期刊
REMOTE SENSING
卷 11, 期 23, 页码 -出版社
MDPI
DOI: 10.3390/rs11232880
关键词
individual segmentation method; leaf area index; canopy cover; tree density; coefficient of variation of tree height
类别
资金
- Key Deployment Project of the Chinese Academy of Sciences [KFZD-SW-319-06]
- National Key R&D Program of China [2017YFC0503905]
- CAS Pioneer Hundred Talents Program
- Provincial Key Technology Research and Development Program of Sichuan Ministry of Natural Resources for Ecological Geohazard Prevention and Mitigation in the 8.8 Jiuzhaigou Earthquake Area [KJ-2018-21]
- Provincial Key R&D Program of the Sichuan Ministry of Science and Technology [2019YFS0074]
This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.
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