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

Abstract Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidarand Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodlandecosystems. Compared to field measurements, airborne lidarenabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 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 14 Mg ha −1 across all woodlandand forest species. Land-sat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha −1 . Thus, airborne lidarallowed 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.