMamanuca_Sentinel2 This resource contains satellite imagery for the Yasawa and Mamanuca Islands in Fiji. The imagery was collected on July 6, September 28, and October 9, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20200705T223009_N0214_R072_T60KWG_20200706T001130 S2B_MSIL2A_20200705T223009_N0214_R072_T60KWF_20200706T001130 S2A_MSIL2A_20200928T223021_N0214_R072_T60KVF_20200928T235635 S2A_MSIL2A_20201008T223021_N0214_R072_T60KVG_20201009T001515 FJI_2020_ImageryYasawaMamanuca_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Fiji EPSG:32760 CRS:84 176.7032030008189 177.71499926596604 -17.939808031756353 -16.63133699923546 text/plain fsm:FSM_2005_DEM10mChuuk_USGS A 10-meter resolution land surface digital elevation model (DEM) for the islands of Chuuk in the Federated States of Micronesia (FSM) from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. FSM_2005_DEM10mChuuk_USGS WCS GeoTIFF DEM Elevation USGS Micronesia EPSG:4326 CRS:84 151.4475901388118 152.0867596446419 7.065092881732372 7.680651171571348 text/plain text/plain fsm:FSM_2005_DEM10mKosrae_USGS A 10-meter resolution land surface digital elevation model (DEM) for the island of Kosrae in the Federated States of Micronesia (FSM) from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. FSM_2005_DEM10mKosrae_USGS WCS GeoTIFF USGS Hillshade Micronesia EPSG:4326 CRS:84 162.89453656857302 163.05750054896657 5.251201849722848 5.385924913059563 text/plain text/plain fsm:FSM_2005_DEM10mPohnpei_USGS A 10-meter resolution land surface digital elevation model (DEM) for the island of Pohnpei in the Federated States of Micronesia (FSM) from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. FSM_2005_DEM10mPohnpei_USGS WCS GeoTIFF USGS DEM elevation Micronesia EPSG:4326 CRS:84 158.0849037217906 158.37610939552889 6.7587945795512 7.036574236535259 text/plain text/plain fsm:FSM_2005_Hillshade10mChuuk_USGS Hillshade derived from USGS 10-meter DEM. FSM_2005_Hillshade10mChuuk_USGS WCS GeoTIFF hillshade USGS Micronesia EPSG:4326 CRS:84 151.4475901388118 152.0867596446419 7.065092881732372 7.680651171571353 text/plain text/plain fsm:FSM_2005_Hillshade10mKosrae_USGS Hillsahde for Kosrae derived from USGS 10-meter DEM. FSM_2005_Hillshade10mKosrae_USGS WCS GeoTIFF hillshade USGS Kosrae Micronesia EPSG:4326 CRS:84 162.89453656857302 163.05750054896657 5.251201849722848 5.385924913059566 text/plain fsm:FSM_2005_Hillshade10mPohnpei_USGS A 10-meter resolution land surface hillshade for the island of Pohnpei in the Federated States of Micronesia (FSM) from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. FSM_2005_Hillshade10mPohnpei_USGS WCS GeoTIFF hillshade USGS Micronesia EPSG:4326 CRS:84 158.0849037217906 158.37610939552889 6.7587945795512 7.03657423653526 text/plain text/plain fsm:FSM_2016_ImageryKosrae_Sentinel2 Satellite imagery of Kosrae derived from 2016 Sentinel-2 satellite. FSM_2016_ImageryKosrae_Sentinel2 WCS GeoTIFF imagery Kosrae Sentinel-2 Micronesia EPSG:32658 CRS:84 162.7098788675485 163.22256491635443 5.10732499851115 5.524612467165803 text/plain fsm:FSM_2019_ImageryPohnpei_Sentinel2 This resource contains satellite imagery for Pohnpei. The imagery was collected on June 12, 2018, February 27, 2019, and September 10, 2019. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. FSM_2019_ImageryPohnpei_Sentinel2 WCS GeoTIFF Sentinel-2 RGB imagery Micronesia EPSG:32657 CRS:84 157.73186596025445 158.38420204261527 6.591400268203991 7.113738930454767 text/plain fsm:FSM_2019_ImageryYap_Sentinel2 This resource contains satellite imagery for Yap. The imagery was collected on April 12, 2019. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. FSM_2019_ImageryYap_Sentinel2 WCS GeoTIFF Sentinel-2 imagery RGB earth observation Micronesia EPSG:32655 CRS:84 137.79964223596693 138.46088824575165 9.254576445451201 9.785971476256643 text/plain global:Global_2000-2014_SurfaceCalciteMean_BioOracle2 Raster data representing the mean levels of calcite in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceCalciteMean_BioOracle2 WCS GeoTIFF calcite sea surface BioOracle EPSG:4326 CRS:84 -179.648249372 179.637254127 -64.85343325800001 64.825355795 text/plain global:Global_2000-2014_SurfaceChlorophyllMean_BioOracle2 Raster data representing the mean levels of chlorophyll in mg/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceChlorophyllMean_BioOracle2 WCS GeoTIFF sea surface chlorophyll Bio-Oracle 2 EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceCurrentVelocityMean_BioOracle2 Raster data representing the mean levels of current velocities in meters/second for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceCurrentVelocityMean_BioOracle2 WCS GeoTIFF sea surface current velocity BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceDissolvedOxygenMean_BioOracle2 Raster data representing the mean levels of dissolved oxygen in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceDissolvedOxygenMean_BioOracle2 WCS GeoTIFF sea surface dissolved oxygen BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceIronMean_BioOracle2 Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceIronMean_BioOracle2 WCS GeoTIFF BioOracle iron sea surface EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceNitrateMean_BioOracle2 Raster data representing the mean levels of nitrate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceNitrateMean_BioOracle2 WCS GeoTIFF nitrate sea surface BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfacePARMean_BioOracle2 Raster data representing the mean levels of photosynthetically active radiation (PAR) in E/m2/year for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Raster data representing the mean levels of photosynthetically active radiation (PAR) in E/m2/year for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfacePARMean_BioOracle2 WCS GeoTIFF sea surface PAR photosynthetically active radiation BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfacePhosphateMean_BioOracle2 Raster data representing the mean levels of phosphate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfacePhosphateMean_BioOracle2 WCS GeoTIFF sea surface phosphate BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfacePhytoplanktonMean_BioOracle2 Raster data representing the mean levels of phytoplankton in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Raster data representing the mean levels of phytoplankton in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfacePhytoplanktonMean_BioOracle2 WCS GeoTIFF sea surface phytoplankton BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceSalinityMean_BioOracle2 Raster data representing the mean levels of salinity in practical salinity scale (PSS) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Raster data representing the mean levels of salinity in practical salinity scale (PSS) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceSalinityMean_BioOracle2 WCS GeoTIFF salinity sea surface BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceSilicateMean_BioOracle2 Raster data representing the mean levels of silicate in µmol/m3 for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceSilicateMean_BioOracle2 WCS GeoTIFF silicate sea surface BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfaceTemperatureMean_BioOracle2 Raster data representing the mean levels of temperature in degrees Celsius (°C) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Raster data representing the mean levels of temperature in degrees Celsius (°C) for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfaceTemperatureMean_BioOracle2 WCS GeoTIFF BioOracle sea surface temperature EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000-2014_SurfacepHMean_BioOracle2 Raster data representing the mean levels of pH for the surface water layer. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator). Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation. Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask. The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography. Global_2000-2014_SurfacepHMean_BioOracle2 WCS GeoTIFF pH sea surface BioOracle EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2000_PopulationDensity30sec_GPWv4 The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions. Global_2000_PopulationDensity30sec_GPWv4 WCS GeoTIFF Population Gridded World Population EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99999999999991 text/plain global:Global_2005_PopulationDensity30sec_GPWv4 The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions. Global_2005_PopulationDensity30sec_GPWv4 WCS GeoTIFF population Gridded World Population EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99999999999991 text/plain global:Global_2010_PopulationDensity30sec_GPWv4 The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2010. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR. Global_2010_PopulationDensity30sec_GPWv4 WCS GeoTIFF population density world EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99999999999991 text/plain global:Global_2011_KnollsBaseArea_ZSL This dataset shows the global distribution of knolls identified using global bathymetric data at 30 arc-sec resolution. A total of 138,412 knolls were identified, representing the largest global set of identified knolls to date. Knoll habitat was found to constitute approximately 16.3% of the ocean floor. The research leading to these results received funding from the European Community’s Seventh Framework Programme, and from the International Union for Conservation of Nature (IUCN). Please use the following citation for this dataset: Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers 58: 442-453. doi: 10.1016/j.dsr.2011.02.004. Data URL: http://data.unep-wcmc.org/datasets/41 features Global_2011_KnollsBaseArea_ZSL knolls seafloor marine EPSG:4326 CRS:84 -181.25416666666666 181.05000000000004 -77.88693033854166 85.17556966145838 text/plain global:Global_2011_Knolls_ZSL This dataset shows the global distribution of knolls identified using global bathymetric data at 30 arc-sec resolution. A total of 138,412 knolls were identified, representing the largest global set of identified knolls to date. Knoll habitat was found to constitute approximately 16.3% of the ocean floor. The research leading to these results received funding from the European Community’s Seventh Framework Programme, and from the International Union for Conservation of Nature (IUCN). Please use the following citation for this dataset: Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers 58: 442-453. doi: 10.1016/j.dsr.2011.02.004. Data URL: http://data.unep-wcmc.org/datasets/41 features Global_2011_Knolls_ZSL knolls seafloor marine EPSG:4326 CRS:84 -179.98749999999998 179.98333333333338 -77.79526367187499 84.99223632812505 text/plain global:Global_2011_SeamountsBaseArea_ZSL This dataset shows the global distribution of seamounts identified using global bathymetric data at 30 arc-sec resolution. A total of 33,452 seamounts were identified, representing the largest global set of identified seamounts to date. Seamount habitat was found to constitute approximately 4.7% of the ocean floor. The research leading to these results received funding from the European Community’s Seventh Framework Programme, and from the International Union for Conservation of Nature (IUCN). Please use the following citation for this dataset: Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers 58: 442-453. doi: 10.1016/j.dsr.2011.02.004. Data URL: http://data.unep-wcmc.org/datasets/41 features Global_2011_SeamountsBaseArea_ZSL seafloor seamount EPSG:4326 CRS:84 -180.000015 179.999985 -75.28273700520833 85.16306299479167 text/plain global:Global_2011_Seamounts_ZSL This dataset shows the global distribution of seamounts identified using global bathymetric data at 30 arc-sec resolution. A total of 33,452 seamounts were identified, representing the largest global set of identified seamounts to date. Seamount habitat was found to constitute approximately 4.7% of the ocean floor. The research leading to these results received funding from the European Community’s Seventh Framework Programme, and from the International Union for Conservation of Nature (IUCN). Please use the following citation for this dataset: Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers 58: 442-453. doi: 10.1016/j.dsr.2011.02.004. Data URL: http://data.unep-wcmc.org/datasets/41 features Global_2011_Seamounts_ZSL seafloor seamount EPSG:4326 CRS:84 -179.979166666999 179.983333332999 -75.1452636718999 84.9797363281 text/plain global:Global_2013_Abyss_BlueHabitats Global distribution of abyss seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Abyss_BlueHabitats abyss geomorphic seafloor marine EPSG:4326 CRS:84 -179.99999999999997 180.00000000000034 -76.42115940356985 89.99999999999994 text/plain global:Global_2013_AbyssalClassification_BlueHabitats Global distribution of abyssal class seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_AbyssalClassification_BlueHabitats abyssal class seafloor geomorphic marine EPSG:4326 CRS:84 -180.00000000044744 180.0000000017788 -76.42115940385702 89.99999999966897 text/plain global:Global_2013_Basins_BlueHabitats Global distribution of basin seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Basins_BlueHabitats basin seafloor geomorphic marine EPSG:4326 CRS:84 -180.0 180.00000000000034 -78.60166015337172 89.99999366924888 text/plain global:Global_2013_Bridges_BlueHabitats Global distribution of bridge seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Bridges_BlueHabitats bridge seafloor geomorphic marine EPSG:4326 CRS:84 -178.46229435696867 178.03710848253218 -69.18571478490048 83.11178958991215 text/plain global:Global_2013_Canyons_BlueHabitats Global distribution of canyon seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Canyons_BlueHabitats canyon marine seafloor geomorphic EPSG:4326 CRS:84 -179.99998386345104 180.0 -76.54034688798612 89.66331039344334 text/plain global:Global_2013_Escarpments_BlueHabitats Global distribution of escarpment seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Escarpments_BlueHabitats escarpment seafloor marine geomorphic EPSG:4326 CRS:84 -180.0 180.0000000000001 -76.98917898581297 89.60219984768582 text/plain global:Global_2013_Fans_BlueHabitats Global distribution of fan seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Fans_BlueHabitats fan seafloor geomorphic marine EPSG:4326 CRS:84 -176.52427546579818 179.96162990770168 -76.42115940356986 84.56918262932129 text/plain global:Global_2013_GlacialTroughs_BlueHabitats Global distribution of glacial trough seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_GlacialTroughs_BlueHabitats marine seafloor geomorphic glacial trough EPSG:4326 CRS:84 -180.0 180.0 -78.60166015337177 83.79608868525094 text/plain global:Global_2013_Guyots_BlueHabitats Global distribution of guyot seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Guyots_BlueHabitats guyot seafloor geomorphic marine EPSG:4326 CRS:84 -179.9472129674865 178.55731670575892 -69.01585040428131 57.64146887290178 text/plain global:Global_2013_Hadal_BlueHabitats Global distribution of hadal seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Hadal_BlueHabitats marine seafloor geomorphic hadal EPSG:4326 CRS:84 -179.99999999999997 180.0 -60.94928014437488 55.10833333086282 text/plain global:Global_2013_HoloceneEruptions_SmithsonianVOTW The Global Volcanism Program database currently contains 1422 volcanoes with eruptions during the Holocene period (approximately the last 10,000 years). This resource contains the mapped locations of the Holocene volcanoes thoughout the world in shapefile format. The Smithsonian Institution's Global Volcanism Program (GVP) is housed in the Department of Mineral Sciences, National Museum of Natural History, in Washington D.C. We are devoted to a better understanding of Earth's active volcanoes and their eruptions during the last 10,000 years. The mission of GVP is to document, understand, and disseminate information about global volcanic activity. We do this through four core functions: reporting, archiving, research, and outreach. The data systems that lie at our core have been in development since 1968 when GVP began documenting the eruptive histories of volcanoes. Reporting. GVP is unique in its documentation of current and past activity for all volcanoes on the planet active during the last 10,000 years. During the early stages of an eruption anywhere in the world we act as a clearinghouse of reports, data, and imagery. Reports are released in two formats. The Smithsonian / USGS Weekly Volcanic Activity Report provides timely information vetted by GVP staff about current eruptions. The Bulletin of the Global Volcanism Network provides comprehensive reporting on recent eruptions on a longer time horizon to allow incorporation of peer-reviewed literature and observatory reports. Archiving. Complementing our effort toward reporting of current eruptive activity is our database of volcanoes and eruptions that documents the last 10,000 years of Earth's volcanism. These databases and interpretations based on them were published in three editions of the book "Volcanoes of the World". Research. GVP researchers are curators in the Department of Mineral Sciences and maintain active research programs on volcanic products, processes, and the deep Earth that is the ultimate source of volcanism. Outreach. This website presents more than 7,000 reports on volcanic activity, provides access to the baseline data and eruptive histories of Holocene volcanoes, and makes available other resources to our international partners, scientists, civil-authorities, and the public. The Global Volcanism Program relies on an international network of collaborating individuals, programs and organizations, many of which are listed below: United States Geological Survey Volcano Hazards Program (USA). The Volcano Hazards Program monitors active and potentially active volcanoes, assesses their hazards, responds to volcanic crises, and conducts research on volcanoes. The Volcano Disaster Assistance Program (VDAP) (with the U.S. Office of Foreign Disaster Assistance) works to reduce fatalities and economic losses in countries experiencing a volcano emergency. Global Volcano Model (Bristol University and the British Geological Survey, UK). GVM is a growing international network that aims to create a sustainable, accessible information platform on volcanic hazard and risk. WOVOdat (Earth Observatory of Singapore). A collective record of volcano monitoring, worldwide - brought to you by the WOVO (World Organization of Volcano Observatories). Integrated Earth Data Applications (Lamont-Doherty Earth Observatory of Columbia University, USA). A community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences. VHub (The State University of New York at Buffalo, USA). An online resource for collaboration in volcanology research and risk mitigation. International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI). IAVCEI represents the primary international focus for: (1) research in volcanology, (2) efforts to mitigate volcanic disasters, and (3) research into closely related disciplines, such as igneous geochemistry and petrology, geochronology, volcanogenic mineral deposits, and the physics of the generation and ascent of magmas in the upper mantle and crust. IAVCEI has charged GVP with providing the official names and unique identifier numbers for the world's volcanoes. National Oceanographic and Atmospheric Administration (NOAA). Volcanic Ash Advisory Centers (VAACs) The International Civil Aviation Organization (ICAO) has established nine Volcanic Ash Advisory Centers tasked with