Biodiversity data

Trailheads are point locations that provide access, and often vehicle parking, for recreational trail users. This map layer features many of Utah’s trailheads but is not yet complete. In the future, it is expected to include trailhead data assembled from a variety of sources including: US Forest Service, US National Parks Service, State Parks, and map data for which full rights were purchased by the GOED Outdoor Recreation Office in July 2014, from a private entity (Orbital View, Inc). Currently (April 2017), this layer includes only the original data purchased from Orbital View. Additional trailheads can be submitted to Utah AGRC for inclusion in this map layer. The primary goal for the trailheads map layer is to assign a location and a name to each trailhead. However, the trailhead data is stored within a more complex data model that will allow for collection and publishing of more robuts trail and trailhead information in the future: (https://docs.google.com/spreadsheets/d/1OPGKmRrLYUBr9qO9EtbX6AwcRla82xMvc-BDr26ia9k/edit#gid=0)

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

CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.

The Nature Repair Act 2023 (the Act), which took effect on 15 December 2023, establishes a framework for a legislated, national biodiversity market. The Act provides clear rules to ensure transparency and integrity, fostering collaboration to address environmental decline. The Nature Repair Market is a government scheme designed to deliver high-integrity biodiversity outcomes and increase investment in nature. This scheme establishes a marketplace where individuals and organisations can undertake nature repair projects to generate a tradable certificates. This dataset represents the eligible regions, defined as a subset of IBRA subregions, where biodiversity projects can be established under the Nature Repair (Replanting Native Forest and Woodland Ecosystems) methodology determination. These subregions include landscapes with intensive historical land use, where native vegetation has often been cleared or thinned. Remaining vegetation in these areas is frequently degraded by forestry and agriculture, impacted by pests and weeds, and affected by low levels of ecological connectivity.

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions. VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress. The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation. The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.

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A feature service of Kenya Constituencies around 1999. Obtained form Stanford University website. This data was used in Map 2.10 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being from the Stanford University Library. Data created by Central Bureau of Statistics and Kenya, Ministry of Planning and National Development.

Vegetation of the Zambezi area as used in the SAFARI 2000 project assessing the contribution of burning woody African vegetation south of the Equator to atmospheric aerosols. Vegetation of the East Africa as used in the SAFARI 2000 project assessing the contribution of burning woody African vegetation south of the Equator to atmospheric aerosols. Project abstract: The objective of the National Botanical Institute's (NBI) vegetation mapping project was to map woody plant species to provide estimates of the fraction of individual species contributing to the peak leaf area index for designated vegetation types in southern Africa. The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources are both published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998) covering South Africa, Lesotho, and Swaziland; Giess (1971) covering Namibia; Wild and Barbosa (1968) covering Botswana, Zimbabwe, Zambia, Malawi, and Mozambique; Barbosa (1970) covering Angola; White (1983) covering Tanzania, Kenya, Uganda, and Somalia; and White (1983) covering Ruanda, Burundi, Congo, and Gabon. Each source map delineates of a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final regional map is made up of six independent sub-regional maps. The data set also includes a cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units. The following areas and vegetation types were excluded according to the sub-project objectives, appearing as transparent in the map : areas north of the Equator, evergreen moist forests, pure grasslands, desert and arid regions, Fynbos. These areas are indicated as such on the final product map. Main water bodies are also mapped specifically.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.