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Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

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 e...

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Detalles Bibliográficos
Autores principales: Huang, Wenli, Swatantran, Anu, Johnson, Kristofer, Duncanson, Laura, Tang, Hao, O’Neil Dunne, Jarlath, Hurtt, George, Dubayah, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537504/
https://www.ncbi.nlm.nih.gov/pubmed/26294932
http://dx.doi.org/10.1186/s13021-015-0030-9
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13021-015-0030-9) contains supplementary material, which is available to authorized users.