Global biodiversity data

The Spatial Database of Planted Trees (SDPT) was compiled by Global Nature Watch using data obtained from national governments, non-governmental organizations and independent researchers. In version 2.0 (v2.0) data were compiled for 158 countries around the world, with most country maps originating from supervised classification or manual polygon delineation of Landsat, SPOT or RapidEye satellite imagery.  “Planted trees” in the SDPT includes planted forests, stand of planted trees - other than tree crops - grown for wood and wood fiber production or for ecosystem protection against wind and/or soil erosion. Planted forests can efficiently produce high quantities of wood products that may alleviate pressure on natural forests, create jobs that support rural development, and/or provide a range of ecosystem services, especially when established on degraded lands. The data set also includes perennial tree crops, such as rubber, oil palm, coffee, coconut, cocoa and orchards. The SDPT makes it possible to identify planted forests and tree crops apart from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.  The SDPT v2.0 contains 264 million hectares of planted forest and 65 million hectares of agricultural trees, or approximately 90% of the world’s total planted forest area in 2020 (FAO 2020). The SDPT was compiled through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table.  For more detailed information, please refer to the Technical Note. Data is available for download in all countries except Papua New Guinea. If you are aware of any additional planted trees data, please let us know by filling out this form.  Identifies planted forests and tree crops on a near-global scale.

Logging concessions refers to areas allocated by a government for harvesting timber and other wood products in a public forest. Logging concessions are distinct from wood fiber concessions, where tree plantations are established for the exclusive production of pulp and paper products. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes "forest permits," "tenures," "licenses," and other terms. This data set displays logging concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source. As available: additional data source information is linked below: - [Cambodia]() - Cameroon - Democratic Republic of the Congo - Gabon - Liberia - Suriname If you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org). Displays boundaries of forested areas allocated by governments to companies for harvesting timber and other wood products.

This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels. Alerts are classified as high confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82 The integrated deforestation alerts are available on **Google Earth Engine** with asset ID: projects/forma-250/assets/gfw_integrated_alerts/default_latest **Download** the raster tiles here: https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about Monitor forest disturbance in near-real-time using integrated alerts from three alerting systems

This net flux layer is part of the forest greenhouse gas flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest fluxes which can assist a variety of actors and organizations with tracking greenhouse gas (GHG) fluxes from forests and in decreasing emissions or increasing removals by forests. Net forest flux represents the exchange of GHGs between forests and the atmosphere, calculated as the difference between GHGs emitted by forests and carbon removed by (or sequestered by) forests during the model period. Net carbon flux is calculated by subtracting average gross removals from annual gross emissions in each forested pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of GHGs between 2001 and 2025. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. 2022. This layer reflects the cumulative net flux during the model period (2001-2025) and must be divided by 25 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). We have made several updates to the model since its original release. For documentation through the current version, please refer to this blog. For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to Gibbs et al. (2025). Net flux is available for download in two different area units over the model duration: 1) megagrams of CO2e emissions/ha, and 2) megagrams of CO2e emissions/pixel. The first is appropriate for visualizing (mapping) net flux because it represents the density of GHG fluxes per hectare. The second is appropriate for calculating the net flux in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total net flux for that area. The values in the latter were calculated by adjusting the net flux per hectare by the size of each pixel, which varies by latitude. When estimating net flux occurring over a defined number of years between 2001 and 2025, divide the values by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Download data from here. Access data on GEE here Displays the net flux of greenhouse gases from forests, calculated as the difference between gross forest greenhouse gas emissions from stand-replacing forest disturbances and gross carbon removals from forest growth

OverviewThis feature service supports public reporting of feral hog sightings and related follow-up information. It stores submitted survey responses for managing hog observation reports and communicating with the person who submitted the report. ContentThe service contains a single survey layer that captures reporter contact details, sighting date and time, the estimated number of hogs observed, location notes, and additional comments about the sighting. It also includes status tracking fields and internal notes used to support review and processing of each report. Use CasesCollecting public reports of feral hog activity Supporting wildlife or land management response workflows Tracking report review and processing status Maintaining a record of hog sighting submissions for operational follow-up Geographic CoverageThe service is configured for use in Flagler County.

