Global biodiversity data

Seagrass beds are vital coastal ecosystems that provide habitat and nursery grounds for a diverse array of marine life, supporting rich biodiversity. They also play a crucial role in stabilizing sediments, improving water quality, and capturing carbon, making them essential for healthy oceans and climate regulation. This layer shows the extent of shallow-water seagrass beds derived from Sentinel-2 imagery from 2023-2024, based on analysis by Peng et al (Nature 2026).    Dataset Summary Phenomenon Mapped: Shallow-water seagrass extent Geographic Extent: Global Layer Projection: WGS 1984 Web Mercator Cell Size: 10-meters Pixel Depth: 1-bit integer Update Frequency: None Source: Peng et al 2026 Data Vintage: June 2026 Publication Date: July 2026 Analysis: Supports analysis What can you do with this layer? This tile imagery service is designed for fast data visualization and analysis. Identify pixels by clicking on the map to reveal the pre-configured pop-ups. It can be used to model areas suitable for seagrass habitat. An additional 2019-2020 analysis is available from the source.  Citation: Peng, J. (2026). Global 10-m shallow-water seagrass extent mapping code. Zenodo. https://doi.org/10.5281/zenodo.18212209 Revisions None Questions? Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Overview Carotenoid Content Index (Car) from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). Car describes the canopy content of carotenoids in terrestrial vegetation. Car is calculated as ((1/R495)-(1/R705))*R800, where R495, R705, and R800 are the surface reflectance at 495, 705 and 800 nanometers (nm), respectively (Gitelson et al. 2002). Data provided as global, 8-day averages mapped to a standard, equal-angle, latitude-longitude grid of 0.02 degrees, which equates to ~2 kilometers (km) at the equator. Data has been corrected for atmospheric scattering and absorption. Pixels with no data values are masked (excluded) due to presence of water, cloud cover, or other features marked by quality control flags as described below. Data Layer Summary Name, units Carotenoid Content Index (Car) Measure of terrestrial canopy carotenoid content, a phytosynthetic and photoprotective pigment. Car is unitless. Spatial resolution 2 km2 Level-3, mapped to standard grid, global coverage Time Resolution 8 day average Each pixel represents an average of the valid, daily values observed within that grid element during the 8-day interval (approx 1 week). Note: 8 day average data layer is calculated from daily, "refined" (archive-quality) Level 3 PACE Car products available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Data Version Version 3.1 Data versions change as improved calibration, validation, algorithm refinements, etc are applied over time. See Version 3.1 Release Notes for details. Latency ~1 month This imagery service provides “refined” (archive-quality) PACE data products from March 2024-present, which have an approx 1 month latency. Note: Lower latency “near real time (NRT)” Level 3 PACE Car products available daily from NASA Earthdata at this link in NetCDF format. Latency typically Earthdataaccount required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Intended Use: What can I do with this layer? There are three major classes of plant pigments: chlorophylls, carotenoids, and anthocyanins. Car provides information on changes in vegetation carotenoid content and photophysiological status. Carotenoids are structural components of the photosynthetic antenna and reaction center complexes and serve both photosynthetic and photoprotective functions (Blackburn 2007). The time (8 day average) and space (2 km2) resolution of this data layer enables weekly to seasonal and annual insight at local to global scales.   Limitations of Use: What should I know about the data? Data Status (Maturity): This product is provisional, which means that product results have been reviewed and are in family with heritage data products or other basis of expectation, but have not yet been extensively validated and so may still contain significant errors. Data layer is sufficient for qualitative determinations of product fitness-for-purpose, for testing by operational users, and may be suitable for scientific publication. Data Masking: This is a Level 3, globally-binned product from NASA’s Ocean Biology Processing Group (OBPG). By default, OBPG Level 3 data values are masked (value is set to null, or no data) according to a standard set of quality control parameters. Please see Level 3 Algorithm Usage Constraints for masking parameter details. Algorithm Theoretical Basis Document (ATBD): See ATBD for algorithm details including scientific theory, calculations, etc. https://doi.org/10.5067/8L77O0Y8GHV8.   Where do I go with further questions? Visit NASA’s Earthdata Forum where users can post questions that are answered by the personnel working at NASA with the PACE mission and/or the Ocean Biology Distributed Active Archive Center (OB.DAAC) that processes and distributes PACE data. First, follow this link to view only “PACE” and “OBDAAC” tagged questions on the Earthdata Forum. Then search and read through previous posts for your question. If it has not yet been asked please post your question in the Forum and be sure "PACE" and "OBDAAC" filter tags are applied.     Mission and Product Background PACE Mission Overview: Launched in February 2024, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory uniquely provides near-daily, hyperspectral coverage of Earth’s surface, plus hyperspectral and hyperangular polarimetry. PACE is a comprehensive Earth System mission, delivering all new, space-based information on the health and diversity of aquatic and terrestrial life (algae, plants), aerosol properties & modes (e.g. smoke, dust), cloud types, and more.  The Ocean Color Instrument (OCI), PACE's primary sensor, is a hyperspectral (5 nm resolution) imaging spectrometer that measures properties of light from the ultraviolet (UV, 340 nm) through visible and near infrared (NIR, 895 nm) portions of the electromagnetic spectrum in 1.25 to 2.5 nm steps, plus 7 shortwave infrared (SWIR) bands (Werdell et al. 2019). Observations from OCI are moderate spatial resolution (1.2 km2), enabling a relatively frequent revisit time of 1-2 days. This combination of continuous, global coverage and spectral capabilities is unmatched by any other satellite platform, public or private. PACE benefits society by expanding our foundational knowledge of Earth and enabling novel, space-based science and applications tools across water resources (e.g. fisheries), air quality, land monitoring & management, and more.   Terrestrial Remote Sensing from an Ocean Color Sensor? Designed and optimized to operate across a broad dynamic range, from the dark target of the ocean to bright, highly reflective cloud tops, PACE’s OCI also collects surface reflectance data from the world's terrestrial environments across 122 bands, resolving spectral properties of the Earth more finely than previous multispectral missions could achieve. Work thus far has demonstrated viability of OCI’s land data products for terrestrial investigations (Caplan et al. 2025). OCI’s coverage and capabilities enable provision of both novel, hyperspectral-enabled land observations as well as maintenance and extension of certain historical multispectral data records. Advanced vegetation indexes (VIs) enabled by OCI’s narrow-wavelength surface reflectance measurements provide continuous, synoptic insights into foliar pigment amounts (chlorophylls, carotenoids, and anthocyanins), water content, and photosynthetic light use efficiency across the globe each day. Heritage VIs (e.g., normalized difference vegetation index, NDVI; reviewed in Huang et al. 2021) can also be calculated by aggregating (averaging) OCI’s narrow bandwidths across ranges that approximate the multi-spectral, broadband reflectances of instruments such as MODIS, see the Mathematical Theory section of the ATBD for details. See the full list of heritage and hyperspectral-enabled VIs produced by PACE here.   What other PACE data layers are available? This data layer is 1 of 11 PACE products initially selected for the Living Atlas catalog from the mission’s comprehensive aquatic, atmospheric, and terrestrial product catalog. Click here to view all 11 PACE Living Atlas imagery services. The Level 3, 122-band hyperspectral surface reflectance data (SFREFL) used to derive all VIs is available from NASA Earthdata to download at 2 kilometer resolution in NetCDF format. To access PACE mission data products available from NASA, first learn about PACE data access here, next decide which PACE product(s) you would like to use, then go to NASA Earthdata to find PACE products in NetCDF format, which can be downloaded and then imported into ArcGIS. An Earthdata account is needed to download data from NASA, sign up here. To learn about using Earthdata Search to find data, watch this tutorial.   How can I stay up to date with the PACE mission?   Join the PACE Community for mission news, events, new product release announcements, and to receive our newsletters. Would you like to see imagery services for more PACE products? Please let us know here.   References: Algorithm Theoretical Basis Document: https://doi.org/10.5067/8L77O0Y8GHV8 Blackburn, G. A. (2007) Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58(4), 855-867. https://doi.org/10.1093/jxb/erl123 Caplan, S. and Huemmrich, K. F. (2025) Unveiling PACE OCI’s hyperspectral terrestrial data products. Remote Sensing Letters, 16(4), 422–433. https://doi.org/10.1080/2150704X.2025.2470905 Gitelson, A. A., Zur, Y., Chivkunova, O. B., and Merzlyak, M. N.(2002) Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75(3): 272–281. https://doi.org/10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2 Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1 Werdell, P. J. et al. (2019) The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances. Bulletin of the American Meteorological Society, 100, 1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1

