文献类型：期刊文献

英文题名：China's larch stock volume estimation using Sentinel-2 and LiDAR data

作者：Yu, Tao[1,2] Pang, Yong[1,2] Liang, Xiaojun[1,2] Jia, Wen[1,2] Bai, Yu[1,2] Fan, Yilin[1,2] Chen, Dongsheng[3] Liu, Xianzhao[1] Deng, Guang[1,2] Li, Chonggui[4] Sun, Xiangnan[1,2,5] Zhang, Zhidong[6] Jia, Weiwei[7] Zhao, Zhonghua[3] Wang, Xiao[8]

第一作者：Yu, Tao

通信作者：Pang, Y[1];Pang, Y[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat, State Forestry Adm, Beijing, Peoples R China;[4]Xian Univ Sci & Technol, Coll Geomat, Xian, Peoples R China;[5]Natl Forestry & Grassland Adm, Acad Inventory & Planning, Beijing, Peoples R China;[6]Hebei Agr Univ, Coll Forestry, Baoding, Peoples R China;[7]Northeast Forestry Univ, Coll Forestry, Harbin, Peoples R China;[8]Chinese Acad Forestry, Ecol & Nat Conservat Inst, Key Lab Forest Ecol & Environm, Natl Forestry & Grassland Adm, Beijing, Peoples R China

年份：0

外文期刊名：GEO-SPATIAL INFORMATION SCIENCE

收录：;EI(收录号：20224012841644);Scopus(收录号：2-s2.0-85139108959);WOS:【SCI-EXPANDED(收录号:WOS:000857959400001)】；

基金：This study was funded by the National Key Research and Development Program of China (grant number: 2017YFD0600404); National Natural Science Foundation of China (grant number: 41871278 & 32071759) and forest parameter inversion by integrating LiDAR and multiple angle optical data for the terrestrial ecosystem carbon inventory satellite (2016K-10 & YGD-202100105737-006-001).

语种：英文

外文关键词：Sentinel-2; LiDAR; stock volume; larch; validation

摘要：Forest Stock Volume (FSV) is one of the key indicators in forestry resource investigation and management on local, regional, and national scales. Limited by the saturation problems of optical satellite remote-sensing imagery in the retrieving of stock volume, and the high cost of Light Detection And Ranging (LiDAR) data, it is still challenging to estimate FSV in a large area using single-sensor remote-sensing data. In this paper, a method integrated multispectral satellite imagery and LiDAR data was developed to map stock volume in a large area. A random forest model was adopted to estimate the stock volume of larch forest in China based on the training samples from the Airborne Laser Scanning (ALS)-derived stock volume and corresponding Sentinel-2 imagery. Validation using National Forest Inventory (NFI) data, ALS-derived stock volume and ground investigation data demonstrated that the estimated stock volume had a high accuracy (R-2 = 0.59, RMSE = 59.69 m(3)/ha, MD = 39.96 m(3)/ha when validated with NFI data; R-2 ranged from 0.77 to 0.85, RMSE ranged from 38.68 m(3)/ha to 67.38 m(3)/ha, MD ranged from 24.90 m(3)/ha to 37.27 m(3)/ha when validated with ALS stock volume; R-2 = 0.42, RMSE = 79.10 m(3)/ha, MD = 62.06 m(3)/ha when validated with field investigation data). Results of this paper indicated the applicability of estimating stock volume of larch forest in a large area by combining Sentinel-2 data and airborne LiDAR data.