IGSNRR OpenIR
Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data
Sun, Xiaofang1; Li, Bai2; Du, Zhengping3; Li, Guicai4; Fan, Zemeng3; Wang, Meng1; Yue, Tianxiang3
2019-08-23
Source PublicationGEOCARTO INTERNATIONAL
ISSN1010-6049
Pages16
Corresponding AuthorWang, Meng(wangmeng@qfnu.edu.cn)
AbstractAn 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.
KeywordAboveground biomass high accuracy surface modelling mapping
DOI10.1080/10106049.2019.1655799
WOS KeywordTROPICAL FOREST ; SPATIAL-DISTRIBUTION ; VEGETATION ; PREDICTION ; IMAGERY ; LIDAR ; UNCERTAINTY ; COMBINATION ; STOCK ; GLAS
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000484060700001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69636
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Meng
Affiliation1.Qufu Normal Univ, Coll Geog & Tourism, Rizhao, Peoples R China
2.Forestry Bur Suining Cty, Shaoyang, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing, Peoples R China
Recommended Citation
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.
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