monitoring Volcanic Ash plumes within their assigned airspace. Global_2013_HoloceneEruptions_SmithsonianVOTW features volcano eruption EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/plain global:Global_2013_HoloceneVolcanoes_SmithsonianVOTW The Global Volcanism Program database currently contains 1422 volcanoes with eruptions during the Holocene period (approximately the last 10,000 years). This resource contains the mapped locations of the Holocene volcanoes thoughout the world in shapefile format. The Smithsonian Institution's Global Volcanism Program (GVP) is housed in the Department of Mineral Sciences, National Museum of Natural History, in Washington D.C. We are devoted to a better understanding of Earth's active volcanoes and their eruptions during the last 10,000 years. The mission of GVP is to document, understand, and disseminate information about global volcanic activity. We do this through four core functions: reporting, archiving, research, and outreach. The data systems that lie at our core have been in development since 1968 when GVP began documenting the eruptive histories of volcanoes. Reporting. GVP is unique in its documentation of current and past activity for all volcanoes on the planet active during the last 10,000 years. During the early stages of an eruption anywhere in the world we act as a clearinghouse of reports, data, and imagery. Reports are released in two formats. The Smithsonian / USGS Weekly Volcanic Activity Report provides timely information vetted by GVP staff about current eruptions. The Bulletin of the Global Volcanism Network provides comprehensive reporting on recent eruptions on a longer time horizon to allow incorporation of peer-reviewed literature and observatory reports. Archiving. Complementing our effort toward reporting of current eruptive activity is our database of volcanoes and eruptions that documents the last 10,000 years of Earth's volcanism. These databases and interpretations based on them were published in three editions of the book "Volcanoes of the World". Research. GVP researchers are curators in the Department of Mineral Sciences and maintain active research programs on volcanic products, processes, and the deep Earth that is the ultimate source of volcanism. Outreach. This website presents more than 7,000 reports on volcanic activity, provides access to the baseline data and eruptive histories of Holocene volcanoes, and makes available other resources to our international partners, scientists, civil-authorities, and the public. The Global Volcanism Program relies on an international network of collaborating individuals, programs and organizations, many of which are listed below: United States Geological Survey Volcano Hazards Program (USA). The Volcano Hazards Program monitors active and potentially active volcanoes, assesses their hazards, responds to volcanic crises, and conducts research on volcanoes. The Volcano Disaster Assistance Program (VDAP) (with the U.S. Office of Foreign Disaster Assistance) works to reduce fatalities and economic losses in countries experiencing a volcano emergency. Global Volcano Model (Bristol University and the British Geological Survey, UK). GVM is a growing international network that aims to create a sustainable, accessible information platform on volcanic hazard and risk. WOVOdat (Earth Observatory of Singapore). A collective record of volcano monitoring, worldwide - brought to you by the WOVO (World Organization of Volcano Observatories). Integrated Earth Data Applications (Lamont-Doherty Earth Observatory of Columbia University, USA). A community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences. VHub (The State University of New York at Buffalo, USA). An online resource for collaboration in volcanology research and risk mitigation. International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI). IAVCEI represents the primary international focus for: (1) research in volcanology, (2) efforts to mitigate volcanic disasters, and (3) research into closely related disciplines, such as igneous geochemistry and petrology, geochronology, volcanogenic mineral deposits, and the physics of the generation and ascent of magmas in the upper mantle and crust. IAVCEI has charged GVP with providing the official names and unique identifier numbers for the world's volcanoes. National Oceanographic and Atmospheric Administration (NOAA). Volcanic Ash Advisory Centers (VAACs) The International Civil Aviation Organization (ICAO) has established nine Volcanic Ash Advisory Centers tasked with monitoring Volcanic Ash plumes within their assigned airspace. features Global_2013_HoloceneVolcanoes_SmithsonianVOTW volcano Holocene EPSG:4326 CRS:84 -179.97 179.58 -78.5 85.608 text/plain global:Global_2013_Plateaus_BlueHabitats Global distribution of plateau seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Plateaus_BlueHabitats plateau marine geomorphic seafloor EPSG:4326 CRS:84 -180.0 180.00000000000023 -73.32857808874456 89.6167513120335 text/plain global:Global_2013_PleistoceneVolcanoes_SmithsonianVOTW The Global Volcanism Program database currently contains 1242 volcanoes thought to have been active during the Pleistocene period (approximately the last 2.5 million years); volcanoes active in the past approximately 10,000 years are on the Holocene list and are not duplicated here. This resource contains the mapped locations of the Pleistocene volcanoes thoughout the world in shapefile format. The Smithsonian Institution's Global Volcanism Program (GVP) is housed in the Department of Mineral Sciences, National Museum of Natural History, in Washington D.C. We are devoted to a better understanding of Earth's active volcanoes and their eruptions during the last 10,000 years. The mission of GVP is to document, understand, and disseminate information about global volcanic activity. We do this through four core functions: reporting, archiving, research, and outreach. The data systems that lie at our core have been in development since 1968 when GVP began documenting the eruptive histories of volcanoes. Reporting. GVP is unique in its documentation of current and past activity for all volcanoes on the planet active during the last 10,000 years. During the early stages of an eruption anywhere in the world we act as a clearinghouse of reports, data, and imagery. Reports are released in two formats. The Smithsonian / USGS Weekly Volcanic Activity Report provides timely information vetted by GVP staff about current eruptions. The Bulletin of the Global Volcanism Network provides comprehensive reporting on recent eruptions on a longer time horizon to allow incorporation of peer-reviewed literature and observatory reports. Archiving. Complementing our effort toward reporting of current eruptive activity is our database of volcanoes and eruptions that documents the last 10,000 years of Earth's volcanism. These databases and interpretations based on them were published in three editions of the book "Volcanoes of the World". Research. GVP researchers are curators in the Department of Mineral Sciences and maintain active research programs on volcanic products, processes, and the deep Earth that is the ultimate source of volcanism. Outreach. This website presents more than 7,000 reports on volcanic activity, provides access to the baseline data and eruptive histories of Holocene volcanoes, and makes available other resources to our international partners, scientists, civil-authorities, and the public. The Global Volcanism Program relies on an international network of collaborating individuals, programs and organizations, many of which are listed below: United States Geological Survey Volcano Hazards Program (USA). The Volcano Hazards Program monitors active and potentially active volcanoes, assesses their hazards, responds to volcanic crises, and conducts research on volcanoes. The Volcano Disaster Assistance Program (VDAP) (with the U.S. Office of Foreign Disaster Assistance) works to reduce fatalities and economic losses in countries experiencing a volcano emergency. Global Volcano Model (Bristol University and the British Geological Survey, UK). GVM is a growing international network that aims to create a sustainable, accessible information platform on volcanic hazard and risk. WOVOdat (Earth Observatory of Singapore). A collective record of volcano monitoring, worldwide - brought to you by the WOVO (World Organization of Volcano Observatories). Integrated Earth Data Applications (Lamont-Doherty Earth Observatory of Columbia University, USA). A community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences. VHub (The State University of New York at Buffalo, USA). An online resource for collaboration in volcanology research and risk mitigation. International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI). IAVCEI represents the primary international focus for: (1) research in volcanology, (2) efforts to mitigate volcanic disasters, and (3) research into closely related disciplines, such as igneous geochemistry and petrology, geochronology, volcanogenic mineral deposits, and the physics of the generation and ascent of magmas in the upper mantle and crust. IAVCEI has charged GVP with providing the official names and unique identifier numbers for the world's volcanoes. National Oceanographic and Atmospheric Administration (NOAA). Volcanic Ash Advisory Centers (VAACs) The International Civil Aviation Organization (ICAO) has established nine Volcanic Ash Advisory Centers tasked with monitoring Volcanic Ash plumes within their assigned airspace. features Global_2013_PleistoceneVolcanoes_SmithsonianVOTW Pleistocene volcano EPSG:4326 CRS:84 -179.957 179.953 -78.58 79.43 text/plain global:Global_2013_Ridges_BlueHabitats Global distribution of ridge seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Ridges_BlueHabitats seafloor marine geomorphic ridge EPSG:4326 CRS:84 -179.99999999999997 180.0 -72.40031187547802 89.55005810622362 text/plain global:Global_2013_Rises_BlueHabitats Global distribution of rise seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Rises_BlueHabitats rise seafloor geomorphic marine EPSG:4326 CRS:84 -179.99999999999997 180.00000000000034 -76.42115940356985 84.53746405524777 text/plain global:Global_2013_Seamounts_BlueHabitats Global distribution of seamount seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Seamounts_BlueHabitats seamount geomorphic seafloor marine EPSG:4326 CRS:84 -179.99999999999997 180.0 -75.2333303625266 77.08469688023433 text/plain global:Global_2013_ShelfClassification_BlueHabitats Global distribution of shelf class seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_ShelfClassification_BlueHabitats shelf class marine seafloor geomorphic EPSG:4326 CRS:84 -179.9999735907981 179.99998683840715 -78.6869716641077 84.2073540820156 text/plain global:Global_2013_Shelf_BlueHabitats Global distribution of shelf seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Shelf_BlueHabitats shelf marine seafloor geomorphic EPSG:4326 CRS:84 -179.99999999994986 180.00000000010016 -78.68697166446958 84.20735408192564 text/plain global:Global_2013_Sills_BlueHabitats Global distribution of sill seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Sills_BlueHabitats sill seafloor geomorphic marine EPSG:4326 CRS:84 -176.84064404861644 178.68167396082617 -76.75350550123694 88.4415965946489 text/plain global:Global_2013_Slope_BlueHabitats Global distribution of slope seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Slope_BlueHabitats slope geomorphic seafloor marine EPSG:4326 CRS:84 -179.99999999999997 180.00000000005025 -76.51648330682184 84.54081347288451 text/plain global:Global_2013_SpreadingRidges_BlueHabitats Global distribution of spreading ridge seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_SpreadingRidges_BlueHabitats spreading ridge seafloor geomorphic marine EPSG:4326 CRS:84 -178.98549524070526 178.13836748055974 -68.02354460589049 87.88849534687586 text/plain global:Global_2013_Terraces_BlueHabitats Global distribution of terrace seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Terraces_BlueHabitats marine seafloor geomorphic terrace EPSG:4326 CRS:84 -179.9999377157567 179.9999649724998 -74.68680882677654 88.86009699676168 text/plain global:Global_2013_Trenches_BlueHabitats Global distribution of trench seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. features Global_2013_Trenches_BlueHabitats trench seafloor geomorphic marine EPSG:4326 CRS:84 -179.99999999999997 180.0 -61.06267903428227 55.52645152623984 text/plain global:Global_2013_Troughs_BlueHabitats Global distribution of trough seafloor geomorphic features. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/ Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. Global_2013_Troughs_BlueHabitats features trough seafloor geomorphic marine EPSG:4326 CRS:84 -179.99999999999997 180.0 -71.9051387451247 89.04938205712966 text/plain global:Global_2015_PopulationDensity30sec_GPWv4 The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions. Global_2015_PopulationDensity30sec_GPWv4 WCS GeoTIFF Gridded World Population population EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99999999999991 text/plain global:Global_2016_MangroveDistribution_GMWv2 The Global Mangrove Watch (GMW) is a collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The GMW aims to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective of the GMW has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation. The GMW has generated a global baseline map of mangroves for 2010 using ALOS PALSAR and Landsat (optical) data, and changes from this baseline for 2016 derived from JERS-1, ALOS and ALOS-2. Annual maps are planned from 2018 and onwards. Citations: Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669. Other cited references: Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change features Global_2016_MangroveDistribution_GMWv2 mangrove distribution global marine EPSG:4326 CRS:84 -175.33955555555622 179.97955555555458 -38.85666666666581 33.799333333333536 text/plain global:Global_2018_ModelledSeagrass30arcsec_IMSUA This is a MaxEnt model map of the global distribution of the seagrass biome. Species occurrence records were extracted from the Global Biodiversity Information Facility (GBIF), United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC) Ocean Data Viewer and Ocean biogeographic information system (OBIS). This map shows the suitable habitats for the seagrass distribution at global scale. Citation: Jayathilake D.R.M., Costello M.J. 2018. A modelled global distribution of the seagrass biome. Biological Conservation. https://doi.org/10.1016/j.biocon.2018.07.009 Use Constraints: Creative Commons Attribution 4.0 Unported (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/. features Global_2018_ModelledSeagrass30arcsec_IMSUA seagrass distribution marine UNEP WCMC EPSG:3975 CRS:84 -179.99998854118687 179.99998854118687 -43.54999999566708 69.60393269438443 text/plain global:Global_2020_HydrothermalVents_InterRidgeVentsDatabasev3.4 The InterRidge Vents Database is a global database of submarine hydrothermal vent fields. The InterRidge Vents Database is supported by the InterRidge program for international cooperation in ridge-crest studies (www.interridge.org). Purpose of the database The purpose of the InterRidge Global Database of Active Submarine Hydrothermal Vent Fields, hereafter referred to as the “InterRidge Vents Database,” is to provide a comprehensive list of active submarine hydrothermal vent fields for use in academic research and education. As stated by the InterRidge Working Group (WG) on Global Distribution of Hydrothermal Activity (InterRidge News 9.1, April 2000): “The idea of this data-base is that it should become the international standard for all known sites of submarine hydrothermal activity which can be updated simply by submitting an electronic message to the InterRidge Office." Version 3.4 was completed on 25 March 2020 and and is published at PANGAEA® Data Publisher: Beaulieu, Stace E; Szafrański, Kamil M (2020) InterRidge Global Database of Active Submarine Hydrothermal Vent Fields Version 3.4. PANGAEA, https://doi.org/10.1594/PANGAEA.917894 (temporary link https://doi.pangaea.de/10.1594/PANGAEA.917894) features Global_2020_HydrothermalVents_InterRidgeVentsDatabasev3.4 hydrothermal vents vents EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 pdf url text/plain global:Global_2020_MarineSpeciesRichness_AquaMaps Raster depicting species richness values for marine species (with a probability of occurrence > 0.5) derived from AquaMaps. A total of 33,512 species were used in the generation of this file. AquaMaps are computer-generated predictions of natural occurrence of marine species, based on the environmental tolerance of a given species with respect to depth, salinity, temperature, primary productivity, and its association with sea ice or coastal areas. These 'environmental envelopes' are matched against an authority file which contains respective information for the Oceans of the World. Independent knowledge such as distribution by FAO areas or bounding boxes are used to avoid mapping species in areas that contain suitable habitat, but are not occupied by the species. Maps show the color-coded likelihood of a species to occur in a half-degree cell, with about 50 km side length near the equator. Experts are able to review, modify and approve maps. Environmental envelopes are created in part (FAO areas, bounding boxes, depth ranges) from respective information in species databases such as FishBase and in part from occurrence records available from OBIS or GBIF. AquaMaps predictions have been validated successfully for a number of species using independent data sets and the model was shown to perform equally well or better than other standard species distribution models, when faced with the currently existing suboptimal input data sets (Ready et al. 2010). The creation of AquaMaps is supported by the following projects: MARA, Pew Fellows Program in Marine Conservation, INCOFISH, Sea Around Us, and Biogeoinformatics of Hexacorals. Kaschner, K., D.P. Tittensor, J. Ready, T Gerrodette and B. Worm (2011). Current and Future Patterns of Global Marine Mammal Biodiversity. PLoS ONE 6(5): e19653. PDF Ready, J., K. Kaschner, A.B. South, P.D Eastwood, T. Rees, J. Rius, E. Agbayani, S. Kullander and R. Froese (2010). Predicting the distributions of marine organisms at the global scale. Ecological Modelling 221(3): 467-478. PDF Copyright Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. (CC-BY-NC) You are welcome to include maps from www.aquamaps.org in your own web sites for non-commercial use, given that such inserts are clearly identified as coming from AquaMaps, with a backward link to the respective source page. Contacts Rainer Froese, GEOMAR, Coordinator rfroese@geomar.de Kristin Kaschner, Uni Freiburg, model development Kristin.Kaschner@biologie.uni-freiburg.de Ma. Lourdes D. Palomares, UBC, extension to non-fish marine organisms m.palomares@fisheries.ubc.ca Sven Kullander, NRM, extension to freshwater ve-sven@nrm.se Jonathan Ready, NRM, implementation jonathan.ready@gmail.com Tony Rees, formerly with CSIRO, mapping tools Tony.Rees@marinespecies.org Paul Eastwood, SOPAC, validation Paul.Eastwood@sopac.org Andy South, CEFAS, validation andy.south@cefas.co.uk Josephine Rius-Barile, Q-quatics, database programming / data collection j.barile@q-quatics.org Cristina Garilao, GEOMAR, web programming cgarilao@geomar.de Kathleen Kesner-Reyes, Q-quatics, map validation k.reyes@q-quatics.org Elizabeth Bato, Q-quatics, map validation (non-fish) e.david@q-quatics.org Citing AquaMaps General citation Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2019. