Dennis G. Dye
John M. Vogel
Barry R. Middleton
Zhuoting Wu
2016
<p><span>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 (R</span><span>2</span><span> = 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</span><i>Mg ha</i><span> </span><span>–1</span><span> across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 </span><i>Mg ha</i><span> </span><span>–1</span><span>. 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.</span></p>
application/pdf
10.14358/PERS.82.4.271
en
Ingenta
Estimating forest and woodland aboveground biomass using active and passive remote sensing
article