Global biodiversity data

The MOD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) 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 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.

The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VJ217A3GF) Version 2 data product is a cumulative composite of Gross Primary Productivity (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. VJ217A3GF is a yearly composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VJ217A3GF will be generated at the end of each year when the entire yearly 8-day VJ215A2H is available. Hence, the gap-filled VJ217A3GF is the improved VJ217A2, 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 VJ217A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ217A3GF product are layers for GPP, NPP, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ217A3GF granule. 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 16-Day (MOD13C1) 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 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MOD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for 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 emissions 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 emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2e emissions/ha, between 2001 and 2025). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO2, NH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GNW. All input layers were resampled to a common resolution of 0.00025 × 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). Three variations of emissions rasters are available for download:    1) megagrams CO2e emissions/ha in pixels with >30% tree cover density (TCD) in 2000 or tree cover gain: Used for visualizing (mapping) emissions according to the default GNW TCD threshold because it represents the density of emissions per hectare. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.    2) megagrams CO2e emissions/pixel in pixels with >30% TCD in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) according to the default GNW TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.    3) megagrams CO2e emissions/pixel in pixels with any amount of tree cover in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) without any TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. This would represent the total emissions from tree cover loss in the AOI without applying a TCD threshold. You would use this if you want to include emissions in pixels that have low (<30%) TCD in 2000. The values in the megagrams CO2e/pixel layers were calculated by adjusting the emissions per hectare by the size of each pixel, which varies by latitude. Tree cover density in 2000 is according to Hansen et al. (2013) and tree cover gain between 2000 to 2020 is according to Potapov et al. (2022). Download data from here. Access data on GEE here Displays forest greenhouse gas emissions from stand-replacing disturbances

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides 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 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. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS 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.

The PRODES project monitors clear cut deforestation in the Brazilian Amazon and Cerrado biomes, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online. Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon and Cerrado are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year's analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. This data set shows annual deforestation in 2008-2020 in the Amazon biome, and 2000-2020 in the Cerrado biome (bi-annual from 2000-2008). Deforestation monitoring system for the Brazilian Amazon and Cerrado biomes, used by the Brazilian government to establish public policy

The PRODES project monitors clear cut deforestation in the Brazilian Legal Amazon, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online. Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year's analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. This data set shows annual deforestation between 2000 and 2015. Deforestation monitoring system for the Brazilian Amazon used by the Brazilian government to establish public policy

This data set shows the dominant driver of tree cover loss from 2001-2023 using the following five categories: * **Commodity-driven deforestation:** Large-scale deforestation linked primarily to commercial agricultural expansion. * **Shifting agriculture:** Temporary loss or permanent deforestation due to small- and medium-scale agriculture. * **Forestry:** Temporary loss from plantation and natural forest harvesting, with some deforestation of primary forests. * **Wildfire:** Temporary loss, does not include fire clearing for agriculture. * **Urbanization:** Deforestation for expansion of urban centers. The commodity-driven deforestation and urbanization categories represent permanent deforestation, while tree cover usually regrows in the other categories. The data were generated using decision tree models to separate each 10 km grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells, and use metrics derived from the Hansen tree cover, tree cover gain, and tree cover loss, NASA fires, global land cover, and population count. Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map, which is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world. All model code, reference samples, decision trees, and the final model are available in the Supplementary Materials of the paper. Shows the dominant driver of tree cover loss within each 10 km grid cell and the intensity of that loss for the time period 2001-2023.