Forest Aboveground Biomass for the Southwestern U.S. from MISR, 2000-2021

Description

This dataset provides estimates of forest aboveground biomass (AGB; in Mg ha-1) at a resolution of 250 m for the southwestern United States over the time period 2000-2021. The AGB estimates were derived from the Jet Propulsion Laboratory Multiangle Imaging Spectro-Radiometer (MISR) Level 1B2 Terrain radiance data and a multi-angle approach that exploits the relationship between forest AGB and a suite of red band reflectance values modeled at viewing angles with respect to the direction of illumination. The year 2000 National Biomass and Carbon Dataset (NBCD 2000) AGB estimates were used to fit a model coefficient for the MISR-derived AGB estimates for the year 2000, with AGB estimates for all subsequent years dependent on both this coefficient and MISR red band bidirectional reflectance factors (BRFs). Quality assurance (QA) files are also provided that allow users to impose criteria of varying stringency. The bidirectional reflectance distribution function (BRDF) model-fitting root mean square error (RMSE) value was used as one of the criteria to determine if the AGB estimates were reasonable. This dataset is the first example of forest AGB estimation based on a multi-angle index applied using MISR data.

Resources

Name Format Description Link
Get a related visualization PNG Generalized 2005 aboveground biomass (AGB) map for the southwestern U.S, derived from the Multiangle Imaging Spectro-Radiometer (MISR). Open resource
View documentation related to this dataset HTML ORNL DAAC Data Set Documentation Open resource
Download this dataset HTML This link allows direct data access via Earthdata login Open resource
View documentation related to this dataset PDF Forest Aboveground Biomass for the Southwestern U.S. from MISR, 2000-2021: MISR_Forest_AGB_SW_US.pdf Open resource
This dataset's landing page HTML Data set Landing Page DOI URL Open resource
Google Scholar search results HTML Search results for publications that cite this dataset by its DOI. Open resource

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