气候变化领域集成服务门户(CCPortal): High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA

CCPortal

DOI	10.1088/1748-9326/ab2917
	High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA
	Huang W.; Dolan K.; Swatantran A.; Johnson K.; Tang H.; O'Neil-Dunne J.; Dubayah R.; Hurtt G.
发表日期	2019
ISSN	17489318
卷号	14 期号:9
英文摘要	Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over multiple phases of bottom-up monitoring efforts within NASA's Carbon Monitoring Program. Lidar data were collected by different local and federal agencies and provided a wall-to-wall coverage of three states in the USA (Maryland, Pennsylvania and Delaware with a total area of 157 865 km2). We generated a set of standardized forestry metrics from lidar-derived imagery (i.e. canopy height model, CHM) to minimize inconsistency of data quality. We then estimated plot-scale biomass from field data that had the closet acquisition time to lidar data, and linked to lidar metrics using Random Forest models at four USDA Forest Service ecological regions. Additionally, we examined pixel-scale errors using independent field plot measurements across these ecoregions. Collectively, we estimate a total of ∼680 Tg C in aboveground biomass over the Tri-State region (13 DE, 103 MD, 564 PA) circa 2011. A comparison with existing products at pixel-, county-, and state-scale highlighted the contribution of trees over 'non-forested' areas, including urban trees and small patches of trees, an important biomass component largely omitted by previous studies due to insufficient spatial resolution. Our results indicated that integrating field data and low point density (∼1 pt m-2) airborne lidar can generate large-scale aboveground biomass products at an accuracy close to mainstream lidar forestry applications (R 2 = 0.46-0.54, RMSE = 51.4-54.7 Mg ha-1; and R 2 = 0.33-0.61, RMSE = 65.3-100.9 Mg ha-1; independent validation). Local, high-resolution lidar-derived biomass maps such as products from this study, provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale mapping efforts and future development of a national carbon monitoring system. © 2019 The Author(s). Published by IOP Publishing Ltd.
语种	英语
scopus关键词	Biomass; Carbon; Decision trees; Forestry; Machine learning; Mapping; NASA; Optical radar; Pixels; Random forests; Timber; Above ground biomass; Accurate estimation; Canopy Height Models; Forestry applications; High-resolution lidar; High-resolution mapping; Monitoring programs; USDA Forest Service; Monitoring; aboveground biomass; airborne survey; carbon; ecoregion; forest ecosystem; lidar; mapping method; monitoring system; resolution; satellite imagery; Delaware; Maryland; Pennsylvania; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154412
作者单位	Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States; School of Resources and Environmental Sciences, Wuhan University, HUBEI, 430079, China; USDA Forest Service, Northern Research Station, Newtown Square, PA 19073, United States; Forest and Agriculture Organization of the United Nations, Dhaka, Bangladesh; Rubenstein School of the Environment and Natural Resources, University of Vermont, Burlington, VT 05405, United States
推荐引用方式 GB/T 7714	Huang W.,Dolan K.,Swatantran A.,et al. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA[J],2019,14(9).
APA	Huang W..,Dolan K..,Swatantran A..,Johnson K..,Tang H..,...&Hurtt G..(2019).High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA.Environmental Research Letters,14(9).
MLA	Huang W.,et al."High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA".Environmental Research Letters 14.9(2019).