NOTICE: This is a beta service without operational support. It will be expanded in a future release to cover the historical and ongoing temporal record of ATL08. This map service is a GIS-ready version of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 (ATL08) data set. ATL08 provides along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters, acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory. The complete ATL08 source data formatted as HDF5 files are available from the NASA NSIDC DAAC, along with user documentation and resources.  This beta map service displays ATL08 data as a series of georeferenced points arranged in linear "ground tracks." These ground tracks represent the 100 m land segments output from the algorithms applied to the photon return data collected by the ATLAS instrument as ICESat-2 orbits the Earth over its 91-day repeat cycle. As shown in the figure below, the ATLAS instrument measures data along six ground tracks (GT) arranged in 3 pairs (one "weak" beam and one "strong" beam in each pair). Each GT is numbered according to the relative location of the laser that generates it, along with L (left) or R (right). GT1L and GT1R form pair one, GT2L and GT2R form pair two, and GT3L and GT3R form pair three, with GT1L on the far left and GT3R on the far right relative to the satellite flight direction. Left/right beams within each pair are approximately 90 m apart with the pairs separated by 3.3 km in the across-track direction. The along-track laser spacing is ~70 cm. Algorithms produce parameters representing 100 m land segments in the along-track direction for each beam. Image source: Smith et al., 2019 - https://doi.org/10.1016/j.rse.2019.111352 Additional information about the ATLAS instrument, ICESat-2 satellite, and ground track naming conventions and geometry can be found in Appendix A of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 User Guide. Available Parameters A selection of "core" parameters was chosen from the full suite of parameters available in the complete ATL08 source data. A list of the available core parameters, including canopy and terrain height, can be found in the technical specification document. When accessing the data through the web-based Map Viewer, these parameters are visible as a popup window when a single data point is selected. Note: where canopy data is absent, these parameters will not have values. Spatial Coverages and Resolution Spatial coverage is nearly global (approximately 88°N to 88°S). However, data are not produced for orbital segments that cross open ocean only (i.e., do not cross a land surface). The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons but may in some cases contain less. Temporal Coverage and Resolution  9 December 2025 to 11 March 2026 Data are re-acquired along each of ATLAS/ICESat-2's 1,387 reference ground track every 91 days. This beta service presents a single 91-day satellite repeat cycle. Recommended Data Access Guidelines If you would like to access these data using ArcGIS Pro or QGIS, we recommend using the Feature Service, rather than the Map Service. When accessing the Map Service data using the web-based Map Viewer, we suggest the following settings be applied:Time Slider: Set temporal range to the full 91-day extent Visibility Range: Within the map properties window, ensure the current map scale (black triangle) is within the selected visibility range Recommended settings when using Map Viewer.  For in-depth data users who wish to perform customized data processing, we suggest using the full ATL08 source data, available from the NASA NSIDC DAAC as ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7.

NOTICE: This is a beta service without operational support. It will be expanded in a future release to cover the historical and ongoing temporal record of ATL08. This map service is a GIS-ready version of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 (ATL08) data set. ATL08 provides along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters, acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory. The complete ATL08 source data formatted as HDF5 files are available from the NASA NSIDC DAAC, along with user documentation and resources.  This beta map service displays ATL08 data as a series of georeferenced points arranged in linear "ground tracks." These ground tracks represent the 100 m land segments output from the algorithms applied to the photon return data collected by the ATLAS instrument as ICESat-2 orbits the Earth over its 91-day repeat cycle. As shown in the figure below, the ATLAS instrument measures data along six ground tracks (GT) arranged in 3 pairs (one "weak" beam and one "strong" beam in each pair). Each GT is numbered according to the relative location of the laser that generates it, along with L (left) or R (right). GT1L and GT1R form pair one, GT2L and GT2R form pair two, and GT3L and GT3R form pair three, with GT1L on the far left and GT3R on the far right relative to the satellite flight direction. Left/right beams within each pair are approximately 90 m apart with the pairs separated by 3.3 km in the across-track direction. The along-track laser spacing is ~70 cm. Algorithms produce parameters representing 100 m land segments in the along-track direction for each beam. Image source: Smith et al., 2019 - https://doi.org/10.1016/j.rse.2019.111352 Additional information about the ATLAS instrument, ICESat-2 satellite, and ground track naming conventions and geometry can be found in Appendix A of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 User Guide. Available Parameters A selection of "core" parameters was chosen from the full suite of parameters available in the complete ATL08 source data. A list of the available core parameters, including canopy and terrain height, can be found in the technical specification document. When accessing the data through the web-based Map Viewer, these parameters are visible as a popup window when a single data point is selected. Note: where canopy data is absent, these parameters will not have values. Spatial Coverages and Resolution Spatial coverage is nearly global (approximately 88°N to 88°S). However, data are not produced for orbital segments that cross open ocean only (i.e., do not cross a land surface). The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons but may in some cases contain less. Temporal Coverage and Resolution  9 December 2025 to 11 March 2026 Data are re-acquired along each of ATLAS/ICESat-2's 1,387 reference ground track every 91 days. This beta service presents a single 91-day satellite repeat cycle. Recommended Data Access Guidelines This Feature Service is best accessed using ArcGIS Pro or QGIS, and may be slow to load if viewed using the web-based Map Viewer. If you wish to access the data using the Map Viewer, we recommend using the Map Service instead. When using Map Viewer, we suggest the following settings be applied:Time Slider: Set temporal range to the full 91-day extent Visibility Range: Within the map properties window, ensure the current map scale (black triangle) is within the selected visibility range Recommended settings when using Map Viewer.  For in-depth data users who wish to perform customized data processing, we suggest using the full ATL08 source data, available from the NASA NSIDC DAAC as ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7.

