文献类型：期刊文献

英文题名：Forest stand delineation using airborne LiDAR and hyperspectral data

作者：Xiong, Hao[1,2,3] Pang, Yong[1,2] Jia, Wen[1,2] Bai, Yu[1,2]

第一作者：Xiong, Hao

通信作者：Pang, Y[1];Pang, Y[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Sun Yat sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China

年份：2024

卷号：58

期号：2

外文期刊名：SILVA FENNICA

收录：;EI(收录号：20241215775998);Scopus(收录号：2-s2.0-85187882109);WOS:【SCI-EXPANDED(收录号:WOS:001199996200002)】；

基金：This research was funded by National Key Research and Development Program (2023YFD2200804 & 2017YFD0600404) .

语种：英文

外文关键词：automatic delineation; canopy height model; merge rule; over-segmentation

摘要：Forest stands, crucial for inventory, planning, and management, traditionally rely on timeconsuming visual analysis by forest managers. To enhance efficiency, there is a growing need for automated methods that take into account essential forest attributes. In response, we propose a novel approach utilizing airborne Light Detection and Ranging (LiDAR) and hyperspectral data for automated forest stand delineation. Our approach initiates with over-segmentation of the Canopy Height Model (CHM), followed by attribute calculation for each segment using both CHM and hyperspectral data. Two rules are applied to merge homogeneous segments and eliminate others based on calculated attributes. The effectiveness of our method was validated using three types of reference forest stands with two indices: the explained variance (R2) and Intersection over Union (IoU). Results from our study demonstrated notable accuracy, with a R2 of 97.35% and 97.86% for mean tree height and mean diameter at breast height (DBH), respectively. The R2 for mean canopy height is 81.80%, outperforming manual delineation by 7.31% and multi-scale segmentation results by 2.13%. Furthermore, our approach achieved high IoU values, which indicates a strong spatial agreement with manually delineated forest stands and leading to fewer manual adjustments when applied directly to forest management. In conclusion, our forest stand delineation method enhances both internal consistency and spatial accuracy. This method contributes to improving practical performance and forest management efficiency.