Terrestrial and marine protected areas. Pixels where there is presence of terrestrial and marine protected were given a value of 1 (YES response) 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. **Data revision:** 2021-12-14 **Citation:** IUCN and UNEP-WCMC (2020). The World Database on Protected Areas (WDPA). Available at: www.protectedplanet.net. **Contact points:** Metadata contact: [OCB Environment](mailto:OCB-Environment-List@fao.org) FAO-UN **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:**

- This data set, a collaboration between the Global Land Analysis & Discovery (GLAD) () lab at the University of Maryland (UMD), Google, USGS, and NASA, measures areas of tree cover loss across all global land (except - Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval, and additional images used for 2023, 2024 and 2025. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.  - In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.  - This data set has been updated five times since its creation, and now includes loss up to 2025 (Version 1.13). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2025, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.13 update here:  and access on GEE here: .  - When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).  - The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.  Identifies areas of gross tree cover loss

Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.

This data set from the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland measures areas of tree cover gain from the year 2000 to 2020 across the globe at 30 × 30 meter resolution, displayed as a 20-year cumulative layer. Tree cover gain was determined using tree height information from the years 2000 and 2020. Tree height was modeled by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. Gain was identified where pixels had tree height ≥5 m in 2020 and tree height <5 m in 2000. Tree cover gain may indicate a number of potential activities, including natural forest growth, the tree crop rotation cycle, or tree plantation management.  When zoomed out (< zoom level 12), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 12).  Identifies areas of tree cover gain

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

This carbon removals layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2025. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. (2022). Atmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2025) and must be divided by 25 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). We have made several updates to the model since its original release. For documentation through the current version, please refer to this blog. For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to Gibbs et al. (2025). Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel. The first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare. The second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area. The values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude. When estimating removals occurring over a defined number of years between 2001 and 2025 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Download data from here. Access data on GEE here Displays forest carbon removals by forest sinks

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22Q2 product are 19 Science Dataset (SDS) variables. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final variable is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. Important information is provided in Section 5 of the User Guide when comparing VNP22Q2 with the MCD12Q2 data product. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MOD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MOD13A2 products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. Known Issues * The Relative Azimuth Angle (RAA) for the input MODIS data is computed based on absolute values of the finer resolution pixels resulting in positive values and has minor usefulness. * The RAA for the input AVHRR data contain values in the -360° to 360° range. The routine to restrict the values in the -180° to 180° range was accidentally missed and can be corrected using the following routine described in Section 4.2.1 of the User Guide and Algorithm Theoretical Basis Document: * SinRelativeAz=sin(RAA) * CosRelativeAz=cos(RAA) * Correct-RAA = atan2(SinRelativeAz,CosRelativeAz)

For more information on this data product including usage, metadata, mission information, and help resources, please reference PACE Carotenoid Content Index (Car) March 2024 – present (8 Day Average) - OverviewArcGIS Image Service, WMS, and WCS functionality may be found through the Image Service REST Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/GESDISC/PACE_OCI_L3M_CAR/ImageServer

Dates of Images: Post-Event: 5/6/2018; 5/15/2024 Pre-Event: 4/8/2018; 4/21/2024 Date of Next Image: Unknown Summary: NDVI: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: NDVI: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_landsat_ndwi/ImageServer/WMSServer

Dates of Images: Post-Event: May 24, 2022 Pre-Event: May 16, 2022 Date of Next Image: None Summary: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus REST Endpoint: See URL on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_sentinel2_ndvi/ImageServer/WMSServer

For more information on this data product including usage, metadata, mission information, and help resources, please reference PACE Chlorophyll Index Red Edge (CIRE) March 2024 – present (8 Day Average) - OverviewArcGIS Image Service, WMS, and WCS functionality may be found through the Image Service REST Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/GESDISC/PACE_OCI_L3M_CIRE/ImageServer

For more information on this data product including usage, metadata, mission information, and help resources, please reference PACE Normalized Difference Vegetation Index (NDVI) March 2024 – present (8 Day Average) - OverviewArcGIS Image Service, WMS, and WCS functionality may be found through the Image Service REST Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/GESDISC/PACE_OCI_L3M_LANDVI/ImageServer

