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.
|Title||Estimating forest and woodland aboveground biomass using active and passive remote sensing|
|Authors||Zhuoting Wu, Dennis G. Dye, John M. Vogel, Barry R. Middleton|
|Publication Subtype||Journal Article|
|Series Title||Photogrammetric Engineering and Remote Sensing|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Western Geographic Science Center|