GC11B-1034
Local Discrepancies in Continental Scale Biomass Maps: A Case Study over Forested and Non-Forested Landscapes in Maryland, USA

Monday, 14 December 2015
Poster Hall (Moscone South)
Wenli Huang1, Anu Swatantran2, Kristofer D Johnson3, Laura Duncanson1, Hao Tang1, Jarlath ONeil-Dunne4, George C Hurtt1,2 and Ralph Dubayah1,2, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland, Department of Geographical Sciences, College Park, MD, United States, (3)U.S. Forest Service, Newtown Square, PA, United States, (4)University of Vermont, Rubenstein School of Environment & Natural Resources, Burlington, VT, United States
Abstract:
Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5 Mg·ha-1–92.7 Mg·ha-1).

Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0 Mg·ha-1–54.6 Mg·ha-1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas.

Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest / non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.