Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

doi:10.3390/rs8070565

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

	Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data
	Hu, Tianyu ; Su, Yanjun 3; Xue, Baolin ; Liu, Jin ; Zhao, Xiaoqian ; Fang, Jingyun 2,4; Guo, Qinghua 3
刊名	REMOTE SENSING
	2016
卷号	8 期号:7
关键词	global forest aboveground biomass remote sensing LiDAR
DOI	10.3390/rs8070565
文献子类	Article
英文摘要	As a large carbon pool, global forest ecosystems are a critical component of the global carbon cycle. Accurate estimations of global forest aboveground biomass (AGB) can improve the understanding of global carbon dynamics and help to quantify anthropogenic carbon emissions. Light detection and ranging (LiDAR) techniques have been proven that can accurately capture both horizontal and vertical forest structures and increase the accuracy of forest AGB estimation. In this study, we mapped the global forest AGB density at a 1-km resolution through the integration of ground inventory data, optical imagery, Geoscience Laser Altimeter System/Ice, Cloud, and Land Elevation Satellite data, climate surfaces, and topographic data. Over 4000 ground inventory records were collected from published literatures to train the forest AGB estimation model and validate the resulting global forest AGB product. Our wall-to-wall global forest AGB map showed that the global forest AGB density was 210.09 Mg/ha on average, with a standard deviation of 109.31 Mg/ha. At the continental level, Africa (333.34 +/- 63.80 Mg/ha) and South America (301.68 +/- 67.43 Mg/ha) had higher AGB density. The AGB density in Asia, North America and Europe were 172.28 +/- 94.75, 166.48 +/- 84.97, and 132.97 +/- 50.70 Mg/ha, respectively. The wall-to-wall forest AGB map was evaluated at plot level using independent plot measurements. The adjusted coefficient of determination (R-2) and root-mean-square error (RMSE) between our predicted results and the validation plots were 0.56 and 87.53 Mg/ha, respectively. At the ecological zone level, the R-2 and RMSE between our map and Intergovernmental Panel on Climate Change suggested values were 0.56 and 101.21 Mg/ha, respectively. Moreover, a comprehensive comparison was also conducted between our forest AGB map and other published regional AGB products. Overall, our forest AGB map showed good agreements with these regional AGB products, but some of the regional AGB products tended to underestimate forest AGB density.
学科主题	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	2072-4292
出版地	BASEL
WOS关键词	TROPICAL RAIN-FOREST ; SMALL-FOOTPRINT LIDAR ; CARBON STOCKS ; RADAR BACKSCATTER ; SATELLITE LIDAR ; BIOSPHERE MODEL ; AIRBORNE LIDAR ; BOREAL FOREST ; GROUND PLOTS ; MODIS
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
语种	英语
出版者	MDPI
WOS记录号	WOS:000382224800035
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/2S10CLM1/24517]
专题	植被与环境变化国家重点实验室
作者单位	1.Peking Univ, Minist Educ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China 2.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95343 USA 3.Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, Beijing 100093, Peoples R China 4.Peking Univ, Coll Urban & Environm Sci, Dept Ecol, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Hu, Tianyu,Su, Yanjun,Xue, Baolin,et al. Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data[J]. REMOTE SENSING,2016,8(7).
APA	Hu, Tianyu.,Su, Yanjun.,Xue, Baolin.,Liu, Jin.,Zhao, Xiaoqian.,...&Guo, Qinghua.(2016).Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data.REMOTE SENSING,8(7).
MLA	Hu, Tianyu,et al."Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data".REMOTE SENSING 8.7(2016).