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, version 10/2019. Cite individual maps as, e.g., Computer Generated Map for Gadus morhua (Atlantic cod). www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019). Reviewed Native Distribution Map for Gadus morhua (Atlantic cod). www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019). Cite biodiversity maps as, e.g., Shark and Ray Biodiversity Map. www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019). Cite the environmental dataset as, e.g., Kesner-Reyes, K., Segschneider, J., Garilao, C., Schneider, B., Rius-Barile, J., Kaschner, K., and Froese, R.(editors). AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF). World Wide Web electronic publication, www.aquamaps.org/main/envt_main.php, ver. 7, 10/2019. Using Full or Large Sets of AquaMaps Data We encourage partnering with the AquaMaps team for larger research projects or publications that would make intensive use of AquaMaps to ensure that you have access to the latest version and/or reviewed maps, the limitations of the data set are clearly understood and addressed, and that critical maps and/or unlikely results are recognized as such and double-checked for correctness prior to drawing conclusions and/or subsequent publication. The AquaMaps team can be contacted through Rainer Froese (rfroese@geomar.de) or Kristin Kaschner (Kristin.Kaschner@biologie.uni-freiburg.de). Privacy Policy AquaMaps uses log data generate usage statistics. Like most websites, AquMaps gathers information about internet protocol (IP) addresses, browser, referring pages, operating system, date/time, clicks, and visited pages, and store it in log files. This information is used to find errors in our website, analyze trends, and determine country of origin of our users. The log files are stored indefinitely. Only the administrators of the AquaMaps server has direct access to the log files. The information is used to inform further development of AquaMaps. Usage statistics may be shared with third parties for non-commercial purposes. Disclaimer AquaMaps generates standardized computer-generated and fairly reliable large scale predictions of marine and freshwater species. Although the AquaMaps team and their collaborators have obtained data from sources believed to be reliable and have made every reasonable effort to ensure its accuracy, many maps have not yet been verified by experts and we strongly suggest you verify species occurrences with independent sources before usage. We will not be held responsible for any consequence from the use or misuse of these data and/or maps by any organization or individual. Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License (CC-BY-NC). You are welcome to include text, numbers and maps from AquaMaps in your own web sites for non-commercial use, given that such inserts are clearly identified as coming from AquaMaps, with a backward link to the respective source page. Note that although species photos and drawings draw mainly from FishBase and SeaLifeBase, they belong to the indicated persons or organizations and have their own copyright statements. Global_2020_MarineSpeciesRichness_AquaMaps WCS GeoTIFF biodiversity marine species richness Aquamaps EPSG:4326 CRS:84 -180.0 180.0 -78.5 90.0 text/plain global:Global_2020_PopulationDensity30sec_GPWv4 The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions. Global_2020_PopulationDensity30sec_GPWv4 WCS GeoTIFF Gridded World Population population EPSG:4326 CRS:84 -180.0 179.99999999999983 -90.0 89.99999999999991 xml kir:KIR_2019_DEM30mGilbertIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_DEM30mGilbertIslands_ASTERv3 WCS GeoTIFF ASTER DEM elevation Kiribati EPSG:4326 CRS:84 168.99986111111102 177.00013888888873 -3.0001388888888485 4.000138888888861 text/plain kir:KIR_2019_DEM30mLineIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_DEM30mLineIslands_ASTERv3 WCS GeoTIFF ASTER DEM elevation Kiribati EPSG:4326 CRS:84 -163.00013888888896 -149.9998611111113 -11.000138888888761 6.00013888888885 text/plain kir:KIR_2019_DEM30mPhoenixIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_DEM30mPhoenixIslands_ASTERv3 WCS GeoTIFF ASTER DEM elevation Kiribati EPSG:4326 CRS:84 -175.00013888888898 -169.99986111111124 -5.000138888888858 -1.9998611111111098 text/plain kir:KIR_2019_Hillshade30mGilbertIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_Hillshade30mGilbertIslands_ASTERv3 WCS GeoTIFF ASTER hillshade Kiribati EPSG:4326 CRS:84 168.99986111111102 177.00013888888873 -3.0001388888888485 4.000138888888871 text/plain kir:KIR_2019_Hillshade30mLineIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_Hillshade30mLineIslands_ASTERv3 WCS GeoTIFF ASTER hillshade Kiribati EPSG:4326 CRS:84 -163.00013888888896 -149.9998611111113 -11.000138888888761 6.000138888888854 text/plain kir:KIR_2019_Hillshade30mPhoenixIslands_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. KIR_2019_Hillshade30mPhoenixIslands_ASTERv3 WCS GeoTIFF ASTER hillshade Kiribati EPSG:4326 CRS:84 -175.00013888888898 -169.99986111111124 -5.000138888888858 -1.9998611111111035 text/plain kir:KIR_2020_ImageryGilbertIslandsNorth_Sentinel2 This resource contains satellite imagery for Makin, Butaitai, Marakei, Abalang, Tarawa, Maiana, Abemama, Aranuka, and Kuria islands in the Gilbert Island chain in Kiribati. The imagery was collected on June 12 and June 14, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200612T230341_N0214_R101_T59NPD_20200613T005752 S2A_MSIL2A_20200612T230341_N0214_R101_T59NQD_20200613T005752 S2B_MSIL2A_20200614T225459_N0214_R058_T59NPC_20200615T004548 S2B_MSIL2A_20200614T225459_N0214_R058_T59NQA_20200615T004548 S2B_MSIL2A_20200614T225459_N0214_R058_T59NQB_20200615T004548 S2B_MSIL2A_20200614T225459_N0214_R058_T59NQC_20200615T004548 S2B_MSIL2A_20200614T225459_N0214_R058_T59NRA_20200615T004548 KIR_2020_ImageryGilbertIslandsNorth_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Kiribati EPSG:32655 CRS:84 172.50858950694231 174.14656679062807 -0.0343070538663773 3.4045294837207085 text/plain kir:KIR_2020_ImageryGilbertIslandsSouth_Sentinel2 This resource contains satellite imagery for Nonouti, Beru, Nikunau, Tabiteuea, Onotoa, Tamana, and Arorae islands in the Gilbert Island chain in Kiribati. The imagery was collected on June 1, June 3, June 6, and August 15, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20200601T224609_N0214_R015_T60MTD_20200602T002531 S2A_MSIL2A_20200603T223641_N0214_R115_T60MVC_20200604T023722 S2A_MSIL2A_20200606T224621_N0214_R015_T60MUD_20200607T001506 S2B_MSIL2A_20200813T225459_N0214_R058_T59MRV_20200814T003201 S2A_MSIL2A_20200815T224621_N0214_R015_T60MVC_20200816T001830 S2A_MSIL2A_20200815T224621_N0214_R015_T60MUC_20200816T001830 S2A_MSIL2A_20200815T224621_N0214_R015_T60MVD_20200816T001830 KIR_2020_ImageryGilbertIslandsSouth_Sentinel2 WCS GeoTIFF Sentinel-2 earth observation satellite imagery Kiribati EPSG:32655 CRS:84 173.91318821008792 177.12712622299082 -2.76517875210534 -0.4053392434501375 text/plain mhl:MHL_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. MHL_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Marshall Islands EPSG:4326 CRS:84 159.9998611111111 174.00013888888876 2.999861111111123 15.000138888888785 text/plain mhl:MHL_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. MHL_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF hillshade ASTER Marshall Islands EPSG:4326 CRS:84 159.9998611111111 174.00013888888876 2.999861111111123 15.000138888888795 text/plain mhl:MHL_2019_ImageryErikubWatje_Sentinel2 This resource contains satellite imagery for Erikub Atoll and Wotje Atoll in the Marshall Islands. The imagery was collected on December 3, 2019. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20191203T231229_N0208_R001_T59PLL_20191204T003038 S2B_MSIL1C_20191203T231229_N0208_R001_T59PML_20191204T003038 MHL_2019_ImageryErikubWatje_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Marshall Islands EPSG:32659 CRS:84 169.17569760978222 171.08904151036387 8.953811758453359 9.951396178341643 text/plain mhl:MHL_2019_ImageryMiliKnox_Sentinel2 This resource contains satellite imagery for Mili Atoll and Knox Atoll in the Marshall Islands. The imagery was collected on August 12, 2019. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20190812T230339_N0208_R101_T59NNG_20190813T001838 S2B_MSIL1C_20190812T230339_N0208_R101_T59NPG_20190813T001838 MHL_2019_ImageryMiliKnox_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 170.9998191688487 172.89656584424424 5.336833146032486 6.33304216613588 text/plain mhl:MHL_2019_ImageryNamorik_Sentinel2 This resource contains satellite imagery for Namorik Atoll in the Marshall Islands. The imagery was collected on December 21, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20191221T232201_N0208_R044_T58NHM_20191222T004652 MHL_2019_ImageryNamorik_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32658 CRS:84 167.7064935029994 168.70242171938878 5.328635058317998 6.325963636514705 text/plain mhl:MHL_2020_ImageryAilinginaeRongelapRongerik_Sentinel2 This resource contains satellite imagery for Ailinginae Atoll, Rongerik Atoll, and Rongelap Atoll in the Marshall Islands. The imagery was collected on May 22, June 26, and July 8, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200522T233301_N0209_R087_T58PGT_20200523T004504 S2B_MSIL1C_20200626T233259_N0209_R087_T58PFT_20200627T005656 S2A_MSIL1C_20200708T232211_N0209_R044_T58PGT_20200709T005058 MHL_2020_ImageryAilinginaeRongelapRongerik_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Marshall Islands EPSG:32658 CRS:84 165.91463220910538 167.84180206915926 10.754102422534078 11.758567886482854 text/plain mhl:MHL_2020_ImageryAilukJemo_Sentinel2 This resource contains satellite imagery for Ailuk Atoll and Jemo Island in the Marshall Islands. The imagery was collected on February 6, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200206T231221_N0209_R001_T59PLM_20200207T003700 MHL_2020_ImageryAilukJemo_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Marshall Islands EPSG:32659 CRS:84 169.17044276734228 170.17734657695306 9.857478810046604 10.85437483653782 text/plain mhl:MHL_2020_ImageryBikar_Sentinel2 This resource contains satellite imagery for Bikar Atoll in the Marshall Islands. The imagery was collected on April 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200411T231229_N0209_R001_T59PLP_20200412T003202 MHL_2020_ImageryBikar_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Marshall Islands EPSG:32659 CRS:84 169.1584619653071 170.17242432711944 11.665740831980457 12.66325335258812 text/plain mhl:MHL_2020_ImageryBikini_Sentinel2 This resource contains satellite imagery for Bikini Island. The imagery was collected on January 28, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200128T233249_N0208_R087_T58PET_20200130T192210 MHL_2020_ImageryBikini_Sentinel2 WCS GeoTIFF RGB Sentinel-2 satellite imagery earth observation Marshall Islands EPSG:32658 CRS:84 164.99981643701017 166.00752284452162 10.76540463017321 11.760044027808236 text/plain mhl:MHL_2020_ImageryBokakTaongi_Sentinel2 This resource contains satellite imagery for Bokak/Taongi Atoll in the Marshall Islands. The imagery was collected on May 19, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200519T232211_N0209_R044_T59PKS_20200520T005619 MHL_2020_ImageryBokakTaongi_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Marshall Islands EPSG:32659 CRS:84 168.20554290876075 169.23587862690198 14.368052294336673 15.370088377261439 text/plain mhl:MHL_2020_ImageryEbon_Sentinel2 This resource contains satellite imagery for Ebon Atoll in the Marshall Islands. The imagery was collected on February 16, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200216T231231_N0209_R001_T59NKF_20200217T003721 MHL_2020_ImageryEbon_Sentinel2 WCS GeoTIFF Marshall Islands earth observation imagery satellite Sentinel-2 EPSG:32659 CRS:84 168.2927551124814 169.28570048658304 4.429955960885932 5.425787018836145 text/plain mhl:MHL_2020_ImageryEnewetok_Sentinel2 This resource contains satellite imagery for Enewetak Atoll in the Marshall Islands. The imagery was collected on September 28, 2019 and June 27, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20190928T234301_N0208_R130_T57PZN_20191004T123848 S2A_MSIL1C_20200627T235311_N0209_R030_T57PZN_20200628T012024 MHL_2020_ImageryEnewetok_Sentinel2 WCS GeoTIFF imagery earth observation satellite Sentinel-2 Marshall Islands EPSG:32657 CRS:84 161.74274376857926 162.75819556175637 10.744423290479943 11.746771696052052 text/plain mhl:MHL_2020_ImageryJaluitKili_Sentinel2 This resource contains satellite imagery for Jaluit Atoll and Kili Island in the Marshall Islands. The imagery was collected on November 11, 2019 and June 15, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20191108T231231_N0208_R001_T59NKG_20191109T003629 S2A_MSIL1C_20200615T231241_N0209_R001_T59NLG_20200616T004800 MHL_2020_ImageryJaluitKili_Sentinel2 WCS GeoTIFF Sentinel-2 earth observation satellite imagery Marshall Islands EPSG:32659 CRS:84 168.28838370505312 170.18591373088213 5.3337873775074085 6.332401637137316 text/plain mhl:MHL_2020_ImageryKwajaleinLib_Sentinel2 This resource contains satellite imagery for Kwajalein Atoll and Lib Island in the Marshall Islands. The imagery was collected on January 15 and June 28, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200115T232159_N0208_R044_T58PGQ_20200116T004824 S2A_MSIL1C_20200628T232211_N0209_R044_T58PFR_20200629T004925 S2A_MSIL1C_20200628T232211_N0209_R044_T58PGR_20200629T004925 MHL_2020_ImageryKwajaleinLib_Sentinel2 WCS GeoTIFF Sentinel-2 earth observation satellite imagery Marshall Islands EPSG:32658 CRS:84 165.907527632251 167.8247878523012 8.043948211356259 9.950163774005182 text/plain mhl:MHL_2020_ImageryLae_Sentinel2 This resource contains satellite imagery for Lae Atoll in the Marshall Islands. The imagery was collected on March 18, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200318T233259_N0209_R087_T58PFQ_20200319T005508 MHL_2020_ImageryLae_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32658 CRS:84 165.907527632251 166.9086468529447 8.049153270568084 9.04561414016698 text/plain mhl:MHL_2020_ImageryLikiep_Sentinel2 This resource contains satellite imagery for Likiep Atoll in the Marshall Islands. The imagery was collected on December 3, 2019, February 6, 2020, May 14, 2020, and June 13, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20191203T231229_N0208_R001_T59PLL_20191204T003038 S2A_MSIL1C_20200206T231221_N0209_R001_T59PLM_20200207T003700 S2B_MSIL1C_20200514T232209_N0209_R044_T59PKL_20200515T004614 S2B_MSIL1C_20200613T232209_N0209_R044_T59PKM_20200614T020555 MHL_2020_ImageryLikiep_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 168.25608845340207 170.1794810112384 8.948227359323448 10.85437483653782 text/plain mhl:MHL_2020_ImageryMajuroArno_Sentinel2 This resource contains satellite imagery for Majuro Atoll and Arno Atoll in the Marshall Islands. The imagery was collected on January 28, May 18, June 25, and July 17, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200128T233249_N0208_R087_T58PET_20200130T192210 S2B_MSIL1C_20200518T230339_N0209_R101_T59NNH_20200519T002105 S2A_MSIL1C_20200625T231241_N0209_R001_T59NMH_20200626T004801 S2A_MSIL1C_20200625T231241_N0209_R001_T59NNH_20200626T004801 S2B_MSIL1C_20200717T230339_N0209_R101_T59NNJ_20200718T001945 MHL_2020_ImageryMajuroArno_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 170.73962108817452 172.02233703319243 6.809064541943613 7.614768613516375 text/plain mhl:MHL_2020_ImageryMalolelapAurImagery_Sentinel2 This resource contains satellite imagery for Maloelap Atoll and Aur Atoll in the Marshall Islands. The imagery was collected on June 25, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200625T231241_N0209_R001_T59PMK_20200626T004801 S2A_MSIL1C_20200625T231241_N0209_R001_T59PNK_20200626T004801 MHL_2020_ImageryMalolelapAurImagery_Sentinel2 WCS GeoTIFF Marshall Islands satellite imagery earth observation Sentinel-2 EPSG:32659 CRS:84 170.08975609329622 171.9988573673643 8.05235960109292 9.046740686671825 text/plain mhl:MHL_2020_ImageryMeijit_Sentinel2 This resource contains satellite imagery for Mejit Island in the Marshall Islands. The imagery was collected on February 6, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200206T231221_N0209_R001_T59PMM_20200207T003700 MHL_2020_ImageryMeijit_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 170.08473397417777 171.08929816523272 9.861175024224776 10.855468266855908 text/plain mhl:MHL_2020_ImageryNamuAilinglapalap_Sentinel2 This resource contains satellite imagery for Namu Atoll, Jabat Island, and Ailinglaplap Atoll in the Marshall Islands. The imagery was collected on July 3 and 25, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200703T232209_N0209_R044_T59NKJ_20200704T004810 S2B_MSIL1C_20200703T232209_N0209_R044_T58NHP_20200704T004810 S2B_MSIL1C_20200703T232209_N0209_R044_T58PHQ_20200704T004810 S2A_MSIL1C_20200725T231241_N0209_R001_T59NKJ_20200726T004604 MHL_2020_ImageryNamuAilinglapalap_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 167.70038121281524 169.277536962805 7.127907512828487 9.047088580113945 text/plain mhl:MHL_2020_ImageryUjae_Sentinel2 This resource contains satellite imagery for Ujae Atoll in the Marshall Islands. The imagery was collected on February 2 and June 26, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200202T233251_N0208_R087_T58PER_20200203T005726 S2B_MSIL1C_20200626T233259_N0209_R087_T58PEQ_20200627T005656 MHL_2020_ImageryUjae_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32658 CRS:84 164.99981753781248 166.00148210114278 8.05235960109292 9.951408032022043 text/plain mhl:MHL_2020_ImageryUjelang_Sentinel2 This resource contains satellite imagery for Ujelang Atoll in the Marshall Islands. The imagery was collected on March 19, April 8, and July 27, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200319T235301_N0209_R030_T57PYL_20200320T012003 S2A_MSIL1C_20200408T235301_N0209_R030_T57PYL_20200409T031320 S2A_MSIL1C_20200727T235311_N0209_R030_T57PXL_20200728T012445 MHL_2020_ImageryUjelang_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32657 CRS:84 159.90965894295894 161.82478785230117 8.947564560808189 9.950163774005182 text/plain mhl:MHL_2020_ImageryUterikTaka_Sentinel2 This resource contains satellite imagery for the Utirik Atoll and Taka Atoll in Marshall Islands. The imagery was collected on May 31, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200531T231239_N0209_R001_T59PLN_20200601T003233_RGB.tif MHL_2020_ImageryUterikTaka_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32659 CRS:84 169.16469901832585 170.1749949473096 10.761637470721952 11.758843009779822 text/plain mhl:MHL_2020_ImageryWotho_Sentinel2 This resource contains satellite imagery for Wotho Atoll in Marshall Islands. The imagery was collected on February 2, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200202T233251_N0208_R087_T58PFS_20200203T005726 MHL_2020_ImageryWotho_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Marshall Islands EPSG:32658 CRS:84 165.91202518458516 166.91917142631954 9.856984048947911 10.85412139120383 text/plain niu:NIU_2018_Imagery_Sentinel2 This resource contains satellite imagery for Niue. The imagery was collected on August 10, 2018. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20180810T213919_N0206_R143_T02KPD_20180810T224659 S2B_MSIL1C_20180810T213919_N0206_R143_T02KPE_20180810T224659 NIU_2018_Imagery_Sentinel2 WCS GeoTIFF Sentinel-2 imagery satellite earth observation Niue EPSG:32702 CRS:84 -170.07633215936934 -169.63300112485547 -19.208584105700215 -18.893480795490486 text/plain niu:NIU_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. NIU_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Niue EPSG:4326 CRS:84 -170.2976891177109 -169.63296689548866 -19.203724554258542 -18.892613443147436 text/plain niu:NIU_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. NIU_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF hillshade ASTER Niue EPSG:4326 CRS:84 -170.2976891177109 -169.63296689548866 -19.203724554258542 -18.89261344314743 text/plain nru:NRU_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. NRU_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF DEM elevation ASTER Nauru EPSG:4326 CRS:84 166.82712639366704 167.05684861588927 -0.6086172905911807 -0.4422284017022935 text/plain nru:NRU_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. NRU_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF hillshade ASTER Nauru EPSG:4326 CRS:84 166.82712639366704 167.05684861588927 -0.6086172905911807 -0.4422284017022922 text/plain nru:NRU_2020_ImageryNauru_Sentinel2 This resource contains satellite imagery for Nauru. The imagery was collected on May 9, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200509T232631_N0214_R044_T58MGE_20200510T011147 NRU_2020_ImageryNauru_Sentinel2 WCS GeoTIFF earth observation Sentinel-2 satellite imagery Nauru EPSG:32758 CRS:84 166.78409470389983 167.08884050771644 -0.6577668636064642 -0.4118161401534539 text/plain plw:PLW_2005_DEM10m_USGS A 10-meter resolution land surface digital elevation model (DEM) and for the islands of Palau from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. PLW_2005_DEM10m_USGS WCS GeoTIFF DEM elevation USGS Palau EPSG:4326 CRS:84 134.09684827229006 134.64972131020784 6.874718731620753 7.754260012341108 text/plain text/plain plw:PLW_2005_Hillshade10m_USGS A 10-meter hillshade for the islands of Palau from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. PLW_2005_Hillshade10m_USGS WCS GeoTIFF hillshade DEM elevation Palau EPSG:4326 CRS:84 134.09684827229006 134.64972131020784 6.874718731620753 7.754260012341062 text/plain text/plain plw:PLW_2020_Imagery_Sentinel2 This resource contains satellite imagery for Palau. The imagery was collected on April 24, 2020. SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20200424T013449_N0214_R031_T53NLH_20200424T032337 S2B_MSIL2A_20200424T013449_N0214_R031_T53NMH_20200424T032337 S2B_MSIL2A_20200424T013449_N0214_R031_T53NMJ_20200424T032337 PLW_2020_Imagery_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Palau EPSG:32653 CRS:84 134.08386309000764 135.0156567374725 6.840166576300976 8.147784186407714 text/plain png:PNG_2018-2020_Imagery_Landsat8 This resource contains 30-meter resolution satellite imagery for Papua New Guinea. The imagery was collected during the years 2018-2020 from the Landsat-8 satellite. Images were selected for their visibility, however there is cloud cover present in the final images due to the size of Papua New Guinea and availability of datasets. This imagery should be used for guidance only and cited as "Landsat-8 image courtesy of the U.S. Geological Survey" if used in any presentations or other work. The image was mosaicked from the following individual Landsat-8 scenes: LC08_L1GT_090068_20191231_20200101_01_RT_B4 LC08_L1GT_091067_20200224_20200225_01_RT_B4 LC08_L1GT_093065_20200223_20200223_01_RT_B4 LC08_L1GT_096063_20200822_20200822_01_RT_B4 LC08_L1TP_090064_20190302_20190309_01_T1_B4 LC08_L1TP_090065_20200624_20200707_01_T1_B4 LC08_L1TP_091063_20181203_20181204_01_RT_B4 LC08_L1TP_091064_20200802_20200807_01_T1_B4 LC08_L1TP_091065_20180829_20180911_01_T1_B4 LC08_L1TP_091066_20200428_20200509_01_T1_B4 LC08_L1TP_091068_20181203_20181211_01_T1_B4 LC08_L1TP_092062_20200910_20200911_01_RT_B4 LC08_L1TP_092063_20190228_20190309_01_T1_B4 LC08_L1TP_092066_20191127_20191203_01_T1_B4 LC08_L1TP_092067_20200302_20200314_01_T1_B4 LC08_L1TP_092068_20201012_20201013_01_RT_B4 LC08_L1TP_093062_20180928_20181009_01_T1_B4 LC08_L1TP_093063_20180929_20181009_01_T1_B4 LC08_L1TP_093064_20180217_20180307_01_T1_B4 LC08_L1TP_093066_20200223_20200225_01_T1_B4 LC08_L1TP_093067_20190511_20190521_01_T1_B4 LC08_L1TP_093068_20200223_20200225_01_T1_B4 LC08_L1TP_094062_20200605_20200608_01_T1_B4 LC08_L1TP_094063_20191126_20191203_01_T1_B4 LC08_L1TP_094064_20200418_20200423_01_T1_B4 LC08_L1TP_094065_20200418_20200423_01_T1_B4 LC08_L1TP_094066_20200418_20200423_01_T1_B4 LC08_L1TP_094067_20181006_20181010_01_T1_B4 LC08_L1TP_095061_20200409_20200409_01_RT_B4 LC08_L1TP_095062_20191203_20191216_01_T1_B4 LC08_L1TP_095063_20190914_20190914_01_RT_B4 LC08_L1TP_095064_20191203_20191216_01_T1_B4 LC08_L1TP_095065_20191203_20191216_01_T1_B4 LC08_L1TP_095066_20190218_20190222_01_T1_B4 LC08_L1TP_095067_20180810_20180815_01_T1_B4 LC08_L1TP_096061_20191210_20191210_01_RT_B4 LC08_L1TP_096062_20190209_20190221_01_T1_B4 LC08_L1TP_096064_20191124_20191203_01_T1_B4 LC08_L1TP_096065_20191124_20191203_01_T1_B4 LC08_L1TP_096066_20191124_20191203_01_T1_B4 LC08_L1TP_096067_20191124_20191203_01_T1_B4 LC08_L1TP_097061_20180621_20180703_01_T1_B4 LC08_L1TP_097062_20180621_20180703_01_T1_B4 LC08_L1TP_097063_20190912_20190917_01_T1_B4 LC08_L1TP_097064_20180723_20180731_01_T1_B4 LC08_L1TP_097065_20181214_20181227_01_T1_B4 LC08_L1TP_097066_20181214_20181227_01_T1_B4 LC08_L1TP_098061_20200125_20200125_01_RT_B4 LC08_L1TP_098062_20190327_20190404_01_T1_B4 LC08_L1TP_098063_20200703_20200708_01_T1_B4 LC08_L1TP_098064_20191208_20191217_01_T1_B4 LC08_L1TP_098065_20180204_20180220_01_T1_B4 LC08_L1TP_098066_20180204_20180220_01_T1_B4 LC08_L1TP_098067_20200820_20200904_01_T1_B4 LC08_L1TP_099061_20200608_20200625_01_T1_B4 LC08_L1TP_099062_20200710_20200721_01_T1_B4 LC08_L1TP_099063_20200710_20200721_01_T1_B4 LC08_L1TP_099064_20200304_20200314_01_T1_B4 LC08_L1TP_099065_20200304_20200314_01_T1_B4 LC08_L1TP_099066_20200405_20200410_01_T1_B4 LC08_L1TP_099067_20200912_20200919_01_T1_B4 LC08_L1TP_100061_20200818_20200823_01_T1_B4 LC08_L1TP_100062_20191019_20191029_01_T1_B4 LC08_L1TP_100063_20191019_20191029_01_T1_B4 LC08_L1TP_100064_20200123_20200128_01_T1_B4 LC08_L1TP_100065_20200818_20200823_01_T1_B4 LC08_L1TP_100066_20180813_20180828_01_T1_B4 PNG_2018-2020_Imagery_Landsat8 WCS GeoTIFF earth observation satellite imagery Landsat8 Papua New Guinea EPSG:32656 CRS:84 140.60039980044704 156.877472616221 -12.118261687847301 -0.8656325134122322 text/plain png:PNG_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. PNG_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Papua New Guinea EPSG:4326 CRS:84 139.99986111111102 157.00013888888876 -12.000138888888836 1.388888888889E-4 text/plain png:PNG_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. PNG_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF ASTER hillshade Papua New Guinea EPSG:4326 CRS:84 139.99986111111102 157.00013888888876 -12.000138888888836 1.38888888916E-4 text/plain pacific:Pacific_2000-2004_EarthquakeMag4.5_USGS Earthquake centers over a magnitude of 4.5 for the Pacific Islands Region for years 2000-2004. Data are derived from the ANSS Comprehensive Earthquake Catalog (ComCat). ComCat contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks. Fields are defined below: alert Data Type: String Typical Values: “green”, “yellow”, “orange”, “red”. Description: The alert level from the PAGER earthquake impact scale. cdi Data Type: Decimal Typical Values: [0.0, 10.0] Description: The maximum reported intensity for the event. Computed by DYFI. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. code Data Type: String Typical Values: "2013lgaz", "c000f1jy", "71935551" Description: An identifying code assigned by - and unique from - the corresponding source for the event. Depth Data Type: Decimal Typical Values: [0, 1000] Description: Depth of the event in kilometers. Additional Information: The depth where the earthquake begins to rupture. This depth may be relative to the WGS84 geoid, mean sea-level, or the average elevation of the seismic stations which provided arrival-time data for the earthquake location. The choice of reference depth is dependent on the method used to locate the earthquake, which varies by seismic network. Since ComCat includes data from many different seismic networks, the process for determining the depth is different for different events. The depth is the least-constrained parameter in the earthquake location, and the error bars are generally larger than the variation due to different depth determination methods. Sometimes when depth is poorly constrained by available seismic data, the location program will set the depth at a fixed value. For example, 33 km is often used as a default depth for earthquakes determined to be shallow, but whose depth is not satisfactorily determined by the data, whereas default depths of 5 or 10 km are often used in mid-continental areas and on mid-ocean ridges since earthquakes in these areas are usually shallower than 33 km. depthError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported depth of the event in kilometers. Additional Information: The depth error, in km, defined as the largest projection of the three principal errors on a vertical line. detail Data Type: String Description: Link to GeoJSON detail feed from a GeoJSON summary feed. NOTE: When searching and using geojson with callback, no callback is included in the detail url. dmin Data Type: Decimal Typical Values: [0.4, 7.1] Description: Horizontal distance from the epicenter to the nearest station (in degrees). 1 degree is approximately 111.2 kilometers. In general, the smaller this number, the more reliable is the calculated depth of the earthquake. felt Data Type: Integer Typical Values: [44, 843] Description: The total number of felt reports submitted to the DYFI? system. gap Data Type: Decimal Typical Values: [0.0, 180.0] Description: The largest azimuthal gap between azimuthally adjacent stations (in degrees). In general, the smaller this number, the more reliable is the calculated horizontal position of the earthquake. Earthquake locations in which the azimuthal gap exceeds 180 degrees typically have large location and depth uncertainties. horizontalError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported location of the event in kilometers. Additional Information: The horizontal location error, in km, defined as the length of the largest projection of the three principal errors on a horizontal plane. The principal errors are the major axes of the error ellipsoid, and are mutually perpendicular. The horizontal and vertical uncertainties in an event's location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. id Data Type: String Typical Values: A (generally) two-character network identifier with a (generally) eight-character network-assigned code. Description: A unique identifier for the event. This is the current preferred id for the event, and may change over time. See the "ids" GeoJSON format property. ids Data Type: String Typical Values: ",ci15296281,us2013mqbd,at00mji9pf," Description: A comma-separated list of event ids that are associated to an event. latitude Data Type: Decimal Typical Values: [-90.0, 90.0] Description: Decimal degrees latitude. Negative values for southern latitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. locationSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The network that originally authored the reported location of this event. longitude Data Type: Decimal Typical Values: [-180.0, 180.0] Description: Decimal degrees longitude. Negative values for western longitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. mag Data Type: Decimal Typical Values: [-1.0, 10.0] Description: The magnitude for the event. See also magType. Additional Information: The magnitude reported is that which the U.S. Geological Survey considers official for this earthquake, and was the best available estimate of the earthquake’s size, at the time that this page was created. Other magnitudes associated with web pages linked from here are those determined at various times following the earthquake with different types of seismic data. Although they are legitimate estimates of magnitude, the U.S. Geological Survey does not consider them to be the preferred "official" magnitude for the event. Earthquake magnitude is a measure of the size of an earthquake at its source. It is a logarithmic measure. At the same distance from the earthquake, the amplitude of the seismic waves from which the magnitude is determined are approximately 10 times as large during a magnitude 5 earthquake as during a magnitude 4 earthquake. The total amount of energy released by the earthquake usually goes up by a larger factor: for many commonly used magnitude types, the total energy of an average earthquake goes up by a factor of approximately 32 for each unit increase in magnitude. There are various ways that magnitude may be calculated from seismograms. Different methods are effective for different sizes of earthquakes and different distances between the earthquake source and the recording station. The various magnitude types are generally defined so as to yield magnitude values that agree to within a few-tenths of a magnitude-unit for earthquakes in a middle range of recorded-earthquake sizes, but the various magnitude-types may have values that differ by more than a magnitude-unit for very large and very small earthquakes as well as for some specific classes of seismic source. This is because earthquakes are commonly complex events that release energy over a wide range of frequencies and at varying amounts as the faulting or rupture process occurs. The various types of magnitude measure different aspects of the seismic radiation (e.g., low-frequency energy vs. high-frequency energy). The relationship among values of different magnitude types that are assigned to a particular seismic event may enable the seismologist to better understand the processes at the focus of the seismic event. The various magnitude-types are not all available at the same time for a particular earthquake. Preliminary magnitudes based on incomplete but rapidly-available data are sometimes estimated and reported. For example, the Tsunami Warning Centers will calculate a preliminary magnitude and location for an event as soon as sufficient data are available to make an estimate. In this case, time is of the essence in order to broadcast a warning if tsunami waves are likely to be generated by the event. Such preliminary magnitudes are superseded by improved estimates of magnitude as more data become available. For large earthquakes of the present era, the magnitude that is ultimately selected as the preferred magnitude for reporting to the public is commonly a moment magnitude that is based on the scalar seismic-moment of an earthquake determined by calculation of the seismic moment-tensor that best accounts for the character of the seismic waves generated by the earthquake. The scalar seismic-moment, a parameter of the seismic moment-tensor, can also be estimated via the multiplicative product rigidity of faulted rock x area of fault rupture x average fault displacement during the earthquake. magError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported magnitude of the event. The estimated standard error of the magnitude. The uncertainty corresponds to the specific magnitude type being reported and does not take into account magnitude variations and biases between different magnitude scales. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. magNst Data Type: Integer Description: The total number of seismic stations used to calculate the magnitude for this earthquake. magSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: Network that originally authored the reported magnitude for this event. magType Data Type: String Typical Values: “Md”, “Ml”, “Ms”, “Mw”, “Me”, “Mi”, “Mb”, “MLg” Description: The method or algorithm used to calculate the preferred magnitude for the event. Additional Information: See Magnitude Types Table. mmi Data Type: Decimal Typical Values:[0.0, 10.0] Description: The maximum estimated instrumental intensity for the event. Computed by ShakeMap. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. net Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event. nph Data Type: String Description: Number of Phases Used. Number of P and S arrival-time observations used to compute the hypocenter location. Increased numbers of arrival-time observations generally result in improved earthquake locations. nst Data Type: Integer Description: The total number of seismic stations used to determine earthquake location. Additional Information: Number of seismic stations which reported P- and S-arrival times for this earthquake. This number may be larger than the Number of Phases Used if arrival times are rejected because the distance to a seismic station exceeds the maximum allowable distance or because the arrival-time observation is inconsistent with the solution. place Data Type: String Description: Textual description of named geographic region near to the event. This may be a city name, or a Flinn-Engdahl Region name. Additional Information: We use a GeoNames dataset to reference populated places that are in close proximity to a seismic event. GeoNames has compiled a list of cities in the United States where the population is 1,000 or greater (cities1000.txt). This is the primary list that we use when selecting nearby places. In order to provide the public with a better understanding for the location of an event we try to list a variety of places in our nearby places list. This includes the closest known populated place in relation to the seismic event (which based on our dataset will have a population of 1,000 or greater). We also include the next 3 closest places that have a population of 10,000 or greater, and finally make sure to include the closest capital city to the seismic event. The reference point for the descriptive locations is usually either the City Hall of the town (or prominent intersection in the middle of town if there is no City Hall), but please refer to the GeoNames website for the most accurate information on their data. If there is no nearby city within 300 kilometers (or if the nearby cities database is unavailable for some reason), the Flinn-Engdahl (F-E) seismic and geographical regionalization scheme is used. The boundaries of these regions are defined at one-degree intervals and therefore differ from irregular political boundaries. For example, F-E region 545 (Northern Italy) also includes small parts of France, Switzerland, Austria and Slovenia and F-E region 493 (Chesapeake Bay Region) includes all of the State of Delaware, plus parts of the District of Columbia, Maryland, New Jersey, Pennsylvania and Virginia. Beginning with January 2000, the 1995 revision to the F-E code has been used in the QED and PDE listings. As an agency of the U.S. Government, we are expected to use the names and spellings approved by the U.S. Board on Geographic Names. Any requests to approve additional names should be made to the U.S. Board on Geographic Names. rms Data Type: Decimal Typical Values: [0.13,1.39] Description: The root-mean-square (RMS) travel time residual, in sec, using all weights. This parameter provides a measure of the fit of the observed arrival times to the predicted arrival times for this location. Smaller numbers reflect a better fit of the data. The value is dependent on the accuracy of the velocity model used to compute the earthquake location, the quality weights assigned to the arrival time data, and the procedure used to locate the earthquake. sig Data Type: Integer Typical Values: [0, 1000] Description: A number describing how significant the event is. Larger numbers