The identification of species is one of the most basic, and yet critically important, issues in biology with far-reaching potential implications for fields such as biodiversity conservation, population ecology and epidemiology. Morphology has long been the primary tool biologists have used to categorize life. However, we now know that a significant portion of natural diversity is morphologically hidden, and therefore, we must integrate nonmorphological tools into the description of biodiversity. Here, we demonstrate the utility of multilocus population genetic data for identifying and characterizing cryptic species complexes, even when species share large amounts of genetic variability. Specifically, we have used DNA sequence data from 12 genomic regions to characterize two widespread species complexes in the coral genus Acropora: A. cytherea and A. hyacinthus. These two morphospecies have each been sampled from 5 to 7 locations throughout their Indo-Pacific distributions, and with the use of structure and hierarchical clustering, we demonstrate the presence of at least six widespread cryptic species within these two morphospecies complexes. After identifying cryptic lineages, we then utilize the genetic data to examine the history of introgressive hybridization within and between these morphospecies complexes. Our data indicate that these two complexes form a global syngameon with consistent patterns of introgression between species across large geographic distributions. Citation:  Southwestern Pacific OBIS (2015). Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 20 records. Online:  http://nzobisipt.niwa.co.nz/resource.do?r=smpatry_of_coral Released on April 7, 2015. Bibliography:  LADNER, J. T. and PALUMBI, S. R. (2012), Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Molecular Ecology, 21: 2224–2238. doi: 10.1111/j.1365-294X.2012.05528.x_________________________________________________________________________________________________________________ Item Page Created: 2016-06-09 02:30 Item Page Last Modified: 2025-04-05 18:55 Owner: NIWA_OpenData Sympatry_of_two_coral_species_complexes No data edit dates available Fields: id,modified,language,bibliographicCitation,institutionCode,collectionCode,datasetName,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,eventID,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

This image service provides one-time estimates of 30-meter resolution global mangrove distribution, aboveground biomass, and canopy height that represent conditions of mangrove-forested wetlands in the 2000s based on remotely sensed data and in situ field measurements and region-specific allometric models.   The underlying multidimensional image layer contains three variables. Users should use the multidimensional settings to switch between variables.agb (Mg ha-1): mangrove aboveground biomass measured in megagrams per hectare hmax (meters): maximum canopy maximum height (height of the tallest tree) measured in meters hba (meters): basal-area weighted height (individual tree heights weighted in proportion to their basal area).  The maximum canopy height and basal-area weighted height values were capped at the 95th percentile of the maximum value in each country to minimize the impact of canopy height error reported from a potentially small number of misclassified pixels. Data sources used include the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data collected in February 2000, lidar heights collected from 2003 to 2009 by the ICESat/Geoscience Laser Altimeter System (GLAS) Spaceborne Lidar mission, and in situ field measurements collected from 2004 to 2014. Details about the underlying data can be found at https://doi.org/10.3334/ORNLDAAC/1665  Legend:Variable Units Symbol  Pixel Value  Description  agb Mg ha-1  High: 910.476 Mangrove aboveground biomass Low: 0.284 hmax meters   High: 62.789 Mangrove canopy maximum height (height of the tallest tree) Low: 0.849 hba meters   High: 39.79 Mangrove basal-area weighted height (individual tree heights weighted in proportion to their basal area) Low: 0.538  Acknowledgement:Creation of this dataset was funded by NASA"s Carbon Monitoring System (CMS) program.  Dataset Citation:Simard, M., T. Fatoyinbo, C. Smetanka, V.H. Rivera-monroy, E. Castaneda, N. Thomas, and T. Van der stocken. 2019. Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1665 Contact:For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum.

