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This is a higher resolution data product that expands upon the methodology presented in Baccini et al. (2012) to generate a pan-tropical map of aboveground live woody biomass density at 30 m resolution for circa the year 2000. Along with the carbon density values, there is an error map at the same spatial resolution providing the uncertainty in aboveground carbon density estimation. These maps allow for the co-location of biomass estimates with Hansen et al. (2013, v1.0) tree cover loss estimates at similar spatial resolution. The statistical relationship derived between ground-based measurements of forest biomass density and co-located Geoscience Laser Altimeter System (GLAS) LiDAR waveform metrics as described by Baccini et al. (2012) were used to estimate the biomass density of more than 40,000 GLAS footprints throughout the tropics. Then, using randomForest models, the GLAS-derived estimates of biomass density were correlated to continuous, gridded variables including Landsat 7 ETM+ satellite imagery and products (e.g., reflectance), elevation, and biophysical variables. By using continuous gridded datasets as inputs to the randomForest models, a wall-to-wall 30 m resolution map of aboveground woody biomass density across the tropics was produced as well as the associated uncertainty layer. The uncertainty layer takes into account the errors from allometric equations, LiDAR based model, and randomForest model. All the errors are propagated to the final biomass estimate. A detailed description of the work will be reported in a new paper under preparation. 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.

Dataset Attributes

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  • name
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  • shape
    Number
  • st_area(shape)
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  • st_length(shape)
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  • By on January 26, 2016
  • Updated 9 days ago

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