indicate a more significant event. This value is determined on a number of factors, including: magnitude, maximum MMI, felt reports, and estimated impact. sources Data Type: String Typical Values: ",us,nc,ci," Description: A comma-separated list of network contributors. status Data Type: String Typical Values: “automatic”, “reviewed”, “deleted” Description: Indicates whether the event has been reviewed by a human. Additional Information Status is either automatic or reviewed. Automatic events are directly posted by automatic processing systems and have not been verified or altered by a human. Reviewed events have been looked at by a human. The level of review can range from a quick validity check to a careful reanalysis of the event. time Data Type: Long Integer Description: Time when the event occurred. Times are reported in milliseconds since the epoch ( 1970-01-01T00:00:00.000Z), and do not include leap seconds. In certain output formats, the date is formatted for readability. Additional Information: We indicate the date and time when the earthquake initiates rupture, which is known as the "origin" time. Note that large earthquakes can continue rupturing for many 10's of seconds. We provide time in UTC (Coordinated Universal Time). Seismologists use UTC to avoid confusion caused by local time zones and daylight savings time. On the individual event pages, times are also provided for the time at the epicenter, and your local time based on the time your computer is set. tsunami Data Type: Integer Description: This flag is set to "1" for large events in oceanic regions and "0" otherwise. The existence or value of this flag does not indicate if a tsunami actually did or will exist. If the flag value is "1", the event will include a link to the NOAA Tsunami website for tsunami information. The USGS is not responsible for Tsunami warning; we are simply providing a link to the authoritative NOAA source. See http://www.tsunami.gov/ for all current tsunami alert statuses. type Data Type: String Typical Values: “earthquake”, “quarry” Description: Type of seismic event. types Data Type: String Typical Values: “,cap,dyfi,general-link,origin,p-wave-travel-times,phase-data,” Description: A comma-separated list of product types associated to this event. tz Data Type: Integer Typical Values: [-1200, +1200] Description: Timezone offset from UTC in minutes at the event epicenter. updated Data Type: Long Integer Description: Time when the event was most recently updated. Times are reported in milliseconds since the epoch. In certain output formats, the date is formatted for readability. url Data Type: String Description: Link to USGS Event Page for event. features Pacific_2000-2004_EarthquakeMag4.5_USGS USGS earthquake hazard EPSG:4326 CRS:84 -179.999 179.994 -35.149 28.465 text/plain pacific:Pacific_2005-2009_EarthquakeMag4.5_USGS Earthquake centers over a magnitude of 4.5 for the Pacific Islands Region for years 2005-2009. Data are derived from the ANSS Comprehensive Earthquake Catalog (ComCat). ComCat contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks. Fields are defined below: alert Data Type: String Typical Values: “green”, “yellow”, “orange”, “red”. Description: The alert level from the PAGER earthquake impact scale. cdi Data Type: Decimal Typical Values: [0.0, 10.0] Description: The maximum reported intensity for the event. Computed by DYFI. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. code Data Type: String Typical Values: "2013lgaz", "c000f1jy", "71935551" Description: An identifying code assigned by - and unique from - the corresponding source for the event. Depth Data Type: Decimal Typical Values: [0, 1000] Description: Depth of the event in kilometers. Additional Information: The depth where the earthquake begins to rupture. This depth may be relative to the WGS84 geoid, mean sea-level, or the average elevation of the seismic stations which provided arrival-time data for the earthquake location. The choice of reference depth is dependent on the method used to locate the earthquake, which varies by seismic network. Since ComCat includes data from many different seismic networks, the process for determining the depth is different for different events. The depth is the least-constrained parameter in the earthquake location, and the error bars are generally larger than the variation due to different depth determination methods. Sometimes when depth is poorly constrained by available seismic data, the location program will set the depth at a fixed value. For example, 33 km is often used as a default depth for earthquakes determined to be shallow, but whose depth is not satisfactorily determined by the data, whereas default depths of 5 or 10 km are often used in mid-continental areas and on mid-ocean ridges since earthquakes in these areas are usually shallower than 33 km. depthError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported depth of the event in kilometers. Additional Information: The depth error, in km, defined as the largest projection of the three principal errors on a vertical line. detail Data Type: String Description: Link to GeoJSON detail feed from a GeoJSON summary feed. NOTE: When searching and using geojson with callback, no callback is included in the detail url. dmin Data Type: Decimal Typical Values: [0.4, 7.1] Description: Horizontal distance from the epicenter to the nearest station (in degrees). 1 degree is approximately 111.2 kilometers. In general, the smaller this number, the more reliable is the calculated depth of the earthquake. felt Data Type: Integer Typical Values: [44, 843] Description: The total number of felt reports submitted to the DYFI? system. gap Data Type: Decimal Typical Values: [0.0, 180.0] Description: The largest azimuthal gap between azimuthally adjacent stations (in degrees). In general, the smaller this number, the more reliable is the calculated horizontal position of the earthquake. Earthquake locations in which the azimuthal gap exceeds 180 degrees typically have large location and depth uncertainties. horizontalError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported location of the event in kilometers. Additional Information: The horizontal location error, in km, defined as the length of the largest projection of the three principal errors on a horizontal plane. The principal errors are the major axes of the error ellipsoid, and are mutually perpendicular. The horizontal and vertical uncertainties in an event's location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. id Data Type: String Typical Values: A (generally) two-character network identifier with a (generally) eight-character network-assigned code. Description: A unique identifier for the event. This is the current preferred id for the event, and may change over time. See the "ids" GeoJSON format property. ids Data Type: String Typical Values: ",ci15296281,us2013mqbd,at00mji9pf," Description: A comma-separated list of event ids that are associated to an event. latitude Data Type: Decimal Typical Values: [-90.0, 90.0] Description: Decimal degrees latitude. Negative values for southern latitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. locationSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The network that originally authored the reported location of this event. longitude Data Type: Decimal Typical Values: [-180.0, 180.0] Description: Decimal degrees longitude. Negative values for western longitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. mag Data Type: Decimal Typical Values: [-1.0, 10.0] Description: The magnitude for the event. See also magType. Additional Information: The magnitude reported is that which the U.S. Geological Survey considers official for this earthquake, and was the best available estimate of the earthquake’s size, at the time that this page was created. Other magnitudes associated with web pages linked from here are those determined at various times following the earthquake with different types of seismic data. Although they are legitimate estimates of magnitude, the U.S. Geological Survey does not consider them to be the preferred "official" magnitude for the event. Earthquake magnitude is a measure of the size of an earthquake at its source. It is a logarithmic measure. At the same distance from the earthquake, the amplitude of the seismic waves from which the magnitude is determined are approximately 10 times as large during a magnitude 5 earthquake as during a magnitude 4 earthquake. The total amount of energy released by the earthquake usually goes up by a larger factor: for many commonly used magnitude types, the total energy of an average earthquake goes up by a factor of approximately 32 for each unit increase in magnitude. There are various ways that magnitude may be calculated from seismograms. Different methods are effective for different sizes of earthquakes and different distances between the earthquake source and the recording station. The various magnitude types are generally defined so as to yield magnitude values that agree to within a few-tenths of a magnitude-unit for earthquakes in a middle range of recorded-earthquake sizes, but the various magnitude-types may have values that differ by more than a magnitude-unit for very large and very small earthquakes as well as for some specific classes of seismic source. This is because earthquakes are commonly complex events that release energy over a wide range of frequencies and at varying amounts as the faulting or rupture process occurs. The various types of magnitude measure different aspects of the seismic radiation (e.g., low-frequency energy vs. high-frequency energy). The relationship among values of different magnitude types that are assigned to a particular seismic event may enable the seismologist to better understand the processes at the focus of the seismic event. The various magnitude-types are not all available at the same time for a particular earthquake. Preliminary magnitudes based on incomplete but rapidly-available data are sometimes estimated and reported. For example, the Tsunami Warning Centers will calculate a preliminary magnitude and location for an event as soon as sufficient data are available to make an estimate. In this case, time is of the essence in order to broadcast a warning if tsunami waves are likely to be generated by the event. Such preliminary magnitudes are superseded by improved estimates of magnitude as more data become available. For large earthquakes of the present era, the magnitude that is ultimately selected as the preferred magnitude for reporting to the public is commonly a moment magnitude that is based on the scalar seismic-moment of an earthquake determined by calculation of the seismic moment-tensor that best accounts for the character of the seismic waves generated by the earthquake. The scalar seismic-moment, a parameter of the seismic moment-tensor, can also be estimated via the multiplicative product rigidity of faulted rock x area of fault rupture x average fault displacement during the earthquake. magError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported magnitude of the event. The estimated standard error of the magnitude. The uncertainty corresponds to the specific magnitude type being reported and does not take into account magnitude variations and biases between different magnitude scales. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. magNst Data Type: Integer Description: The total number of seismic stations used to calculate the magnitude for this earthquake. magSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: Network that originally authored the reported magnitude for this event. magType Data Type: String Typical Values: “Md”, “Ml”, “Ms”, “Mw”, “Me”, “Mi”, “Mb”, “MLg” Description: The method or algorithm used to calculate the preferred magnitude for the event. Additional Information: See Magnitude Types Table. mmi Data Type: Decimal Typical Values:[0.0, 10.0] Description: The maximum estimated instrumental intensity for the event. Computed by ShakeMap. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. net Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event. nph Data Type: String Description: Number of Phases Used. Number of P and S arrival-time observations used to compute the hypocenter location. Increased numbers of arrival-time observations generally result in improved earthquake locations. nst Data Type: Integer Description: The total number of seismic stations used to determine earthquake location. Additional Information: Number of seismic stations which reported P- and S-arrival times for this earthquake. This number may be larger than the Number of Phases Used if arrival times are rejected because the distance to a seismic station exceeds the maximum allowable distance or because the arrival-time observation is inconsistent with the solution. place Data Type: String Description: Textual description of named geographic region near to the event. This may be a city name, or a Flinn-Engdahl Region name. Additional Information: We use a GeoNames dataset to reference populated places that are in close proximity to a seismic event. GeoNames has compiled a list of cities in the United States where the population is 1,000 or greater (cities1000.txt). This is the primary list that we use when selecting nearby places. In order to provide the public with a better understanding for the location of an event we try to list a variety of places in our nearby places list. This includes the closest known populated place in relation to the seismic event (which based on our dataset will have a population of 1,000 or greater). We also include the next 3 closest places that have a population of 10,000 or greater, and finally make sure to include the closest capital city to the seismic event. The reference point for the descriptive locations is usually either the City Hall of the town (or prominent intersection in the middle of town if there is no City Hall), but please refer to the GeoNames website for the most accurate information on their data. If there is no nearby city within 300 kilometers (or if the nearby cities database is unavailable for some reason), the Flinn-Engdahl (F-E) seismic and geographical regionalization scheme is used. The boundaries of these regions are defined at one-degree intervals and therefore differ from irregular political boundaries. For example, F-E region 545 (Northern Italy) also includes small parts of France, Switzerland, Austria and Slovenia and F-E region 493 (Chesapeake Bay Region) includes all of the State of Delaware, plus parts of the District of Columbia, Maryland, New Jersey, Pennsylvania and Virginia. Beginning with January 2000, the 1995 revision to the F-E code has been used in the QED and PDE listings. As an agency of the U.S. Government, we are expected to use the names and spellings approved by the U.S. Board on Geographic Names. Any requests to approve additional names should be made to the U.S. Board on Geographic Names. rms Data Type: Decimal Typical Values: [0.13,1.39] Description: The root-mean-square (RMS) travel time residual, in sec, using all weights. This parameter provides a measure of the fit of the observed arrival times to the predicted arrival times for this location. Smaller numbers reflect a better fit of the data. The value is dependent on the accuracy of the velocity model used to compute the earthquake location, the quality weights assigned to the arrival time data, and the procedure used to locate the earthquake. sig Data Type: Integer Typical Values: [0, 1000] Description: A number describing how significant the event is. Larger numbers indicate a more significant event. This value is determined on a number of factors, including: magnitude, maximum MMI, felt reports, and estimated impact. sources Data Type: String Typical Values: ",us,nc,ci," Description: A comma-separated list of network contributors. status Data Type: String Typical Values: “automatic”, “reviewed”, “deleted” Description: Indicates whether the event has been reviewed by a human. Additional Information Status is either automatic or reviewed. Automatic events are directly posted by automatic processing systems and have not been verified or altered by a human. Reviewed events have been looked at by a human. The level of review can range from a quick validity check to a careful reanalysis of the event. time Data Type: Long Integer Description: Time when the event occurred. Times are reported in milliseconds since the epoch ( 1970-01-01T00:00:00.000Z), and do not include leap seconds. In certain output formats, the date is formatted for readability. Additional Information: We indicate the date and time when the earthquake initiates rupture, which is known as the "origin" time. Note that large earthquakes can continue rupturing for many 10's of seconds. We provide time in UTC (Coordinated Universal Time). Seismologists use UTC to avoid confusion caused by local time zones and daylight savings time. On the individual event pages, times are also provided for the time at the epicenter, and your local time based on the time your computer is set. tsunami Data Type: Integer Description: This flag is set to "1" for large events in oceanic regions and "0" otherwise. The existence or value of this flag does not indicate if a tsunami actually did or will exist. If the flag value is "1", the event will include a link to the NOAA Tsunami website for tsunami information. The USGS is not responsible for Tsunami warning; we are simply providing a link to the authoritative NOAA source. See http://www.tsunami.gov/ for all current tsunami alert statuses. type Data Type: String Typical Values: “earthquake”, “quarry” Description: Type of seismic event. types Data Type: String Typical Values: “,cap,dyfi,general-link,origin,p-wave-travel-times,phase-data,” Description: A comma-separated list of product types associated to this event. tz Data Type: Integer Typical Values: [-1200, +1200] Description: Timezone offset from UTC in minutes at the event epicenter. updated Data Type: Long Integer Description: Time when the event was most recently updated. Times are reported in milliseconds since the epoch. In certain output formats, the date is formatted for readability. url Data Type: String Description: Link to USGS Event Page for event. features Pacific_2005-2009_EarthquakeMag4.5_USGS earthquake USGS hazard EPSG:4326 CRS:84 -179.998 180.0 -35.161 28.417 text/plain pacific:Pacific_2010-2014_EarthquakeMag4.5_USGS Earthquake centers over a magnitude of 4.5 for the Pacific Islands Region for years 2010-2014. Data are derived from the ANSS Comprehensive Earthquake Catalog (ComCat). ComCat contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks. Fields are defined below: alert Data Type: String Typical Values: “green”, “yellow”, “orange”, “red”. Description: The alert level from the PAGER earthquake impact scale. cdi Data Type: Decimal Typical Values: [0.0, 10.0] Description: The maximum reported intensity for the event. Computed by DYFI. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. code Data Type: String Typical Values: "2013lgaz", "c000f1jy", "71935551" Description: An identifying code assigned by - and unique from - the corresponding source for the event. Depth Data Type: Decimal Typical Values: [0, 1000] Description: Depth of the event in kilometers. Additional Information: The depth where the earthquake begins to rupture. This depth may be relative to the WGS84 geoid, mean sea-level, or the average elevation of the seismic stations which provided arrival-time data for the earthquake location. The choice of reference depth is dependent on the method used to locate the earthquake, which varies by seismic network. Since ComCat includes data from many different seismic networks, the process for determining the depth is different for different events. The depth is the least-constrained parameter in the earthquake location, and the error bars are generally larger than the variation due to different depth determination methods. Sometimes when depth is poorly constrained by available seismic data, the location program will set the depth at a fixed value. For example, 33 km is often used as a default depth for earthquakes determined to be shallow, but whose depth is not satisfactorily determined by the data, whereas default depths of 5 or 10 km are often used in mid-continental areas and on mid-ocean ridges since earthquakes in these areas are usually shallower than 33 km. depthError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported depth of the event in kilometers. Additional Information: The depth error, in km, defined as the largest projection of the three principal errors on a vertical line. detail Data Type: String Description: Link to GeoJSON detail feed from a GeoJSON summary feed. NOTE: When searching and using geojson with callback, no callback is included in the detail url. dmin Data Type: Decimal Typical Values: [0.4, 7.1] Description: Horizontal distance from the epicenter to the nearest station (in degrees). 1 degree is approximately 111.2 kilometers. In general, the smaller this number, the more reliable is the calculated depth of the earthquake. felt Data Type: Integer Typical Values: [44, 843] Description: The total number of felt reports submitted to the DYFI? system. gap Data Type: Decimal Typical Values: [0.0, 180.0] Description: The largest azimuthal gap between azimuthally adjacent stations (in degrees). In general, the smaller this number, the more reliable is the calculated horizontal position of the earthquake. Earthquake locations in which the azimuthal gap exceeds 180 degrees typically have large location and depth uncertainties. horizontalError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported location of the event in kilometers. Additional Information: The horizontal location error, in km, defined as the length of the largest projection of the three principal errors on a horizontal plane. The principal errors are the major axes of the error ellipsoid, and are mutually perpendicular. The horizontal and vertical uncertainties in an event's location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. id Data Type: String Typical Values: A (generally) two-character network identifier with a (generally) eight-character network-assigned code. Description: A unique identifier for the event. This is the current preferred id for the event, and may change over time. See the "ids" GeoJSON format property. ids Data Type: String Typical Values: ",ci15296281,us2013mqbd,at00mji9pf," Description: A comma-separated list of event ids that are associated to an event. latitude Data Type: Decimal Typical Values: [-90.0, 90.0] Description: Decimal degrees latitude. Negative values for southern latitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. locationSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The network that originally authored the reported location of this event. longitude Data Type: Decimal Typical Values: [-180.0, 180.0] Description: Decimal degrees longitude. Negative values for western longitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. mag Data Type: Decimal Typical Values: [-1.0, 10.0] Description: The magnitude for the event. See also magType. Additional Information: The magnitude reported is that which the U.S. Geological Survey considers official for this earthquake, and was the best available estimate of the earthquake’s size, at the time that this page was created. Other magnitudes associated with web pages linked from here are those determined at various times following the earthquake with different types of seismic data. Although they are legitimate estimates of magnitude, the U.S. Geological Survey does not consider them to be the preferred "official" magnitude for the event. Earthquake magnitude is a measure of the size of an earthquake at its source. It is a logarithmic measure. At the same distance from the earthquake, the amplitude of the seismic waves from which the magnitude is determined are approximately 10 times as large during a magnitude 5 earthquake as during a magnitude 4 earthquake. The total amount of energy released by the earthquake usually goes up by a larger factor: for many commonly used magnitude types, the total energy of an average earthquake goes up by a factor of approximately 32 for each unit increase in magnitude. There are various ways that magnitude may be calculated from seismograms. Different methods are effective for different sizes of earthquakes and different distances between the earthquake source and the recording station. The various magnitude types are generally defined so as to yield magnitude values that agree to within a few-tenths of a magnitude-unit for earthquakes in a middle range of recorded-earthquake sizes, but the various magnitude-types may have values that differ by more than a magnitude-unit for very large and very small earthquakes as well as for some specific classes of seismic source. This is because earthquakes are commonly complex events that release energy over a wide range of frequencies and at varying amounts as the faulting or rupture process occurs. The various types of magnitude measure different aspects of the seismic radiation (e.g., low-frequency energy vs. high-frequency energy). The relationship among values of different magnitude types that are assigned to a particular seismic event may enable the seismologist to better understand the processes at the focus of the seismic event. The various magnitude-types are not all available at the same time for a particular earthquake. Preliminary magnitudes based on incomplete but rapidly-available data are sometimes estimated and reported. For example, the Tsunami Warning Centers will calculate a preliminary magnitude and location for an event as soon as sufficient data are available to make an estimate. In this case, time is of the essence in order to broadcast a warning if tsunami waves are likely to be generated by the event. Such preliminary magnitudes are superseded by improved estimates of magnitude as more data become available. For large earthquakes of the present era, the magnitude that is ultimately selected as the preferred magnitude for reporting to the public is commonly a moment magnitude that is based on the scalar seismic-moment of an earthquake determined by calculation of the seismic moment-tensor that best accounts for the character of the seismic waves generated by the earthquake. The scalar seismic-moment, a parameter of the seismic moment-tensor, can also be estimated via the multiplicative product rigidity of faulted rock x area of fault rupture x average fault displacement during the earthquake. magError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported magnitude of the event. The estimated standard error of the magnitude. The uncertainty corresponds to the specific magnitude type being reported and does not take into account magnitude variations and biases between different magnitude scales. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. magNst Data Type: Integer Description: The total number of seismic stations used to calculate the magnitude for this earthquake. magSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: Network that originally authored the reported magnitude for this event. magType Data Type: String Typical Values: “Md”, “Ml”, “Ms”, “Mw”, “Me”, “Mi”, “Mb”, “MLg” Description: The method or algorithm used to calculate the preferred magnitude for the event. Additional Information: See Magnitude Types Table. mmi Data Type: Decimal Typical Values:[0.0, 10.0] Description: The maximum estimated instrumental intensity for the event. Computed by ShakeMap. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. net Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event. nph Data Type: String Description: Number of Phases Used. Number of P and S arrival-time observations used to compute the hypocenter location. Increased numbers of arrival-time observations generally result in improved earthquake locations. nst Data Type: Integer Description: The total number of seismic stations used to determine earthquake location. Additional Information: Number of seismic stations which reported P- and S-arrival times for this earthquake. This number may be larger than the Number of Phases Used if arrival times are rejected because the distance to a seismic station exceeds the maximum allowable distance or because the arrival-time observation is inconsistent with the solution. place Data Type: String Description: Textual description of named geographic region near to the event. This may be a city name, or a Flinn-Engdahl Region name. Additional Information: We use a GeoNames dataset to reference populated places that are in close proximity to a seismic event. GeoNames has compiled a list of cities in the United States where the population is 1,000 or greater (cities1000.txt). This is the primary list that we use when selecting nearby places. In order to provide the public with a better understanding for the location of an event we try to list a variety of places in our nearby places list. This includes the closest known populated place in relation to the seismic event (which based on our dataset will have a population of 1,000 or greater). We also include the next 3 closest places that have a population of 10,000 or greater, and finally make sure to include the closest capital city to the seismic event. The reference point for the descriptive locations is usually either the City Hall of the town (or prominent intersection in the middle of town if there is no City Hall), but please refer to the GeoNames website for the most accurate information on their data. If there is no nearby city within 300 kilometers (or if the nearby cities database is unavailable for some reason), the Flinn-Engdahl (F-E) seismic and geographical regionalization scheme is used. The boundaries of these regions are defined at one-degree intervals and therefore differ from irregular political boundaries. For example, F-E region 545 (Northern Italy) also includes small parts of France, Switzerland, Austria and Slovenia and F-E region 493 (Chesapeake Bay Region) includes all of the State of Delaware, plus parts of the District of Columbia, Maryland, New Jersey, Pennsylvania and Virginia. Beginning with January 2000, the 1995 revision to the F-E code has been used in the QED and PDE listings. As an agency of the U.S. Government, we are expected to use the names and spellings approved by the U.S. Board on Geographic Names. Any requests to approve additional names should be made to the U.S. Board on Geographic Names. rms Data Type: Decimal Typical Values: [0.13,1.39] Description: The root-mean-square (RMS) travel time residual, in sec, using all weights. This parameter provides a measure of the fit of the observed arrival times to the predicted arrival times for this location. Smaller numbers reflect a better fit of the data. The value is dependent on the accuracy of the velocity model used to compute the earthquake location, the quality weights assigned to the arrival time data, and the procedure used to locate the earthquake. sig Data Type: Integer Typical Values: [0, 1000] Description: A number describing how significant the event is. Larger numbers indicate a more significant event. This value is determined on a number of factors, including: magnitude, maximum MMI, felt reports, and estimated impact. sources Data Type: String Typical Values: ",us,nc,ci," Description: A comma-separated list of network contributors. status Data Type: String Typical Values: “automatic”, “reviewed”, “deleted” Description: Indicates whether the event has been reviewed by a human. Additional Information Status is either automatic or reviewed. Automatic events are directly posted by automatic processing systems and have not been verified or altered by a human. Reviewed events have been looked at by a human. The level of review can range from a quick validity check to a careful reanalysis of the event. time Data Type: Long Integer Description: Time when the event occurred. Times are reported in milliseconds since the epoch ( 1970-01-01T00:00:00.000Z), and do not include leap seconds. In certain output formats, the date is formatted for readability. Additional Information: We indicate the date and time when the earthquake initiates rupture, which is known as the "origin" time. Note that large earthquakes can continue rupturing for many 10's of seconds. We provide time in UTC (Coordinated Universal Time). Seismologists use UTC to avoid confusion caused by local time zones and daylight savings time. On the individual event pages, times are also provided for the time at the epicenter, and your local time based on the time your computer is set. tsunami Data Type: Integer Description: This flag is set to "1" for large events in oceanic regions and "0" otherwise. The existence or value of this flag does not indicate if a tsunami actually did or will exist. If the flag value is "1", the event will include a link to the NOAA Tsunami website for tsunami information. The USGS is not responsible for Tsunami warning; we are simply providing a link to the authoritative NOAA source. See http://www.tsunami.gov/ for all current tsunami alert statuses. type Data Type: String Typical Values: “earthquake”, “quarry” Description: Type of seismic event. types Data Type: String Typical Values: “,cap,dyfi,general-link,origin,p-wave-travel-times,phase-data,” Description: A comma-separated list of product types associated to this event. tz Data Type: Integer Typical Values: [-1200, +1200] Description: Timezone offset from UTC in minutes at the event epicenter. updated Data Type: Long Integer Description: Time when the event was most recently updated. Times are reported in milliseconds since the epoch. In certain output formats, the date is formatted for readability. url Data Type: String Description: Link to USGS Event Page for event. features Pacific_2010-2014_EarthquakeMag4.5_USGS earthquake hazard USGS EPSG:4326 CRS:84 -179.9972 179.9975 -35.1536 28.441 text/plain pacific:Pacific_2015-2019_EarthquakeMag4.5_USGS Earthquake centers over a magnitude of 4.5 for the Pacific Islands Region for years 2015-2019. Data are derived from the ANSS Comprehensive Earthquake Catalog (ComCat). ComCat contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks. Fields are defined below: alert Data Type: String Typical Values: “green”, “yellow”, “orange”, “red”. Description: The alert level from the PAGER earthquake impact scale. cdi Data Type: Decimal Typical Values: [0.0, 10.0] Description: The maximum reported intensity for the event. Computed by DYFI. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. code Data Type: String Typical Values: "2013lgaz", "c000f1jy", "71935551" Description: An identifying code assigned by - and unique from - the corresponding source for the event. Depth Data Type: Decimal Typical Values: [0, 1000] Description: Depth of the event in kilometers. Additional Information: The depth where the earthquake begins to rupture. This depth may be relative to the WGS84 geoid, mean sea-level, or the average elevation of the seismic stations which provided arrival-time data for the earthquake location. The choice of reference depth is dependent on the method used to locate the earthquake, which varies by seismic network. Since ComCat includes data from many different seismic networks, the process for determining the depth is different for different events. The depth is the least-constrained parameter in the earthquake location, and the error bars are generally larger than the variation due to different depth determination methods. Sometimes when depth is poorly constrained by available seismic data, the location program will set the depth at a fixed value. For example, 33 km is often used as a default depth for earthquakes determined to be shallow, but whose depth is not satisfactorily determined by the data, whereas default depths of 5 or 10 km are often used in mid-continental areas and on mid-ocean ridges since earthquakes in these areas are usually shallower than 33 km. depthError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported depth of the event in kilometers. Additional Information: The depth error, in km, defined as the largest projection of the three principal errors on a vertical line. detail Data Type: String Description: Link to GeoJSON detail feed from a GeoJSON summary feed. NOTE: When searching and using geojson with callback, no callback is included in the detail url. dmin Data Type: Decimal Typical Values: [0.4, 7.1] Description: Horizontal distance from the epicenter to the nearest station (in degrees). 