Overview Chlorophyll Index Red Edge (CIRE) product from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). CIRE describes the canopy content of chlorophyll in terrestrial vegetation. CIRE is calculated as (R800/R705)-1, where R800 is the surface reflectance at 800 nanometers (nm) and R705 is the reflectance at 705 nm (Gitelson et al. 2005). Data provided as global, 8-day averages mapped to a standard, equal-angle, latitude-longitude grid of 0.02 degrees, which equates to ~2 kilometers (km) at the equator. Data has been corrected for atmospheric scattering and absorption. Pixels with no data values are masked (excluded) due to presence of water, cloud cover, or other features marked by quality control flags as described below. Data Layer Summary Name, units Chlorophyll Red Edge Index (CIRE) Measure of canopy chlorophyll content using red edge (near infrared) surface reflectance measurements, describes plant health and abundance. CIRE is unitless. Spatial resolution 2 km2 Level-3, mapped to a standard grid, global coverage Time Resolution 8 day average Each pixel represents an average of the valid, daily values observed within that grid element during the 8-day interval (approx 1 week). Note: 8 day average data layer is calculated from daily, "refined" (archive-quality) Level 3 PACE CIRE products available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including CIRE. Data Version Version 3.1 Data versions change as improved calibration, validation, algorithm refinements, etc are applied over time. See Version 3.1 Release Notes for details. Latency ~1 month This imagery service provides “refined” (archive-quality) PACE data products from March 2024-present, which have an approx 1 month latency. Note: Daily, “near real time (NRT)” Level 3 PACE CIRE products with less than approximately 24-48 hour latency available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including CIRE. Intended Use: What can I do with this layer? There are three major classes of plant pigments: chlorophylls, carotenoids, and anthocyanins. The Chlorophyll Index Red Edge (CIRE) is related to vegetation chlorophyll content, a proxy of plant abundance. Chlorophyll content controls potential photosynthesis and provides an indirect estimate of plant nutrient status (Ustin et al. 2009). The time (8 day average) and space (2km2) resolution of this data layer enables weekly to seasonal and annual insight at local to global scales.   Limitations of Use: What should I know about the data? Data Status (Maturity): This product is provisional, which means that product results have been reviewed and are in family with heritage data products or other basis of expectation, but have not yet been extensively validated and so may still contain significant errors. Data layer is sufficient for qualitative determinations of product fitness-for-purpose, for testing by operational users, and may be suitable for scientific publication. Data Masking: This is a Level 3, globally-binned product from NASA’s Ocean Biology Processing Group (OBPG). By default, OBPG Level 3 data values are masked (value is set to null, or no data) according to a standard set of quality control parameters. Please see Level 3 Algorithm Usage Constraints for masking parameter details. Algorithm Theoretical Basis Document (ATBD): See ATBD for algorithm details including scientific theory, calculations, etc. https://doi.org/10.5067/8L77O0Y8GHV8.   Where do I go with further questions? Visit NASA’s Earthdata Forum where users can post questions that are answered by the personnel working at NASA with the PACE mission and/or the Ocean Biology Distributed Active Archive Center (OB.DAAC) that processes and distributes PACE data. First, follow this link to view only “PACE” and “OBDAAC” tagged questions on the Earthdata Forum. Then search and read through previous posts for your question. If it has not yet been asked please post your question in the Forum and be sure "PACE" and "OBDAAC" filter tags are applied.     Mission and Product Background PACE Mission Overview: Launched in February 2024, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory uniquely provides near-daily, hyperspectral coverage of Earth’s surface, plus hyperspectral and hyperangular polarimetry. PACE is a comprehensive Earth System mission, delivering all new, space-based information on the health and diversity of aquatic and terrestrial life (algae, plants), aerosol properties & modes (e.g. smoke, dust), cloud types, and more. The Ocean Color Instrument (OCI), PACE's primary sensor, is a hyperspectral (5 nm resolution) imaging spectrometer that measures properties of light from the ultraviolet (UV, 340 nm) through visible and near infrared (NIR, 895 nm) portions of the electromagnetic spectrum in 1.25 to 2.5 nm steps, plus 7 shortwave infrared (SWIR) bands (Werdell et al. 2019). Observations from OCI are moderate spatial resolution (1.2 km2), enabling a relatively frequent revisit time of 1-2 days. This combination of continuous, global coverage and spectral capabilities is unmatched by any other satellite platform, public or private. PACE benefits society by expanding our foundational knowledge of Earth and enabling novel, space-based science and applications tools across water resources (e.g. fisheries), air quality, land monitoring & management, and more.   Terrestrial Remote Sensing from an Ocean Color Sensor? Designed and optimized to operate across a broad dynamic range, from the dark target of the ocean to bright, highly reflective cloud tops, PACE’s OCI also collects surface reflectance data from the world's terrestrial environments across 122 bands, resolving spectral properties of the Earth more finely than previous multispectral missions could achieve. Work thus far has demonstrated viability of OCI’s land data products for terrestrial investigations (Caplan et al. 2025). OCI’s coverage and capabilities enable provision of both novel, hyperspectral-enabled land observations as well as maintenance and extension of certain historical multispectral data records. Advanced vegetation indexes (VIs) enabled by OCI’s narrow-wavelength surface reflectance measurements provide continuous, synoptic insights into foliar pigment amounts (chlorophylls, carotenoids, and anthocyanins), water content, and photosynthetic light use efficiency across the globe each day. Heritage VIs (e.g., normalized difference vegetation index, NDVI; reviewed in Huang et al. 2021) can also be calculated by aggregating (averaging) OCI’s narrow bandwidths across ranges that approximate the multi-spectral, broadband reflectances of instruments such as MODIS, see the Mathematical Theory section of the ATBD for details. See the full list of heritage and hyperspectral-enabled VIs produced by PACE here.   What other PACE data layers are available? This data layer is 1 of 11 PACE products initially selected for the Living Atlas catalog from the mission’s comprehensive aquatic, atmospheric, and terrestrial product catalog. Click here to view all 11 PACE Living Atlas imagery services. The Level 3, 122-band hyperspectral surface reflectance data (SFREFL) used to derive all VIs is available from NASA Earthdata to download at 2 kilometer resolution in NetCDF format. To access PACE mission data products available from NASA, first learn about PACE data access here, next decide which PACE product(s) you would like to use, then go to NASA Earthdata to find PACE products in NetCDF format, which can be downloaded and then imported into ArcGIS. An Earthdata account is needed to download data from NASA, sign up here. To learn about using Earthdata Search to find data, watch this tutorial.   How can I stay up to date with the PACE mission?   Join the PACE Community for mission news, events, new product release announcements, and to receive our newsletters. Would you like to see imagery services for more PACE products? Please let us know here.   References: Algorithm Theoretical Basis Document: https://doi.org/10.5067/8L77O0Y8GHV8 Caplan, S. and Huemmrich, K. F. (2025) Unveiling PACE OCI’s hyperspectral terrestrial data products. Remote Sensing Letters, 16(4), 422–433. https://doi.org/10.1080/2150704X.2025.2470905 Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. (2005) Remote Estimation of Canopy Chlorophyll Content in Crops. Geophysical Research Letters, 32(8):2005GL022688. https://doi.org/10.1029/2005GL022688 Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1 Ustin, S.L., Gitelson, A.A., Jacquemoud, S., Schaepman, M., Asner, G.P., Gamon, J.A., and Zarco-Tejada, P. (2009) Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113(1), S67-S77. https://doi.org/10.1016/j.rse.2008.10.019 Werdell, P. J. et al. (2019) The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances. Bulletin of the American Meteorological Society, 100, 1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1

Mangroves are a group of tree and bush species that thrive in coastal intertidal zones, or the area between high and low tide. Because they prefer warm-water ecosystems, they are most commonly found at tropical and subtropical latitudes around the world. They are halophytes, or salt tolerant, and adapted to live in low-oxygen saline or brackish water. They are easily recognizable due to their tangle of prop roots, which keep their leafy branches above the water as the cycle of tides changes the water level.   Though more than 50—and possibly up to 110—species are considered mangroves, not all are from the genus Rhizophora. Some mangrove species are the result of convergent evolution, when unrelated species adapt to a harsh environment (such as a high-salt, low-oxygen intertidal zone) by taking on similar characteristics (such as a tangled prop root system). For this reason, extensive mangrove forests may contain only a handful of unique mangrove species—and even within a single forest, different types of mangroves each occupy distinct niches.   Mangroves are crucial to the health of coastal ecosystems. Their root systems protect the shoreline against erosion by reducing wave intensity and trapping sediments against the land. Mangrove forests are also effective at carbon sequestration, the process of removing carbon dioxide (a harmful greenhouse gas) from the atmosphere. Their labyrinth of roots is a popular hiding spot for fish and other marine life evading predators. They also shield coastal communities from tsunamis and storm surge.   Today, the largest mangrove forest in the world is the Sundarbans forest along the coast of Bangladesh and India, spanning 10,000 square kilometers (almost 4,000 square miles). In the past 50 years, though, it is estimated that up to 35 percent of the world's mangrove forests have been lost. This is largely due to shrimp farming, in which mangrove forests are cleared and replaced with artificial ponds for aquaculture practices, as well as other growing threats: unsustainable tourism activities; agricultural practices that cause mangrove removal or harmful runoff and pollution; and mangrove deforestation for coastal development or to gather charcoal and timber. These risks are exacerbated by climate change causing sea levels to rise, effectively drowning mangrove forests, and alterations to water chemistry, temperature, and other conditions, which put stress on the mangroves’ preferred growing conditions.   As threats to mangroves become better understood, organizations around the world have been created and joined together to protect these important ecosystems. The data in this map layer is from the Global Mangrove Alliance, a collaboration between Conservation International, The International Union for the Conservation of Nature, The Nature Conservancy, Wetlands International, and the World Wildlife Fund. Supported by grants from the National Geographic Society, Explorers Ben Somerville and Margaret Owuor use research, education, and storytelling to protect mangrove forests near their homes in the Caribbean and southeastern Kenya. The map layer shows mangrove forest extent around the world in 2020. About 40 percent of people live near the coast, and 90 percent of tropical storms form within 20 degrees of the Equator—the parts of the globe where mangrove forests flourish. You can use the Population Density and Longitudes and Latitudes layers to see how many people and mangroves live in these regions, and the Protected Areas layer to find the areas where special conservation plans exist to defend mangrove ecosystems. Data Citation: Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, T.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L-M. Global Mangrove Extent Change 1996 – 2020: Global Mangrove Watch Version 3.0. Remote Sensing. 2022 doi: 10.5281/zenodo.6894272