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VJ117A2GF) Version 2 data product is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2GF is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VJ117A2GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ117A2GF is the improved VJ117A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ117A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ117A2GF product are variables for GPP, PSN, along with a quality control variable. A low resolution browse image for GPP is also available for each VJ117A2GF granule. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Due to the increasing size and complexity of global forest monitoring data sources, analysis and interpretation tools for this data are ever more important for intervention efforts, allowing for the quick identification and interpretation of significant forest loss. The emerging hot spots data set identifies the most significant clusters of primary forest loss between 2002-2025 at a country level basis, on a tropical scale. The term ‘hot spot’ is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. The different categories of hot spots are described below:  New: A location that is a statistically significant hot spot only for the year 2025 and has never been a hot spot before.  Sporadic: A location that is an on-again then off-again hot spot. Less than 22 of the 24 years have been statistically significant hot spots.  Intensifying: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), including the most recent year (2025). In addition, the intensity of clustering of high counts in each year is increasing.  Persistent: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), with no discernible trend indicating an increase or decrease in the intensity of clustering over time.  Diminishing: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%). In addition, the intensity of clustering of high counts in each year is decreasing, or the most recent year (2025) is not hot.  The emerging hot spots analysis uses the annual Hansen et al 2013 tree cover loss data set between the years 2002 – 2025, the Turubanova et al. 2018 primary forest extent data set for the year 2001, and the ESRI ArcGIS Emerging Hot Spot Analysis geoprocessing tool. In this analysis, primary forest is defined as mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history. Forest loss is defined as ‘stand replacement disturbance,’ or the complete removal of tree cover canopy at the Landsat pixel scale. The emerging hot spots analysis tool uses a combination two statistical measures, the Getis-Ord Gi\* statistic to identify the location and degree of spatial clustering of forest loss, and the Mann-Kendall trend test to evaluate the temporal trend over time.  The forest loss data used in this analysis has a user’s accuracy of 87% and a producer’s accuracy of 83.1% across the tropical biome. Additionally, because this analysis was run for individual countries, results are relative to the patterns and amount of loss in each country. Results should not be directly compared between countries - please use caution when viewing layer at a global scale.  To identify statistically significant clusters of primary forest loss on a country level basis

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). Data users are encouraged to transition to the equivalent VJ213C1 (primary) or VJ113C1 (secondary) data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. This product includes a total of 18 variables, including three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

For more information on this data product including usage, metadata, mission information, and help resources, please reference PACE Modified Anthocyanin Reflectance Index (mARI) March 2024 – present (8 Day Average) - OverviewArcGIS Image Service, WMS, and WCS functionality may be found through the Image Service REST Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/GESDISC/PACE_OCI_L3M_mARI/ImageServer

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover gain across all global land (except Antarctica and other Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain. Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations. When zoomed out (50%. Identifies areas of tree cover gain

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The product consists of eighteen distinct variables in total. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices variables, this product also includes variables for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality variable. Two low resolution browse images are also available for each VJ113C2 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

The MOD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the NPP for that specific month. The data is provided from January 2009 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Map code: L1-NPP-M Scale Factor : 0.001 Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 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 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily:incoming solar radiation and temperature data (Tmin/Tmax); - Monthly:fAPAR and soil moisture stress; - Seasonal:Land Cover. **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 Map code: L1-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 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **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/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded.

This data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions. Provides annual agriculture-linked deforestation estimates for oil palm, soy, cattle, cocoa and coffee for the years 2001-2015 by administrative boundary

This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from four alert systems (DIST-ALERT, GLAD-L, GLAD-S2, RADD) into a single, integrated disturbance alert layer. This integration allows users to detect deforestation and other vegetation loss faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated.   The source alert systems are derived from satellites of varying spectral and spatial resolutions. The 30 m GLAD Landsat-based alerts and DIST-ALERTs are up-sampled to match the 10 m spatial resolution of purely Sentinel-based alerts (GLAD-S2, RADD). Using the same 10 m grid avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.   Alerts are classified as high confidence when detected multiple times (two or more times for GLAD-L, GLAD-S2 and RADD, and four or more times for DIST-ALERT) by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or more) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. Alerts from multiple systems which are within 180 days (6 months) of each other are considered a single event. In this case, the earliest date within that 180 day window will appear as the alert date in the integrated alert product. These will be considered highest confidence alerts. The date of the alert remains until another new alert (which is not within 6 months) in the same pixel is registered.   A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82   Monitor vegetation disturbance in near-real-time using integrated alerts from multiple alerting systems

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the years 2000 and 2010 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend). Data in this layer were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm. The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold. Identifies areas of tree cover

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). Data users are encouraged to transition to the equivalent VJ215A2H (primary) or VJ115A2H (secondary) data products. The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VNP15A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU). Mammal species are mammals excluding whales and porpoises.

A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU). Higher plants are native vascular plant species.