1 degree is approximately 111.2 kilometers. In general, the smaller this number, the more reliable is the calculated depth of the earthquake. felt Data Type: Integer Typical Values: [44, 843] Description: The total number of felt reports submitted to the DYFI? system. gap Data Type: Decimal Typical Values: [0.0, 180.0] Description: The largest azimuthal gap between azimuthally adjacent stations (in degrees). In general, the smaller this number, the more reliable is the calculated horizontal position of the earthquake. Earthquake locations in which the azimuthal gap exceeds 180 degrees typically have large location and depth uncertainties. horizontalError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported location of the event in kilometers. Additional Information: The horizontal location error, in km, defined as the length of the largest projection of the three principal errors on a horizontal plane. The principal errors are the major axes of the error ellipsoid, and are mutually perpendicular. The horizontal and vertical uncertainties in an event's location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. id Data Type: String Typical Values: A (generally) two-character network identifier with a (generally) eight-character network-assigned code. Description: A unique identifier for the event. This is the current preferred id for the event, and may change over time. See the "ids" GeoJSON format property. ids Data Type: String Typical Values: ",ci15296281,us2013mqbd,at00mji9pf," Description: A comma-separated list of event ids that are associated to an event. latitude Data Type: Decimal Typical Values: [-90.0, 90.0] Description: Decimal degrees latitude. Negative values for southern latitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. locationSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The network that originally authored the reported location of this event. longitude Data Type: Decimal Typical Values: [-180.0, 180.0] Description: Decimal degrees longitude. Negative values for western longitudes. Additional Information: An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world. mag Data Type: Decimal Typical Values: [-1.0, 10.0] Description: The magnitude for the event. See also magType. Additional Information: The magnitude reported is that which the U.S. Geological Survey considers official for this earthquake, and was the best available estimate of the earthquake’s size, at the time that this page was created. Other magnitudes associated with web pages linked from here are those determined at various times following the earthquake with different types of seismic data. Although they are legitimate estimates of magnitude, the U.S. Geological Survey does not consider them to be the preferred "official" magnitude for the event. Earthquake magnitude is a measure of the size of an earthquake at its source. It is a logarithmic measure. At the same distance from the earthquake, the amplitude of the seismic waves from which the magnitude is determined are approximately 10 times as large during a magnitude 5 earthquake as during a magnitude 4 earthquake. The total amount of energy released by the earthquake usually goes up by a larger factor: for many commonly used magnitude types, the total energy of an average earthquake goes up by a factor of approximately 32 for each unit increase in magnitude. There are various ways that magnitude may be calculated from seismograms. Different methods are effective for different sizes of earthquakes and different distances between the earthquake source and the recording station. The various magnitude types are generally defined so as to yield magnitude values that agree to within a few-tenths of a magnitude-unit for earthquakes in a middle range of recorded-earthquake sizes, but the various magnitude-types may have values that differ by more than a magnitude-unit for very large and very small earthquakes as well as for some specific classes of seismic source. This is because earthquakes are commonly complex events that release energy over a wide range of frequencies and at varying amounts as the faulting or rupture process occurs. The various types of magnitude measure different aspects of the seismic radiation (e.g., low-frequency energy vs. high-frequency energy). The relationship among values of different magnitude types that are assigned to a particular seismic event may enable the seismologist to better understand the processes at the focus of the seismic event. The various magnitude-types are not all available at the same time for a particular earthquake. Preliminary magnitudes based on incomplete but rapidly-available data are sometimes estimated and reported. For example, the Tsunami Warning Centers will calculate a preliminary magnitude and location for an event as soon as sufficient data are available to make an estimate. In this case, time is of the essence in order to broadcast a warning if tsunami waves are likely to be generated by the event. Such preliminary magnitudes are superseded by improved estimates of magnitude as more data become available. For large earthquakes of the present era, the magnitude that is ultimately selected as the preferred magnitude for reporting to the public is commonly a moment magnitude that is based on the scalar seismic-moment of an earthquake determined by calculation of the seismic moment-tensor that best accounts for the character of the seismic waves generated by the earthquake. The scalar seismic-moment, a parameter of the seismic moment-tensor, can also be estimated via the multiplicative product rigidity of faulted rock x area of fault rupture x average fault displacement during the earthquake. magError Data Type: Decimal Typical Values: [0, 100] Description: Uncertainty of reported magnitude of the event. The estimated standard error of the magnitude. The uncertainty corresponds to the specific magnitude type being reported and does not take into account magnitude variations and biases between different magnitude scales. We report an "unknown" value if the contributing seismic network does not supply uncertainty estimates. magNst Data Type: Integer Description: The total number of seismic stations used to calculate the magnitude for this earthquake. magSource Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: Network that originally authored the reported magnitude for this event. magType Data Type: String Typical Values: “Md”, “Ml”, “Ms”, “Mw”, “Me”, “Mi”, “Mb”, “MLg” Description: The method or algorithm used to calculate the preferred magnitude for the event. Additional Information: See Magnitude Types Table. mmi Data Type: Decimal Typical Values:[0.0, 10.0] Description: The maximum estimated instrumental intensity for the event. Computed by ShakeMap. While typically reported as a roman numeral, for the purposes of this API, intensity is expected as the decimal equivalent of the roman numeral. Learn more about magnitude vs. intensity. net Data Type: String Typical Values: ak, at, ci, hv, ld, mb, nc, nm, nn, pr, pt, se, us, uu, uw Description: The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event. nph Data Type: String Description: Number of Phases Used. Number of P and S arrival-time observations used to compute the hypocenter location. Increased numbers of arrival-time observations generally result in improved earthquake locations. nst Data Type: Integer Description: The total number of seismic stations used to determine earthquake location. Additional Information: Number of seismic stations which reported P- and S-arrival times for this earthquake. This number may be larger than the Number of Phases Used if arrival times are rejected because the distance to a seismic station exceeds the maximum allowable distance or because the arrival-time observation is inconsistent with the solution. place Data Type: String Description: Textual description of named geographic region near to the event. This may be a city name, or a Flinn-Engdahl Region name. Additional Information: We use a GeoNames dataset to reference populated places that are in close proximity to a seismic event. GeoNames has compiled a list of cities in the United States where the population is 1,000 or greater (cities1000.txt). This is the primary list that we use when selecting nearby places. In order to provide the public with a better understanding for the location of an event we try to list a variety of places in our nearby places list. This includes the closest known populated place in relation to the seismic event (which based on our dataset will have a population of 1,000 or greater). We also include the next 3 closest places that have a population of 10,000 or greater, and finally make sure to include the closest capital city to the seismic event. The reference point for the descriptive locations is usually either the City Hall of the town (or prominent intersection in the middle of town if there is no City Hall), but please refer to the GeoNames website for the most accurate information on their data. If there is no nearby city within 300 kilometers (or if the nearby cities database is unavailable for some reason), the Flinn-Engdahl (F-E) seismic and geographical regionalization scheme is used. The boundaries of these regions are defined at one-degree intervals and therefore differ from irregular political boundaries. For example, F-E region 545 (Northern Italy) also includes small parts of France, Switzerland, Austria and Slovenia and F-E region 493 (Chesapeake Bay Region) includes all of the State of Delaware, plus parts of the District of Columbia, Maryland, New Jersey, Pennsylvania and Virginia. Beginning with January 2000, the 1995 revision to the F-E code has been used in the QED and PDE listings. As an agency of the U.S. Government, we are expected to use the names and spellings approved by the U.S. Board on Geographic Names. Any requests to approve additional names should be made to the U.S. Board on Geographic Names. rms Data Type: Decimal Typical Values: [0.13,1.39] Description: The root-mean-square (RMS) travel time residual, in sec, using all weights. This parameter provides a measure of the fit of the observed arrival times to the predicted arrival times for this location. Smaller numbers reflect a better fit of the data. The value is dependent on the accuracy of the velocity model used to compute the earthquake location, the quality weights assigned to the arrival time data, and the procedure used to locate the earthquake. sig Data Type: Integer Typical Values: [0, 1000] Description: A number describing how significant the event is. Larger numbers indicate a more significant event. This value is determined on a number of factors, including: magnitude, maximum MMI, felt reports, and estimated impact. sources Data Type: String Typical Values: ",us,nc,ci," Description: A comma-separated list of network contributors. status Data Type: String Typical Values: “automatic”, “reviewed”, “deleted” Description: Indicates whether the event has been reviewed by a human. Additional Information Status is either automatic or reviewed. Automatic events are directly posted by automatic processing systems and have not been verified or altered by a human. Reviewed events have been looked at by a human. The level of review can range from a quick validity check to a careful reanalysis of the event. time Data Type: Long Integer Description: Time when the event occurred. Times are reported in milliseconds since the epoch ( 1970-01-01T00:00:00.000Z), and do not include leap seconds. In certain output formats, the date is formatted for readability. Additional Information: We indicate the date and time when the earthquake initiates rupture, which is known as the "origin" time. Note that large earthquakes can continue rupturing for many 10's of seconds. We provide time in UTC (Coordinated Universal Time). Seismologists use UTC to avoid confusion caused by local time zones and daylight savings time. On the individual event pages, times are also provided for the time at the epicenter, and your local time based on the time your computer is set. tsunami Data Type: Integer Description: This flag is set to "1" for large events in oceanic regions and "0" otherwise. The existence or value of this flag does not indicate if a tsunami actually did or will exist. If the flag value is "1", the event will include a link to the NOAA Tsunami website for tsunami information. The USGS is not responsible for Tsunami warning; we are simply providing a link to the authoritative NOAA source. See http://www.tsunami.gov/ for all current tsunami alert statuses. type Data Type: String Typical Values: “earthquake”, “quarry” Description: Type of seismic event. types Data Type: String Typical Values: “,cap,dyfi,general-link,origin,p-wave-travel-times,phase-data,” Description: A comma-separated list of product types associated to this event. tz Data Type: Integer Typical Values: [-1200, +1200] Description: Timezone offset from UTC in minutes at the event epicenter. updated Data Type: Long Integer Description: Time when the event was most recently updated. Times are reported in milliseconds since the epoch. In certain output formats, the date is formatted for readability. url Data Type: String Description: Link to USGS Event Page for event. features Pacific_2015-2019_EarthquakeMag4.5_USGS earthquake USGS hazard EPSG:4326 CRS:84 -179.9983 179.9993 -35.143 28.4707 text/plain pacific:Pacific_2018_DeepwaterBioregions_MACBIO Bioregions, of course, are just one of the important data layers in indentifying an ecologically representative system of marine protected areas. To be truly ecologically representative and comprehensive, one must also consider all available information about habitats, species and ecological processes. In addition, socio-economic and cultural considerations are vital in the spatial planning process. This report is focussed upon one important, but only one, input to marine spatial planning: the development of marine bioregions. To take account of differing types and resolution of data, two separate bioregionalisations were developed; firstly, for the deepwater environments and secondly for reef-associated environments. For the deepwater, thirty, mainly physical, environmental variables were assessed to be adequately comprehensive and reliable to be included in the analysis. These data were allocated to over 140 000 grid cells of 20x20 km across the Southwest Pacific. K-means and then hierarchical cluster analyses were then conducted to identify groups of analytical units that contained similar environmental conditions. The number of clusters was determined by examining the dendrogram and setting a similarity value that aligned with a natural break in similarity. For the second bioregionalisation, reef-associated datasets of more than 200 fish, coral and other invertebrate species were collated from multiple data providers who sampled over 6500 sites. We combined these datasets, which were quality-checked for taxonomic consistency and normalised, resulting in more than 800 species that could be used in further analysis. All these species data and seven independent environmental datasets were then allocated to over 45,000 grid cells of 9x9 km across the SW Pacific. Next, the probability of observing these species was predicted, using the environmental variables, for grid cells within the unsurveyed reef-associated habitats. Hierarchical cluster analysis was then applied to the reef-associated datasets to deliver clusters of grid cells with high similarity. The final analytical steps, applied to all the outputs, were to refine the resulting clusters using manual spatial processing and to describe each cluster to deliver the draft bioregions. This work resulted in 262 draft deepwater marine bioregions and 102 draft reef-associated bioregions across the SW Pacific. Please cite this dataset as: Wendt H., Beger M., Sullivan J., LeGrand J., Davey K., Yakub N., Fernandes L. 2018. Draft marine bioregions of the Southwest Pacific.” GIZ, IUCN, SPREP: Suva. features Pacific_2018_DeepwaterBioregions_MACBIO bioregions marine IUCN EPSG:3349 CRS:84 -173.11273117165092 -70.16430491956679 -32.91569751803236 20.04913331557492 text/plain pacific:Pacific_2018_ReefBioregions_MACBIO Bioregions, of course, are just one of the important data layers in indentifying an ecologically representative system of marine protected areas. To be truly ecologically representative and comprehensive, one must also consider all available information about habitats, species and ecological processes. In addition, socio-economic and cultural considerations are vital in the spatial planning process. This report is focussed upon one important, but only one, input to marine spatial planning: the development of marine bioregions. To take account of differing types and resolution of data, two separate bioregionalisations were developed; firstly, for the deepwater environments and secondly for reef-associated environments. For the deepwater, thirty, mainly physical, environmental variables were assessed to be adequately comprehensive and reliable to be included in the analysis. These data were allocated to over 140 000 grid cells of 20x20 km across the Southwest Pacific. K-means and then hierarchical cluster analyses were then conducted to identify groups of analytical units that contained similar environmental conditions. The number of clusters was determined by examining the dendrogram and setting a similarity value that aligned with a natural break in similarity. For the second bioregionalisation, reef-associated datasets of more than 200 fish, coral and other invertebrate species were collated from multiple data providers who sampled over 6500 sites. We combined these datasets, which were quality-checked for taxonomic consistency and normalised, resulting in more than 800 species that could be used in further analysis. All these species data and seven independent environmental datasets were then allocated to over 45,000 grid cells of 9x9 km across the SW Pacific. Next, the probability of observing these species was predicted, using the environmental variables, for grid cells within the unsurveyed reef-associated habitats. Hierarchical cluster analysis was then applied to the reef-associated datasets to deliver clusters of grid cells with high similarity. The final analytical steps, applied to all the outputs, were to refine the resulting clusters using manual spatial processing and to describe each cluster to deliver the draft bioregions. This work resulted in 262 draft deepwater marine bioregions and 102 draft reef-associated bioregions across the SW Pacific. Please cite this dataset as: Wendt H., Beger M., Sullivan J., LeGrand J., Davey K., Yakub N., Fernandes L. 2018. Draft marine bioregions of the Southwest Pacific.” GIZ, IUCN, SPREP: Suva. features Pacific_2018_ReefBioregions_MACBIO reef marine IUCN bioregions EPSG:3349 CRS:84 -180.0 180.0 -59.99999999999866 66.66999999999982 text/plain pacific:Pacific_2020_GriddedBathymetricData_GEBCO2020 GEBCO’s gridded bathymetric data set, the GEBCO_2020 grid, is a global terrain model for ocean and land at 15 arc-second intervals. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2020 Grid is based. If the data sets are used in a presentation or publication then we ask that you acknowledge the source.This should be of the form: GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9) The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information. Pacific_2020_GriddedBathymetricData_GEBCO2020 WCS GeoTIFF bathymetry seafloor depth EPSG:4326 CRS:84 130.77916666666667 234.84583333333333 -32.608333333333334 29.620833333333326 text/plain slb:SLB_2019-2020_ImageryNewGeorgiaIslands_Sentinel2 This resource contains satellite imagery for the New Georgia Islands in the Solomon Islands. The imagery was collected on September 26, 2019, September 12, 2020, September 25, 2020, October 2, 2020, October 10, 2020, and October 20, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. S2B_MSIL2A_20190926T235739_N0213_R030_T57MUM_20190927T015140 S2A_MSIL2A_20200912T234751_N0214_R130_T57LUL_20200913T014018 S2A_MSIL1C_20200925T235751_N0209_R030_T57LUL_20200926T011618 S2A_MSIL2A_20201002T234751_N0214_R130_T57LVL_20201003T014939 S2B_MSIL1C_20201010T235749_N0209_R030_T57MTM_20201011T011117 S2B_MSIL1C_20201010T235749_N0209_R030_T56LRR_20201011T011117 S2B_MSIL1C_20201020T235749_N0209_R030_T57LTL_20201021T013409 SLB_2019-2020_ImageryNewGeorgiaIslands_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Solomon Islands EPSG:32757 CRS:84 156.29522157219486 158.5469685485203 -9.042073447810965 -7.4999909955586626 text/plain slb:SLB_2019-2020_ImageryOntongJava_Sentinel2 This resource contains satellite imagery for the Ontong Java Atoll in the Solomon Islands. The imagery was collected on December 2, 2019 and March 16, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20191202T234739_N0213_R130_T57MWP_20191203T013156 S2A_MSIL1C_20200316T234741_N0209_R130_T57MWQ_20200317T011621 SLB_2019-2020_ImageryOntongJava_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Solomon Islands EPSG:32757 CRS:84 158.99981913784433 159.99270175480942 -6.42151393209642 -4.522705300445328 text/plain slb:SLB_2019-2020_ImageryRennell_Sentinel2 This resource contains satellite imagery for the Rennell Island in the Solomon Islands. The imagery was collected on December 29, 2019, March 3, 2020, and May 2, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20191229T233809_N0213_R087_T57LXH_20191230T012549 S2A_MSIL2A_20200303T233811_N0214_R087_T57LWH_20200304T014717 S2A_MSIL2A_20200502T233821_N0214_R087_T57LXG_20200503T013522 SLB_2019-2020_ImageryRennell_Sentinel2 WCS GeoTIFF Solomon Islands satellite imagery Sentinel-2 earth observation EPSG:32757 CRS:84 159.55414054119154 160.77989109027692 -12.402175247689925 -11.235923649498009 text/plain slb:SLB_2019-2020_ImagerySanCristobal_Sentinel2 This resource contains satellite imagery for the San Cristobal in the Solomon Islands. The imagery was collected on September 5, 2019, January 18, 2020, and April 7, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20190905T233821_N0213_R087_T57LYJ_20190906T013545 S2B_MSIL2A_20200118T233809_N0213_R087_T57LZK_20200119T012832 S2B_MSIL2A_20200407T233809_N0214_R087_T57LZJ_20200408T014505 SLB_2019-2020_ImagerySanCristobal_Sentinel2 WCS GeoTIFF