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

The Global Forest Resources Assessment (FRA) of the Food and Agriculture Organization of the United Nations (FAO) presents global and regional forest data by global ecological zone (GEZ). The GEZ spatial dataset used by FAO has developed over the years from covering only the tropical areas (1990) to the globe (2000). Due to the developments in remote sensing and the compiling of many spatial products relating to climate and land cover between 2000 and 2010, an update to the map was commissioned and resulted in the available GEZ 2010 product **Data creation:** 2025-12-31 **Contact points:** Metadata Contact: [Remi D'Annunzio](mailto:fra@fao.org) **Data lineage:** Data quality information not available **Resource constraints:** copyright **Online resources:** Download Global Ecological Zones for FAO Forest Reporting: 2010 update Download Ecozones 2010 (ESRI Shapefile)

Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020. Maps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the PREDICTS database of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future. Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled “Global 100m Projections of Biodiversity Intactness for the years 2017-2020.” All years are available under a Creative Commons BY-4.0 license.

Mangroves are a group of tree and bush species that thrive in coastal intertidal zones, or the area between high and low tide. Because they prefer warm-water ecosystems, they are most commonly found at tropical and subtropical latitudes around the world. They are halophytes, or salt-tolerant, and adapted to live in low-oxygen saline or brackish water. They are easily recognizable due to their tangle of prop roots, which keep their leafy branches above the water as the cycle of tides changes the water level.  Though over 50—and possibly up to 110—species are considered mangroves, not all are from the genus Rhizophora. Some mangrove species are the result of convergent evolution, when unrelated species adapt to a harsh environment (like a high-salt, low-oxygen intertidal zone) by taking on similar characteristics (like a tangled prop root system). For this reason, extensive mangrove forests may contain only a handful of unique mangrove species—and even within a single forest, different types of mangroves each occupy distinct niches.  Mangroves are crucial to the health of coastal ecosystems. Their root systems protect the shoreline against erosion by reducing wave intensity and trapping sediments against the land. Mangrove forests are also effective at carbon sequestration, the process of removing carbon dioxide (a harmful greenhouse gas) from the atmosphere. Their labyrinth of roots is a popular hiding spot for fish and other marine life evading predators. They also shield coastal communities from tsunamis and storm surge.  Today, the largest mangrove forest in the world is the Sundarban forest along the coast of Bangladesh and India, spanning 10,000 square kilometers (almost 4,000 square miles). In the past fifty years, though, it is estimated that up to 35 percent of the world's mangrove forests have been lost. This is largely due to shrimp farming, where mangrove forests are cleared and replaced with artificial ponds for aquaculture practices, as well as other growing threats: unsustainable tourism activities; agricultural practices that cause mangrove removal or harmful runoff and pollution; and mangrove deforestation for coastal development or to gather charcoal and timber. These risks are exacerbated by climate change causing sea levels to rise, effectively drowning mangrove forests, and alterations to water chemistry, temperature, and other conditions, which put stress on the mangroves’ preferred growing conditions. As threats to mangroves become better understood, organizations around the world have been created and joined together to protect these important ecosystems. The data in this map layer is from the Global Mangrove Alliance, a collaboration between Conservation International, The International Union for the Conservation of Nature, The Nature Conservancy, Wetlands International, and the World Wildlife Fund. Supported by grants from the National Geographic Society, Explorers Ben Somerville and Margaret Owuor use research, education, and storytelling to protect mangrove forests near their homes in the Caribbean and southeastern Kenya. The map layer shows mangrove forest extent around the world in 2016. About 40 percent of people live near the coast, and 90 percent of tropical storms form within 20° of the Equator—the parts of the globe where mangrove forests flourish. You can use the Population Density and Longitudes and Latitudes layers to see how many people and mangroves live in these regions, and the Protected Areas layer to find the areas where special conservation plans exist to defend mangrove ecosystems!

Mangroves are a group of tree and bush species that thrive in coastal intertidal zones, or the area between high and low tide. Because they prefer warm-water ecosystems, they are most commonly found at tropical and subtropical latitudes around the world. They are halophytes, or salt-tolerant, and adapted to live in low-oxygen saline or brackish water. They are easily recognizable due to their tangle of prop roots, which keep their leafy branches above the water as the cycle of tides changes the water level.  Though over 50—and possibly up to 110—species are considered mangroves, not all are from the genus Rhizophora. Some mangrove species are the result of convergent evolution, when unrelated species adapt to a harsh environment (like a high-salt, low-oxygen intertidal zone) by taking on similar characteristics (like a tangled prop root system). For this reason, extensive mangrove forests may contain only a handful of unique mangrove species—and even within a single forest, different types of mangroves each occupy distinct niches.  Mangroves are crucial to the health of coastal ecosystems. Their root systems protect the shoreline against erosion by reducing wave intensity and trapping sediments against the land. Mangrove forests are also effective at carbon sequestration, the process of removing carbon dioxide (a harmful greenhouse gas) from the atmosphere. Their labyrinth of roots is a popular hiding spot for fish and other marine life evading predators. They also shield coastal communities from tsunamis and storm surge.  Today, the largest mangrove forest in the world is the Sundarban forest along the coast of Bangladesh and India, spanning 10,000 square kilometers (almost 4,000 square miles). In the past fifty years, though, it is estimated that up to 35 percent of the world's mangrove forests have been lost. This is largely due to shrimp farming, where mangrove forests are cleared and replaced with artificial ponds for aquaculture practices, as well as other growing threats: unsustainable tourism activities; agricultural practices that cause mangrove removal or harmful runoff and pollution; and mangrove deforestation for coastal development or to gather charcoal and timber. These risks are exacerbated by climate change causing sea levels to rise, effectively drowning mangrove forests, and alterations to water chemistry, temperature, and other conditions, which put stress on the mangroves’ preferred growing conditions. As threats to mangroves become better understood, organizations around the world have been created and joined together to protect these important ecosystems. The data in this map layer is from the Global Mangrove Alliance, a collaboration between Conservation International, The International Union for the Conservation of Nature, The Nature Conservancy, Wetlands International, and the World Wildlife Fund. Supported by grants from the National Geographic Society, Explorers Ben Somerville and Margaret Owuor use research, education, and storytelling to protect mangrove forests near their homes in the Caribbean and southeastern Kenya. The map layer shows mangrove forest extent around the world in 2016. About 40 percent of people live near the coast, and 90 percent of tropical storms form within 20° of the Equator—the parts of the globe where mangrove forests flourish. You can use the Population Density and Longitudes and Latitudes layers to see how many people and mangroves live in these regions, and the Protected Areas layer to find the areas where special conservation plans exist to defend mangrove ecosystems!

