Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
| dc.contributor.author | Huang, Wenli | |
| dc.contributor.author | Swatantran, Anu | |
| dc.contributor.author | Johnson, Kristofer | |
| dc.contributor.author | Duncanson, Laura | |
| dc.contributor.author | Tang, Hao | |
| dc.contributor.author | O’Neil Dunne, Jarlath | |
| dc.contributor.author | Hurtt, George | |
| dc.contributor.author | Dubayah, Ralph | |
| dc.date.accessioned | 2021-08-17T18:35:32Z | |
| dc.date.available | 2021-08-17T18:35:32Z | |
| dc.date.issued | 2015-08-16 | |
| dc.description.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 48.5–92.7 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. 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. | en_US |
| dc.description.uri | https://doi.org/10.1186/s13021-015-0030-9 | |
| dc.identifier | https://doi.org/10.13016/pzem-qf5d | |
| dc.identifier.citation | Huang, W., Swatantran, A., Johnson, K. et al. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance Manage 10, 19 (2015) | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/27624 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.relation.isAvailableAt | College of Behavioral & Social Sciences | en_us |
| dc.relation.isAvailableAt | Geography | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
| dc.subject | Temperate deciduous forest | en_US |
| dc.subject | Lidar | en_US |
| dc.subject | Aboveground biomass | en_US |
| dc.subject | Carbon | en_US |
| dc.title | Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA | en_US |
| dc.type | Article | en_US |