文献类型：期刊文献

英文题名：Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

作者：Gu, Chengyan[1,2] Clevers, Jan G. P. W.[2] Liu, Xiao[3] Tian, Xin[1] Li, Zhouyuan[4] Li, Zengyuan[1]

第一作者：Gu, Chengyan

通信作者：Li, ZY[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, NL-6700 AA Wageningen, Netherlands;[3]Eindhoven Univ Technol, Elect Engn Dept, NL-5600 MB Eindhoven, Netherlands;[4]Wageningen Univ & Res, Water Syst & Global Change Grp, NL-6700 AA Wageningen, Netherlands

年份：2018

卷号：137

起止页码：97-111

外文期刊名：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

收录：;EI(收录号：20180604777393);Scopus(收录号：2-s2.0-85041400914);WOS:【SCI-EXPANDED(收录号:WOS:000427313700008)】；

基金：The first author would like to thank the China Scholarship Council (CSC) for the fellowship support during her study at Wageningen University & Research, The Netherlands. This work was supported by the National Basic Research Program of China (973 Program) [grant number 2013CB733404] and the Fundamental Research Funds for the Central Non-profit Research Academy of Forestry [grant number CAFYBB2017QC005]. Thanks to the Forest Inventory and Planning Institute of China for providing the national first-class forest inventory data.

语种：英文

外文关键词：Forest height; Geometric-Optical Model for Sloping; Terrains (GOST); Airborne LiDAR; Landsat

摘要：Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (COST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image end members and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the COST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the COST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.