Overview Carotenoid Content Index (Car) from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). Car describes the canopy content of carotenoids in terrestrial vegetation. Car is calculated as ((1/R495)-(1/R705))*R800, where R495, R705, and R800 are the surface reflectance at 495, 705 and 800 nanometers (nm), respectively (Gitelson et al. 2002). Data provided as global, 8-day averages mapped to a standard, equal-angle, latitude-longitude grid of 0.02 degrees, which equates to ~2 kilometers (km) at the equator. Data has been corrected for atmospheric scattering and absorption. Pixels with no data values are masked (excluded) due to presence of water, cloud cover, or other features marked by quality control flags as described below. Data Layer Summary Name, units Carotenoid Content Index (Car) Measure of terrestrial canopy carotenoid content, a phytosynthetic and photoprotective pigment. Car is unitless. Spatial resolution 2 km2 Level-3, mapped to standard grid, global coverage Time Resolution 8 day average Each pixel represents an average of the valid, daily values observed within that grid element during the 8-day interval (approx 1 week). Note: 8 day average data layer is calculated from daily, "refined" (archive-quality) Level 3 PACE Car products available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Data Version Version 3.1 Data versions change as improved calibration, validation, algorithm refinements, etc are applied over time. See Version 3.1 Release Notes for details. Latency ~1 month This imagery service provides “refined” (archive-quality) PACE data products from March 2024-present, which have an approx 1 month latency. Note: Lower latency “near real time (NRT)” Level 3 PACE Car products available daily from NASA Earthdata at this link in NetCDF format. Latency typically Earthdataaccount required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Intended Use: What can I do with this layer? There are three major classes of plant pigments: chlorophylls, carotenoids, and anthocyanins. Car provides information on changes in vegetation carotenoid content and photophysiological status. Carotenoids are structural components of the photosynthetic antenna and reaction center complexes and serve both photosynthetic and photoprotective functions (Blackburn 2007). The time (8 day average) and space (2 km2) resolution of this data layer enables weekly to seasonal and annual insight at local to global scales.   Limitations of Use: What should I know about the data? Data Status (Maturity): This product is provisional, which means that product results have been reviewed and are in family with heritage data products or other basis of expectation, but have not yet been extensively validated and so may still contain significant errors. Data layer is sufficient for qualitative determinations of product fitness-for-purpose, for testing by operational users, and may be suitable for scientific publication. Data Masking: This is a Level 3, globally-binned product from NASA’s Ocean Biology Processing Group (OBPG). By default, OBPG Level 3 data values are masked (value is set to null, or no data) according to a standard set of quality control parameters. Please see Level 3 Algorithm Usage Constraints for masking parameter details. Algorithm Theoretical Basis Document (ATBD): See ATBD for algorithm details including scientific theory, calculations, etc. https://doi.org/10.5067/8L77O0Y8GHV8.   Where do I go with further questions? Visit NASA’s Earthdata Forum where users can post questions that are answered by the personnel working at NASA with the PACE mission and/or the Ocean Biology Distributed Active Archive Center (OB.DAAC) that processes and distributes PACE data. First, follow this link to view only “PACE” and “OBDAAC” tagged questions on the Earthdata Forum. Then search and read through previous posts for your question. If it has not yet been asked please post your question in the Forum and be sure "PACE" and "OBDAAC" filter tags are applied.     Mission and Product Background PACE Mission Overview: Launched in February 2024, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory uniquely provides near-daily, hyperspectral coverage of Earth’s surface, plus hyperspectral and hyperangular polarimetry. PACE is a comprehensive Earth System mission, delivering all new, space-based information on the health and diversity of aquatic and terrestrial life (algae, plants), aerosol properties & modes (e.g. smoke, dust), cloud types, and more.  The Ocean Color Instrument (OCI), PACE's primary sensor, is a hyperspectral (5 nm resolution) imaging spectrometer that measures properties of light from the ultraviolet (UV, 340 nm) through visible and near infrared (NIR, 895 nm) portions of the electromagnetic spectrum in 1.25 to 2.5 nm steps, plus 7 shortwave infrared (SWIR) bands (Werdell et al. 2019). Observations from OCI are moderate spatial resolution (1.2 km2), enabling a relatively frequent revisit time of 1-2 days. This combination of continuous, global coverage and spectral capabilities is unmatched by any other satellite platform, public or private. PACE benefits society by expanding our foundational knowledge of Earth and enabling novel, space-based science and applications tools across water resources (e.g. fisheries), air quality, land monitoring & management, and more.   Terrestrial Remote Sensing from an Ocean Color Sensor? Designed and optimized to operate across a broad dynamic range, from the dark target of the ocean to bright, highly reflective cloud tops, PACE’s OCI also collects surface reflectance data from the world's terrestrial environments across 122 bands, resolving spectral properties of the Earth more finely than previous multispectral missions could achieve. Work thus far has demonstrated viability of OCI’s land data products for terrestrial investigations (Caplan et al. 2025). OCI’s coverage and capabilities enable provision of both novel, hyperspectral-enabled land observations as well as maintenance and extension of certain historical multispectral data records. Advanced vegetation indexes (VIs) enabled by OCI’s narrow-wavelength surface reflectance measurements provide continuous, synoptic insights into foliar pigment amounts (chlorophylls, carotenoids, and anthocyanins), water content, and photosynthetic light use efficiency across the globe each day. Heritage VIs (e.g., normalized difference vegetation index, NDVI; reviewed in Huang et al. 2021) can also be calculated by aggregating (averaging) OCI’s narrow bandwidths across ranges that approximate the multi-spectral, broadband reflectances of instruments such as MODIS, see the Mathematical Theory section of the ATBD for details. See the full list of heritage and hyperspectral-enabled VIs produced by PACE here.   What other PACE data layers are available? This data layer is 1 of 11 PACE products initially selected for the Living Atlas catalog from the mission’s comprehensive aquatic, atmospheric, and terrestrial product catalog. Click here to view all 11 PACE Living Atlas imagery services. The Level 3, 122-band hyperspectral surface reflectance data (SFREFL) used to derive all VIs is available from NASA Earthdata to download at 2 kilometer resolution in NetCDF format. To access PACE mission data products available from NASA, first learn about PACE data access here, next decide which PACE product(s) you would like to use, then go to NASA Earthdata to find PACE products in NetCDF format, which can be downloaded and then imported into ArcGIS. An Earthdata account is needed to download data from NASA, sign up here. To learn about using Earthdata Search to find data, watch this tutorial.   How can I stay up to date with the PACE mission?   Join the PACE Community for mission news, events, new product release announcements, and to receive our newsletters. Would you like to see imagery services for more PACE products? Please let us know here.   References: Algorithm Theoretical Basis Document: https://doi.org/10.5067/8L77O0Y8GHV8 Blackburn, G. A. (2007) Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58(4), 855-867. https://doi.org/10.1093/jxb/erl123 Caplan, S. and Huemmrich, K. F. (2025) Unveiling PACE OCI’s hyperspectral terrestrial data products. Remote Sensing Letters, 16(4), 422–433. https://doi.org/10.1080/2150704X.2025.2470905 Gitelson, A. A., Zur, Y., Chivkunova, O. B., and Merzlyak, M. N.(2002) Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75(3): 272–281. https://doi.org/10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2 Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1 Werdell, P. J. et al. (2019) The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances. Bulletin of the American Meteorological Society, 100, 1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS). EMIT uses imaging spectroscopy to take measurements of the sunlit regions of interest between ~52° N latitude and ~52° S latitude. An interactive map showing the locations of regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal. The EMIT Level 2B Fractional Cover and Uncertainty (EMITL2BFRCOV) Version 1 data product provides a series of fractional cover estimates, associated uncertainty metrics, and one quality control (QC) flag mask variable. The fractional cover estimates are derived from the EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) data product, while the QC flag mask variable is derived from EMITL2ARFL, EMIT L2A Mask (EMITL2AMASK), and non-EMIT landcover and water body inputs. Scenes/granules where cloud cover is greater than 80 percent have been excluded from this collection. For more details about inputs used for this data product, see Sections 4.3 and 4.5 of the Algorithm Technical Basis Document (ATBD). Each EMITL2BFRCOV granule consists of seven Cloud Optimized GeoTIFF (COG) files at 60 meter (m) spatial resolution: Photosynthetic Vegetation Fractional Cover (EMIT_L2B_FRCOVPV) and Uncertainty (EMIT_L2B_FRCOVPVUNC), Non-photosynthetic Vegetation Fractional Cover (EMIT_L2B_FRCOVNPV) and Uncertainty (EMIT_L2B_FRCOVNPVUNC), Bare Soil Fractional Cover (EMIT_L2B_FRCOVBARE) and Uncertainty (EMIT_L2B_FRCOVBAREUNC), and QC Flags (EMIT_L2B_FRCOVQC). A browse image is also available. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator with some granules at the end of an orbit segment reaching 150 km in length. Known Issues • Data acquisition gap: From September 13, 2022, to January 6, 2023, a power issue resulted in a shutdown of the EMIT sensor. No data were acquired during that time frame.