imagery earth observation satellite Sentinel-2 Solomon Islands EPSG:32757 CRS:84 160.81909991169658 162.7475979306164 -10.938500009049871 -9.027228624074892 text/plain slb:SLB_2019-2020_ImagerySantaCruzIslands_Sentinel2 This resource contains satellite imagery for the Santa Cruz Islands in the Solomon Islands. The imagery was collected on November 8, 2019, December 3, 2019, February 26, 2020, March 14, 2020, and May 31, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20191108T231901_N0208_R001_T58LGN_20191109T003702 S2A_MSIL2A_20191108T231901_N0213_R001_T58LFN_20191109T010716 S2B_MSIL2A_20191203T231849_N0213_R001_T58LEP_20191204T010501 S2B_MSIL1C_20191203T231849_N0208_R001_T58LFP_20191204T003119 S2A_MSIL2A_20200226T231851_N0214_R001_T58LFP_20200227T012632 S2A_MSIL2A_20200314T230901_N0214_R101_T59LKG_20200315T010207 S2B_MSIL2A_20200531T231859_N0214_R001_T58LGQ_20200601T011048 SLB_2019-2020_ImagerySantaCruzIslands_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Solomon Islands EPSG:32757 CRS:84 165.1689203698276 168.9863408024967 -12.578773985323114 -9.61611668984721 text/plain slb:SLB_2019-2020_ImagerySantaIsabel_Sentinel2 This resource contains satellite imagery for Santa Isabel Island in the Solomon Islands. The imagery was collected on September 26, 2019, March 16, 2020, June 24, 2020, and October 2, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20190926T235739_N0213_R030_T57MUM_20190927T015140 S2A_MSIL2A_20200316T234741_N0214_R130_T57LVL_20200317T015924 S2A_MSIL2A_20200624T234751_N0214_R130_T57MVM_20200625T015109 S2A_MSIL2A_20201002T234751_N0214_R130_T57MWM_20201003T014939 S2A_MSIL2A_20201002T234751_N0214_R130_T57LVL_20201003T014939 S2A_MSIL2A_20201002T234751_N0214_R130_T57LWL_20201003T014939 SLB_2019-2020_ImagerySantaIsabel_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Solomon Islands EPSG:32757 CRS:84 157.82466830644253 159.99861889558196 -8.598920865141649 -7.232717457881782 text/plain slb:SLB_2019_DEM1arcsec_ASTERv3 Resource contains raster files for a Digital Elevation Model (DEM) for the Solomon Islands. The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. SLB_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF DEM Solomon Islands elevation ASTER EPSG:4326 CRS:84 154.999861111111 168.00013888888876 -12.000138888888811 -3.99986111111111 text/plain slb:SLB_2019_Hillshade1arcsec_ASTERv3 Resource contains raster files for a derived hillshade for the Solomon Islands. The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. SLB_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF hillshade ASTER Solomon Islands EPSG:4326 CRS:84 154.999861111111 168.00013888888876 -12.000138888888811 -3.9998611111110396 text/plain slb:SLB_2020_ImageryChoiseul_Sentinel2 This resource contains satellite imagery for the Choiseul Island in the Solomon Islands. The imagery was collected on October 25, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20201025T235751_N0214_R030_T57MTM_20201026T020108 S2A_MSIL2A_20201025T235751_N0214_R030_T57MTN_20201026T020108 S2A_MSIL2A_20201025T235751_N0214_R030_T57MUM_20201026T020108 S2A_MSIL2A_20201025T235751_N0214_R030_T57MUN_20201026T020108 SLB_2020_ImageryChoiseul_Sentinel2 WCS GeoTIFF satellite imagery Sentinel-2 Solomon Islands earth observation EPSG:32757 CRS:84 156.27429025110555 158.0022167616971 -7.830012720813741 -6.332592668117481 text/plain slb:SLB_2020_ImageryGuadalcanalCentralMalaita_Sentinel2 This resource contains satellite imagery for the Guadalcanal, Central, and Malaita Islands in the Solomon Islands. The imagery was collected on January 23, February 22, April 7, June 24, and October 2, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200123T233811_N0213_R087_T57LYK_20200124T013547 S2A_MSIL2A_20200123T233811_N0213_R087_T57LYL_20200124T013547 S2A_MSIL2A_20200123T233811_N0213_R087_T57LXL_20200124T013547 S2A_MSIL2A_20200222T233811_N0214_R087_T57MXM_20200223T013919 S2B_MSIL2A_20200407T233809_N0214_R087_T57LXK_20200408T014505 S2A_MSIL2A_20200624T234751_N0214_R130_T57MVM_20200625T015109 S2A_MSIL1C_20201002T234751_N0209_R130_T57LXL_20201003T012014 S2A_MSIL2A_20201002T234751_N0214_R130_T57LWL_20201003T014939 S2A_MSIL2A_20201002T234751_N0214_R130_T57LVL_20201003T014939 S2A_MSIL2A_20201002T234751_N0214_R130_T57MWM_20201003T014939 SLB_2020_ImageryGuadalcanalCentralMalaita_Sentinel2 WCS GeoTIFF earth observation satellite imagery Sentinel-2 Solomon Islands EPSG:32757 CRS:84 158.59098100013645 161.83605906089636 -10.089517516355887 -7.73161744370351 text/plain slb:SLB_2020_ImageryShorthandIslands_Sentinel2 This resource contains satellite imagery for the Shorthand Islands in the Solomon Islands. The imagery was collected on March 29, October 10, and October 15, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200329T235741_N0214_R030_T56MQT_20200330T015424 S2B_MSIL1C_20201010T235749_N0209_R030_T56MRS_20201011T011117 S2A_MSIL2A_20201015T235751_N0214_R030_T56MQS_20201016T015850 S2A_MSIL2A_20201015T235751_N0214_R030_T56MRT_20201016T015850 SLB_2020_ImageryShorthandIslands_Sentinel2 WCS GeoTIFF imagery satellite Sentinel-2 earth observation Solomon Islands EPSG:32757 CRS:84 155.37520966193375 156.37025551222231 -7.4677633091978155 -6.597778478690702 text/plain ton:TON_2018-2020_ImageryNiuaIslands_Sentinel2 This resource contains satellite imagery for the Niua Islands in Tonga. The imagery was collected on September 10, 2018 and April 22, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200422T215921_N0214_R086_T01LHC_20200423T012742 S2A_MSIL1C_20180910T215911_N0206_R086_T01LFC_20180911T005807 TON_2018-2020_ImageryNiuaIslands_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Tonga EPSG:32701 CRS:84 -175.76580960747003 -173.66261740035625 -16.14312116915406 -15.449209446505678 text/plain ton:TON_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. TON_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Tonga EPSG:4326 CRS:84 -177.00013888888898 -172.99986111111124 -23.00013888888877 -14.9998611111111 text/plain ton:TON_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. TON_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF ASTER Hillshade Tonga EPSG:4326 CRS:84 -177.00013888888898 -172.99986111111124 -23.00013888888877 -14.99986111111107 text/plain ton:TON_2020_ImageryHaapaiGroup_Sentinel2 This resource contains satellite imagery for the Haapia Group in Tonga. The imagery was collected on April 12, April 19, May 4, and May 27, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20200527T215919_N0214_R086_T01KFU_20200527T234510 S2B_MSIL2A_20200504T214859_N0214_R043_T01KGT_20200504T232933 S2A_MSIL2A_20200419T214911_N0214_R043_T01KGU_20200419T233911 S2A_MSIL2A_20200412T215911_N0214_R086_T01KFT_20200412T234540 TON_2020_ImageryHaapaiGroup_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Tonga EPSG:32701 CRS:84 -175.73667374592847 -174.06923730072396 -20.70423479386256 -19.436140051675387 text/plain ton:TON_2020_ImageryTongatapuGroup_Sentinel2 This resource contains satellite imagery for the Tongatapu Group in Tonga. The imagery was collected on May 23, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200522T215921_N0214_R086_T01KFS_20200523T015205 S2A_MSIL2A_20200522T215921_N0214_R086_T01KGS_20200523T015205 TON_2020_ImageryTongatapuGroup_Sentinel2 WCS GeoTIFF Sentinel-2 earth observation satellite imagery Tonga EPSG:32701 CRS:84 -175.62412691347544 -174.50445881543482 -21.620831197539747 -20.775762540714986 text/plain ton:TON_2020_ImageryVavauGroup_Sentinel2 This resource contains satellite imagery for the Vavau Group in Tonga. The imagery was collected from August 11, 2019, August 21, 2019, September 10, 2019, January 20, 2020, and February 19, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20190811T215919_N0213_R086_T01KGA_20190811T232418 S2B_MSIL2A_20190821T215919_N0213_R086_T01KGV_20190821T232737 S2B_MSIL2A_20190910T215919_N0213_R086_T01KGV_20190910T234012 S2A_MSIL2A_20200219T214901_N0214_R043_T01KGV_20200219T233738 S2A_MSIL2A_20200120T214901_N0213_R043_T01KHV_20200120T232609 TON_2020_ImageryVavauGroup_Sentinel2 WCS GeoTIFF Sentinel-2 earth observation satellite imagery Tonga EPSG:32701 CRS:84 -174.97785620448772 -173.59993522973463 -18.979937987051816 -17.920248858944024 text/plain tuv:TUV_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. TUV_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM Elevation Tuvalu EPSG:3832 CRS:84 -180.0 180.0 -9.000425057536336 4.000095118593202 text/plain tuv:TUV_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. TUV_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF ASTER Hillshade Tuvalu EPSG:3832 CRS:84 -180.0 180.0 -9.000425057536336 4.000095118593062 text/plain tuv:TUV_2020_ImageryNanumeaNanumangaNiutaoNiu_Sentinel2 This resource contains satellite imagery for Tuvalu. The imagery was collected on July 20, 2020, September 21, 2020, and October 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200921T223641_N0214_R115_T60MVU_20200922T002114 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAK_20200721T002156 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAJ_20200721T002156 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWU_20201012T002111 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWT_20201012T002111 S2A_MSIL2A_20200921T223641_N0214_R115_T60MUU_20200922T002114 TUV_2020_ImageryNanumeaNanumangaNiutaoNiu_Sentinel2 WCS GeoTIFF imagery Sentinel-2 satellite earth observation Tuvalu EPSG:32701 CRS:84 175.88076392017703 177.78101265330054 -7.342225387511912 -5.517350243560113 text/plain tuv:TUV_2020_ImageryNanumeaNanumanga_Sentinel2 This resource contains satellite imagery for Tuvalu. The imagery was collected on July 20, 2020, September 21, 2020, and October 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200921T223641_N0214_R115_T60MVU_20200922T002114 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAK_20200721T002156 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAJ_20200721T002156 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWU_20201012T002111 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWT_20201012T002111 S2A_MSIL2A_20200921T223641_N0214_R115_T60MUU_20200922T002114 TUV_2020_ImageryNanumeaNanumanga_Sentinel2 WCS GeoTIFF Sentinel-2 imagery satellite earth observation Tuvalu EPSG:32701 CRS:84 175.71364282593638 176.72196793794393 -6.426652380974976 -5.457216020493958 text/plain tuv:TUV_2020_ImageryNiutaoNiu_Sentinel2 This resource contains satellite imagery for Tuvalu. The imagery was collected on July 20, 2020, September 21, 2020, and October 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200921T223641_N0214_R115_T60MVU_20200922T002114 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAK_20200721T002156 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAJ_20200721T002156 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWU_20201012T002111 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWT_20201012T002111 S2A_MSIL2A_20200921T223641_N0214_R115_T60MUU_20200922T002114 TUV_2020_ImageryNiutaoNiu_Sentinel2 WCS GeoTIFF imagery Sentinel-2 satellite earth observation Tuvalu EPSG:32701 CRS:84 176.9717299700276 178.28389620157915 -7.315548804437126 -6.05614412757294 text/plain tuv:TUV_2020_ImageryNukulaelaeNiulakita_Sentinel2 This resource contains satellite imagery for Tuvalu. The imagery was collected on July 20, 2020, September 21, 2020, and October 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200921T223641_N0214_R115_T60MVU_20200922T002114 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAK_20200721T002156 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAJ_20200721T002156 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWU_20201012T002111 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWT_20201012T002111 S2A_MSIL2A_20200921T223641_N0214_R115_T60MUU_20200922T002114 TUV_2020_ImageryNukulaelaeNiulakita_Sentinel2 WCS GeoTIFF imagery earth observation Sentinel-2 Tuvalu EPSG:32701 CRS:84 -180.0 180.0 -10.960860491677549 -9.227853910899663 text/plain tuv:TUV_2020_ImageryVaitupaNukufetauFunafuti_Sentinel2 This resource contains satellite imagery for Tuvalu. The imagery was collected on July 20, 2020, September 21, 2020, and October 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200921T223641_N0214_R115_T60MVU_20200922T002114 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAK_20200721T002156 S2A_MSIL2A_20200720T222801_N0214_R072_T01LAJ_20200721T002156 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWU_20201012T002111 S2A_MSIL2A_20201011T223641_N0214_R115_T60MWT_20201012T002111 S2A_MSIL2A_20200921T223641_N0214_R115_T60MUU_20200922T002114 TUV_2020_ImageryVaitupaNukufetauFunafuti_Sentinel2 WCS GeoTIFF imagery earth observation Sentinel-2 satellite Tuvalu EPSG:32701 CRS:84 178.05650454784052 179.3743032734244 -8.692228728198662 -7.431442663679788 text/plain vut:VUT_2019-2020_ImageryShefaMalampa_Sentinel2 This resource contains satellite imagery for the Shefa and Malampa Provinces in Vanuatu. The imagery was collected on November 11, 2019, February 12, 2020, August 8, 2020, August 11, 2020, and September 10, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20191120T230909_N0208_R101_T58LHH_20191121T002614 S2A_MSIL1C_20200213T230901_N0209_R101_T58LGH_20200214T002904 S2A_MSIL2A_20200808T230031_N0214_R058_T58KHF_20200809T004621 S2A_MSIL2A_20200811T230911_N0214_R101_T58KHG_20200812T004745 S2A_MSIL2A_20200811T230911_N0214_R101_T58LGH_20200812T004745 S2A_MSIL2A_20200910T230911_N0214_R101_T58KGG_20200911T010839 S2A_MSIL2A_20200910T230911_N0214_R101_T58LHH_20200911T010839 VUT_2019-2020_ImageryShefaMalampa_Sentinel2 WCS GeoTIFF satellite imagery Sentinel-2 earth observation Vanuatu EPSG:4326 CRS:84 167.02453285169008 168.81868611452447 -18.00027192023139 -15.659352902628198 text/plain vut:VUT_2019-2020_ImageryTafea_Sentinel2 This resource contains satellite imagery for the Tafae Province in Vanuatu. The imagery was collected on August 20, 2019, March 26, 2020, August 9, 2020, and September 23, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL2A_20190819T230029_N0213_R058_T59KKV_20190820T004843 S2B_MSIL2A_20190819T230029_N0213_R058_T59KLV_20190820T004843 S2B_MSIL2A_20200525T230019_N0214_R058_T59KLU_20200526T004857 S2A_MSIL2A_20200808T230031_N0214_R058_T59KLT_20200809T004621 S2B_MSIL2A_20200922T230019_N0214_R058_T59KMU_20200923T002739 VUT_2019-2020_ImageryTafae_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Vanuatu EPSG:4326 CRS:84 168.84374099699508 170.37161840905247 -20.403001199186118 -18.42210237945864 text/plain vut:VUT_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. VUT_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER DEM elevation Vanuatu EPSG:4326 CRS:84 165.999861111111 171.00013888888876 -21.000138888888827 -12.9998611111111 text/plain vut:VUT_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. VUT_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF ASTER Hillshade Vanuatu EPSG:4326 CRS:84 165.999861111111 171.00013888888876 -21.000138888888827 -12.999861111111064 text/plain vut:VUT_2020_ImageryPenama_Sentinel2 This resource contains satellite imagery for the Penama Province in Vanuatu. The imagery was collected on February 13, March 8, September 9, September 10, and October 15, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200213T230901_N0209_R101_T58LGH_20200214T002904 S2B_MSIL1C_20200508T230909_N0209_R101_T58LGJ_20200509T002230 S2A_MSIL2A_20200910T230911_N0214_R101_T58LHJ_20200911T010839 S2A_MSIL2A_20200910T230911_N0214_R101_T58LHH_20200911T010839 S2B_MSIL2A_20201015T230909_N0214_R101_T58LHJ_20201016T005507 VUT_2020_ImageryPenama_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Vanuatu EPSG:4326 CRS:84 167.3661221833348 168.69580404257442 -16.122519602706816 -14.77602784496261 text/plain vut:VUT_2020_ImagerySanma_Sentinel2 This resource contains satellite imagery for the Sanma Province in Vanuatu. The imagery was collected on February 13, March 8, and August 11, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL1C_20200213T230901_N0209_R101_T58LGH_20200214T002904 S2A_MSIL2A_20200213T230901_N0214_R101_T58LGJ_20200214T010704 S2A_MSIL1C_20200213T230901_N0209_R101_T58LFH_20200214T002904 S2A_MSIL1C_20200213T230901_N0209_R101_T58LGH_20200214T002904 S2B_MSIL1C_20200508T230909_N0209_R101_T58LFJ_20200509T002230 S2A_MSIL2A_20200811T230911_N0214_R101_T58LGH_20200812T010713 VUT_2020_ImagerySanma_Sentinel2 WCS GeoTIFF satellite imagery Sentinel-2 earth observation Vanuatu EPSG:4326 CRS:84 166.38854501281452 167.53434419901643 -15.909645654551028 -14.488505987483917 text/plain vut:VUT_2020_ImageryTorba_Sentinel2 This resource contains satellite imagery for the Torba Province in Vanuatu. The imagery was collected on March 9 and March 22, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2B_MSIL1C_20200508T230909_N0209_R101_T58LGK_20200509T002230 S2B_MSIL1C_20200508T230909_N0209_R101_T58LGL_20200509T002230 S2B_MSIL2A_20200508T230909_N0214_R101_T58LHK_20200509T005531 S2B_MSIL1C_20200521T231859_N0209_R001_T58LFL_20200522T002935 VUT_2020_ImageryTorba_Sentinel2 WCS GeoTIFF Sentinel-2 satellite imagery earth observation Vanuatu EPSG:4326 CRS:84 166.31896950296337 168.1312126572804 -14.645794171858123 -12.938737093398082 text/plain wsm:WSM_2018_Imagery_Sentinel2 This resource contains satellite imagery for the Independent State of Samoa. The imagery was collected on March 11, 2018, August 8, 2018, October 12, 2019, and June 18, 2020. More specially, this resource contains a raster file of RGB imagery at 10-meter resolution, using Level-2A products when available. Level-2A products include atmospheric correction and represent bottom of atmosphere reflectance values in the images. When Level-2A products were not available, Level-1C (top of atmosphere) products were used. The image was mosaicked from the following individual Sentinel-2 scenes: S2A_MSIL2A_20200618T214911_N0214_R043_T02LMK_20200618T234243 S2A_MSIL1C_20180311T214901_N0206_R043_T02LLK_20180311T231354 S2A_MSIL1C_20180311T214901_N0206_R043_T02LLL_20180311T231354 S2A_MSIL2A_20191012T214911_N0213_R043_T02LMK_20191012T232855 S2A_MSIL1C_20180808T214901_N0206_R043_T02LLK_20180809T005556 S2A_MSIL1C_20180808T214901_N0206_R043_T02LLL_20180809T005556 WSM_2018_Imagery_Sentinel2 WCS GeoTIFF satellite imagery Sentinel-2 earth observation Samoa EPSG:32701 CRS:84 -172.86358446738822 -171.33708116963206 -14.138220484819813 -13.347321491590696 text/plain wsm:WSM_2019_DEM1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. WSM_2019_DEM1arcsec_ASTERv3 WCS GeoTIFF ASTER elevation DEM Samoa EPSG:4326 CRS:84 -173.000138888889 -170.99986111111124 -15.00013888888887 -12.9998611111111 text/plain wsm:WSM_2019_Hillshade1arcsec_ASTERv3 The ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan’s Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire ASTER Level 1A archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1° by 1° tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database (ASTWBD) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in GeoTIFF format and projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. WSM_2019_Hillshade1arcsec_ASTERv3 WCS GeoTIFF ASTER Hillshade Samoa EPSG:4326 CRS:84 -173.000138888889 -170.99986111111124 -15.000138888888868 -12.9998611111111 text/plain pipap:World_Database_Protected_Areas_Points The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. features wdpa_points protected areas BIOPAMA WDPA Protected Planet EPSG:4326 CRS:84 -179.967010498047 179.967010498047 -22.5170001983643 14.6300001144409 text/plain text/plain pipap:World_Database_Protected_Areas_Polygons The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. features wdpa_polygons protected areas Protected Planet BIOPAMA WDPA EPSG:4326 CRS:84 -180.0 180.0 -26.201000213623 23.8930015563965 text/plain text/plain