期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2017
卷号：IV-4/W2
页码：3-6
出版社：Copernicus Publications
摘要：Canopy height model(CHM) and tree mean height are critical forestry parameters that many other parameters such as growth, carbon sequestration, standing timber volume, and biomass can be derived from. LiDAR is a new method used to rapidly estimate these parameters over large areas. The estimation of these parameters has been derived successfully from CHM. However, a number of challenges limit the accurate retrieval of tree height and crowns, especially in tropical forest area. In this study, an improved canopy estimation model is proposed based on dynamic moving window that applied on LiDAR point cloud data. DEM, DSM and CHM of large tropical forest area can be derived from LiDAR data effectively and efficiently.
关键词：Canopy Height Model; CHM; LiDAR; Tree Mean Height