The Earth Surface Mineral Dust Source Investigation (EMIT) is an Earth Ventures-Instrument (EVI-4) Mission aboard the International Space Station (ISS). EMIT’s prime mission was designed to map surface mineralogy of arid dust source regions using imaging spectroscopy in the visible and short-wave infrared (VSWIR). EMIT uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. During the prime mission, the EMIT team focused on using surface reflectance to resolve absorption features of iron oxides, clays, sulfates, carbonates, and other dust-forming minerals in Earth's dust source regions. In the extended mission, EMIT’s core measurements remain the same, but acquisitions expand to sample all regions observable from the ISS orbit, supporting efforts across the atmosphere, biosphere, cryosphere, geosphere, hydrosphere, and more. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal. The EMIT Level 2B Fractional Cover and Uncertainty (EMITL2BFRCOV) Version 2 data product provides a series of fractional cover estimates, associated uncertainty metrics, and one quality control (QC) flag mask variable. The fractional cover estimates are derived from the EMIT L2A Estimated Surface Reflectance and Uncertainty Version 2 (EMITL2ARFL) data product, while the QC flag mask variable is derived from EMITL2ARFL Version 2, EMIT L2A Masks (EMITL2AMASK) Version 3, and non-EMIT landcover and water body inputs. For more details about inputs used for this data product, see Sections 4.3 and 4.5 of the Algorithm Technical Basis Document (ATBD). Each EMITL2BFRCOV granule consists of seven Cloud Optimized GeoTIFF (COG) files at 60 meter (m) spatial resolution: Photosynthetic Vegetation (PV) Fractional Cover (EMIT_L2B_FRCOVPV) and Uncertainty (EMIT_L2B_FRCOVPVUNC), Non-photosynthetic Vegetation (NPV) Fractional Cover (EMIT_L2B_FRCOVNPV) and Uncertainty (EMIT_L2B_FRCOVNPVUNC), Bare Soil Fractional Cover (EMIT_L2B_FRCOVBARE) and Uncertainty (EMIT_L2B_FRCOVBAREUNC), and QC Flags (EMIT_L2B_FRCOVQC). A browse image is also available. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator with some granules at the end of an orbit segment reaching 150 km in length. Known Issues * Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe. Outside of this period, regular short-term gaps in coverage are expected depending on the solar and sensor geometry. * Data pixel misidentification: Pixels along cloud edges and in cloud shadows may be incorrectly identified as high NPV pixels. * Missing QC water pixels: Major inland lakes that should be classified as QC water pixels may not be included due to them being absent from an input dataset used in the production of this data product. * QC pixel misidentification: Water, cloud, and snow/ice pixels may be misidentified based on normalized snow difference index, pixel brightness, or pixel shading. * For more information on these topics check the Known Issues Section of the ATBD.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Tiled Water Use Efficiency Instantaneous L4 Global 70 m (ECO_L4T_WUE) Version 2 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE, the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants. The ECO_L4T_WUE Version 2 data product is available globally in Cloud Optimized GeoTIFF (COG) format and uses a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each granule contains separate COG files for each layer: Water Use Efficiency (WUE), Water Gross Primary Production, cloud mask, and water mask. A low-resolution browse is also available showing WUE as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

The Global Mangrove Forests Distribution, 2000 data set is a compilation of the extent of mangroves forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques. The data are available at 30-m spatial resolution. The total area of mangroves in the year 2000 was estimated at 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. **Data creation:** 2025-12-03 **Supplemental Information:** Platforms: LANDSAT-5 Instruments: TM Spatial Extent: N: 90 S: -90 E: 180 W: -180 Spatial Resolution: 30 Decimal Degrees x 30 Decimal Degrees Geographic Region: GLOBAL Coordinate System: CARTESIAN Granule Spatial Representation: CARTESIAN Temporal Extent: 2000-01-01 to 2000-12-31 Data Partner: Earth Science Data Information System (ESDIS) Concept ID: C3550194192-ESDIS Data State: COMPLETE Number of Files/Granules: 111 Processing Level: 4 Published: 2005-12-31 Updated: 2005-12-31 Science Keywords: Mangroves , Sustainability , Climate Adaptation **Citation:** Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2013). Global Mangrove Forests Distribution, 2000 (Version 1.00) [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4J67DW8 Date Accessed: 2025-12-03 **Contact points:** Resource Contact: [EARTH DATA](mailto:https://forum.earthdata.nasa.gov/viewforum.php?f=7) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0). Find more information on the license here . **Online resources:** Global Mangrove Forests Distribution, 2000

The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to 2022). Pixel values indicate the percentage of good NDVI observations over the entire time period. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Percentage (%) Scale Factor : 1.0 Map code : L2-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009-2019). Pixel values indicate the percentage of good NDVI observations over the entire time period. While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud cover. Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

