KMS Institute Of Geographic Sciences And Natural Resources Research,CAS
| 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
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| 2019-08-23 | |
| Source Publication | GEOCARTO INTERNATIONAL
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| ISSN | 1010-6049 |
| Pages | 16 |
| Corresponding Author | Wang, Meng(wangmeng@qfnu.edu.cn) |
| Abstract | 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. |
| Keyword | Aboveground biomass high accuracy surface modelling mapping |
| DOI | 10.1080/10106049.2019.1655799 |
| WOS Keyword | TROPICAL FOREST ; SPATIAL-DISTRIBUTION ; VEGETATION ; PREDICTION ; IMAGERY ; LIDAR ; UNCERTAINTY ; COMBINATION ; STOCK ; GLAS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000484060700001 |
| Publisher | TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/69636 |
| Collection | 中国科学院地理科学与资源研究所 |
| Corresponding Author | Wang, Meng |
| Affiliation | 1.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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