文章基本信息

标题：Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
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作者：Wenli Huang ; Anu Swatantran ; Kristofer Johnson 等
期刊名称：Carbon Balance and Management
印刷版ISSN：1750-0680
电子版ISSN：1750-0680
出版年度：2015
卷号：10
期号：1
页码：19-34
DOI：10.1186/s13021-015-0030-9
出版社：BioMed Central
摘要：Background 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. Results Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 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 (33.0–54.6 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. Conclusions 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.
关键词：Temperate deciduous forest; Lidar; Aboveground biomass; Carbon