The layer gives an indication of the quality of the NDVI input data. It shows whether a valid observation was available for that dekad, and if not, the length of the data gap with the nearest valid observation. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Scale Factor : 1.0 Map code : L2-QUAL-NDVI-D New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The quality layer is produced during the smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts if and how a new value was created for that pixel. The procedure can be described as follows: 1) First all the valid observations (not flagged) are treated. If the final estimate of the smoothed NDVI is very close to the (pre-cleaned) NDVI value, the QI is set to 0 (ideal situation). Where a valid observation was present but it was adapted by the smoothing, the QI is set to 250 ("valid-smoothed" in the legend). 2) For the remaining (flagged) observations, the QI is set to the number of days in-between the surrounding valid observations (i.e. with QI=0 or 250). If the length of the data gap exceeds 240 days, it is saturated to 240. The fundamental idea is that the longer the gap, the less reliable the smoothing is. For the observations at the profile edges, it is assumed that the (a priori unknown) observations preceding the first valid observation and following the last valid observations are "good" This quality layer depicts the quality for the NDVI, fAPAR, albedo and NPP, as all these data components rely on the same input, i.e. the spectral reflectance data. Furthermore, the length of the data gap is the same. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/. Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage Download the data from Google Cloud Storage

Along with the core information on state and changes in forests, FRA 2000 is reporting on various ecological aspects of forests. Forest resources information will be reported by ecological zone, which will contribute to understanding the implications of forest change on (ecosystem) biological diversity and carbon-cycling processes. The 2000 global ecological zone map has been developed, building upon the FRA 1990 experience for the tropics and extending the coverage to include the temperate and boreal forests. A globally consistent classification has been adopted, based on the Koppen-Trewartha climate system in combination with natural vegetation characteristics. A total of 19 global ecological zones have been defined and mapped, ranging from the evergreen tropical rainforest zone to the boreal tundra woodland zone. A main principle of delineating the global ecological zones involved the aggregation or matching of available regional ecological or potential vegetation maps into the global framework. **Data publication:** 2001-06-01 **Supplemental Information:** FAO's main partners in the development of the EZ map were the UNEP-WCMC, EDC and the have been instrumental in the production of the map by providing their expertise, advice and input data. A key event was the Cambridge expert consultation (July 1999), attended by some 20 regional ecozone experts, where the proposed classification framework was presented and adopted. **Contact points:** **Data lineage:** Data quality information not available **Online resources:** Link to Forest Resource Assessment website eco_zone.axl Global Ecological Zones - shapefile

The World Database on Protected Areas (WDPA) is the largest assembly of data on the world's terrestrial and marine protected areas, containing more than 260,000 protected areas as of August 2020, with records covering 245 countries and territories throughout the world. The WDPA is a joint venture between the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) and the International Union for Conservation of Nature (IUCN) World Commission on Protected Areas (WCPA). Data for the WDPA is collected from international convention secretariats, governments and collaborating NGOs, but the role of custodian is allocated to the Protected Areas Programme of UNEP-WCMC, based in Cambridge, UK, who have hosted the database since its creation in 1981. The WDPA is updated on a monthly basis, and can be downloaded from https://www.protectedplanet.net/en/thematic-areas/wdpa. The WDPA dataset is published for the November 2024 release. **Data creation:** 2020-08-01 **Citation:** IUCN and UNEP-WCMC (2020), The World Database on Protected Areas (WDPA) [https://www.protectedplanet.net/en/search-areas?filters%5Bdb_type%5D%5B%5D=wdpa&geo_type=region], [08/2020], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net. **Contact points:** Resource Contact: Protected Planet (WDPA) UNEP-WCMC & IUCN Responsible Party: [UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)](mailto:https://www.unep-wcmc.org/about-us/contact) Metadata Contact: UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) **Data lineage:** Data quality information not available **Resource constraints:** PLEASE READ THESE TERMS AND CONDITIONS CAREFULLY. IF YOU DO NOT AGREE TO ANY OF THE TERMS AND CONDITIONS DO NOT DOWNLOAD. BY DOWNLOADING THE WDPA MATERIALS MADE AVAILABLE ON PROTECTEDPLANET.NET YOU ACCEPT AND AGREE TO COMPLY WITH THE TERMS AND CONDITIONS BELOW. * No Commercial Use * Neither (a) the WDPA Materials nor (b) any work derived from or based upon the WDPA Materials (“Derivative Works") may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use" means a) any use for profit or to generate revenue, or b) any use by an individual or entity operating within or on behalf of or to the benefit of or to assist the activities of any entity other than a not-for-profit organisation. To apply for permission for Commercial Use of the WDPA Materials please send an email to business-support@unep-wcmc.org outlining your needs. * No Sub-licensing or Redistribution of WDPA Data * The WDPA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPA Data contained in the WDPA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPA Data please refer them to protectedplanet.net. If you wish to provide a service through which the WDPA Data are downloadable or otherwise made available for redistribution you must contact protectedareas@unep-wcmc.org for permission and further guidance. More information on the WDPA license at https://www.unep-wcmc.org/policies/wdpa-data-licence#data_policy **Map Disclaimer:** The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Final status of the Abyei area is not yet determined. **Online resources:** Dimension Translator Json file Dimensions labels jsonstat file quoted_csv.xls Download World Database on Protected Areas (WDPA) from Protected Planet

ASEAN Heritage Parks (AHP) are selected protected areas in the Association of Southeast Asian Nations (ASEAN) region that are known for their unique biodiversity and ecosystems, wilderness and outstanding values in scenic, cultural, educational, research, recreational and tourism. **Data creation:** 2026-05-13 **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Resource Contact: [Christian Elloran](mailto:cbelloran@aseanbiodiversity.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** ASEAN Centre for Biodiversity

The RESOLVE Ecoregions 2017 dataset provides a global depiction of 846 terrestrial ecoregions representing the living planet. Ecoregions are ecosystems of regional extent and represent distinct assemblages of biodiversity, including taxa beyond vegetation. The dataset groups terrestrial ecoregions into 14 biomes and 8 realms. It provides a useful basemap for conservation planning because ecoregions are based on natural boundaries, distinct biogeographic assemblages, and ecological habitats rather than political boundaries. Each ecoregion includes attributes such as ecoregion name, ecoregion ID, biome name, realm, color codes, area, and Nature Needs Half (NNH) protection **Data creation:** 2026-06-26 **Supplemental Information:** The dataset contains 846 terrestrial ecoregions grouped into 14 biomes and 8 realms. It includes four Nature Needs Half categories: 1) Half Protected: More than 50% of the total ecoregion area is already protected. 2) Nature Could Reach Half: Less than 50% of the total ecoregion area is protected but the amount of remaining unprotected natural habitat could bring protection to over 50% if new conservation areas are added to the system. 3) Nature Could Recover: The amount of protected and unprotected natural habitat remaining is less than 50% but more than 20%. Ecoregions in this category would require restoration to reach Half Protected. 4)Nature Imperiled: The amount of protected and unprotected natural habitat remaining is less than or equal to 20%. Achieving half protected is not possible in the short term and efforts should focus on conserving remaining, native habitat fragments. The dataset includes fields for ecoregion name, biome name, realm, ecoregion ID, NNH category, NNH category name, ecoregion color, biome color, and NNH color. **Citation:** Bioscience, An Ecoregions-Based Approach to Protecting Half the Terrestrial Realm doi:10.1093/biosci/bix014 **Contact points:** Metadata Contact: [Carolyn Burns](mailto:sdesposito@resolve.ngo) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**