Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data

doi:10.1016/j.rse.2015.12.002

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data
	Su, Yanjun 2; Guo, Qinghua 2; Xue, Baolin ; Hu, Tianyu ; Alvarez, Otto 2; Tao, Shengli 1,4; Fang, Jingyun 1,4
刊名	REMOTE SENSING OF ENVIRONMENT
	2016
卷号	173
关键词	Forest aboveground biomass GIAS/ICESat Lidar Ground inventory China
ISSN号	0034-4257
DOI	10.1016/j.rse.2015.12.002
文献子类	Article; Proceedings Paper
英文摘要	The global forest ecosystem, which acts as a large carbon sink, plays an important role in modeling the global carbon balance. An accurate estimation of the total forest carbon stock in the aboveground biomass (AGB) is therefore necessary for improving our understanding of carbon dynamics, especially against the background of global climate change. The forest area of China is among the top five globally. However, because of limitations in forest AGB mapping methods and the availability of ground inventory data, there is still a lack in the nationwide wall-to-wall forest AGB estimation map for China. In this study, we collected over 8000 ground inventory records from published literatures, and developed an AGB mapping method using a combination of these ground inventory data, Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, and Land Elevation Satellite (ICESat) data, optical imagery, climate surfaces, and topographic data. An uncertainty field model was introduced into the forest AGB mapping procedure to minimize the influence of plot location uncertainty. Our nationwide wall-to-wall forest AGB mapping results show that the forest AGB density in China is 120 Mg/ha on average, with a standard deviation of 61 Mg/ha. Evaluation with an independent ground inventory dataset showed that our proposed method can accurately map wall-to-wall forest AGB across a large landscape. The adjusted coefficient of determination (R-2) and root-mean-square error between our predicted results and the validation dataset were 0.75 and 42.39 Mg/ha, respectively. This new method and the resulting nationwide wall-to-wall forest AGB map will help to improve the accuracy of carbon dynamic predictions in China. (C) 2015 Elsevier Inc All rights reserved.
学科主题	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	1879-0704
出版地	NEW YORK
WOS关键词	SMALL-FOOTPRINT LIDAR ; LEAF-AREA INDEX ; RADAR BACKSCATTER ; CARBON STORAGE ; CLIMATE-CHANGE ; BOREAL FOREST ; GREEN PROGRAM ; WOODY BIOMASS ; CANOPY HEIGHT ; AIRBORNE
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED) ; Conference Proceedings Citation Index - Science (CPCI-S)
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000369200900016
资助机构	National Key Basic Research Program of ChinaNational Basic Research Program of China [2013CB956604] ; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41471363, 31270563] ; National Science FoundationNational Science Foundation (NSF) [DBI 1356077]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/24557]
专题	植被与环境变化国家重点实验室
作者单位	1.Univ Calif Merced, Sierra Nevada Res Inst, Sch Engn, Merced, CA 95343 USA 2.Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, Beijing 100093, Peoples R China 3.Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China 4.Peking Univ, Dept Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Su, Yanjun,Guo, Qinghua,Xue, Baolin,et al. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data[J]. REMOTE SENSING OF ENVIRONMENT,2016,173.
APA	Su, Yanjun.,Guo, Qinghua.,Xue, Baolin.,Hu, Tianyu.,Alvarez, Otto.,...&Fang, Jingyun.(2016).Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data.REMOTE SENSING OF ENVIRONMENT,173.
MLA	Su, Yanjun,et al."Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data".REMOTE SENSING OF ENVIRONMENT 173(2016).