Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

doi:10.1080/10106049.2019.1655799

	Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data
	Sun, Xiaofang 2; Li, Bai 3; Du, Zhengping 4; Li, Guicai 1; Fan, Zemeng 4; Wang, Meng 2; Yue, Tianxiang 4
刊名	GEOCARTO INTERNATIONAL
	2019-08-23
页码	16
关键词	Aboveground biomass high accuracy surface modelling mapping
ISSN号	1010-6049
DOI	10.1080/10106049.2019.1655799
通讯作者	Wang, Meng(wangmeng@qfnu.edu.cn)
英文摘要	An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R-2). A forest AGB map of the study area was generated using the optimal model.
资助项目	National Key Research and Development Program of China[2016YFA0600204] ; National Natural Science Foundation of China[41501428] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41590844] ; National Natural Science Foundation of China[41371400] ; Natural Science Foundation of Shandong Province, China[ZR2017BD010]
WOS关键词	TROPICAL FOREST ; SPATIAL-DISTRIBUTION ; VEGETATION ; PREDICTION ; IMAGERY ; LIDAR ; UNCERTAINTY ; COMBINATION ; STOCK ; GLAS
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000484060700001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/69635]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Meng
作者单位	1.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing, Peoples R China 2.Qufu Normal Univ, Coll Geog & Tourism, Rizhao, Peoples R China 3.Forestry Bur Suining Cty, Shaoyang, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Sun, Xiaofang,Li, Bai,Du, Zhengping,et al. Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data[J]. GEOCARTO INTERNATIONAL,2019:16.
APA	Sun, Xiaofang.,Li, Bai.,Du, Zhengping.,Li, Guicai.,Fan, Zemeng.,...&Yue, Tianxiang.(2019).Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data.GEOCARTO INTERNATIONAL,16.
MLA	Sun, Xiaofang,et al."Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data".GEOCARTO INTERNATIONAL (2019):16.