Sudan - Aboveground Biomass

✦ DISCOVERED BY ORIONAICTreesBiodiversitySudan
Map preview of Sudan - Aboveground BiomassSudan

About this dataset

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.

Documentation

CTrees produces its aboveground biomass (AGB) estimates by combining light detection and ranging (LiDAR) measurements from NASA's ICESat and GEDI satellites -- which provide a global dataset of vegetation structure and aboveground live biomass, calibrated against more than 1.5 million ground plot data points -- with change-detection machine learning algorithms customized for more than 800 ecoregions globally, using satellite radar and optical imagery to map biomass and biomass change annually. This HDX dataset provides only the AGB layer. CTrees' broader Land Carbon Map product (see https://ctrees.org/products) also estimates belowground biomass (via ecological models) and per-pixel uncertainty (via error propagation models); neither is included in this dataset. Peer-reviewed methodology references: Saatchi, S., et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. PNAS, 108(24), 9899-9904. https://doi.org/10.1073/pnas.1019576108. Xu, L., et al. (2021). Changes in global terrestrial live biomass over the 21st century. Science Advances, 7(27), eabe9829. https://doi.org/10.1126/sciadv.abe9829. Pixel values are the source's native int16 representation, scaled x10 (raw value / 10 = true Mg/ha). This scale factor is recorded in each resource's GeoTIFF band metadata (Scale/Offset tags), but most plain script-based reads (e.g. a bare rasterio `.read()` or GDAL `ReadAsArray()` call) do not apply it automatically and will return values 10x too high unless you divide by 10 (or apply the Scale tag) yourself.

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Indexed by OrionGIS from Humanitarian Data Exchange (HDX). Data is loaded from the publisher's own server when you use it. Permanent link