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Estimating forest and woodland aboveground biomass using active and passive remote sensing

May 1, 2016

Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

Citation Information

Publication Year 2016
Title Estimating forest and woodland aboveground biomass using active and passive remote sensing
DOI 10.14358/PERS.82.4.271
Authors Zhuoting Wu, Dennis G. Dye, John M. Vogel, Barry R. Middleton
Publication Type Article
Publication Subtype Journal Article
Series Title Photogrammetric Engineering and Remote Sensing
Series Number
Index ID 70159049
Record Source USGS Publications Warehouse
USGS Organization Western Geographic Science Center