Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data  

作　　者：Wuming Zhang Shangshu Cai Xinlian Liang Jie Shao Ronghai Hu Sisi Yu Guangjian Yan 

机构地区：[1]State Key Laboratory of Remote Sensing Science,Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences,Beijing Engineering Research Center for Global Land Remote Sensing Products,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China [2]Department of Remote Sensing and Photogrammetry,Finnish Geospatial Research Institute,02431 Masala,Finland [3]College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China [4]ICube Laboratory,UMR 7357 CNRS-University of Strasbourg,300 bd Sebastien Brant,CS,10413,F-67412 Illkirch Cedex,France [5]Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China [6]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Forest Ecosystems》2020年第1期1-13,共13页森林生态系统（英文版）

基　　金：the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265);the National Key Research and Development Program of China(No.2016YFB0501404).

摘　　要：Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

关 键 词：Data PITS Tree CROWN CANOPY height MODELS CLOTH simulation Pit-free 

分 类 号：S771.8[农业科学—森林工程]