期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 12, 期 11, 页码 2267-2271出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2466464
关键词
Airborne full-waveform LiDAR; diameter at breast height (DBH); machine learning; prediction; singe trees; stem volume (STV)
类别
资金
- National Natural Science Foundation of China [41001257]
- open fund of the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation [2014NGCM14]
- open fund from Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control [KHK1308]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Using airborne full-waveform LiDAR metrics derived by 3-D tree segmentation, this study estimated single tree's diameter at breast height (DBH) and stem volume (STV). Four regression models were used, including multilinear regression and three up-to-date regression models (i.e., least square boosting trees regression, random forest, and e-support vector regression) from the machine learning field. This study aimed to comparatively evaluate these regression models in predicting DBH and STV at single-tree level and find some clues to regression model's selection. The study sites were located in the Bavarian Forest National Park, Germany, a mixed temperate mountain forest. Our comparisons were performed across different tree species types (coniferous and deciduous) and foliage conditions (leaf-on/leaf-off seasons). The importance of predictor variables was also examined. Experimental results revealed that the best accuracy from machine learning methods outperformed the multilinear model by 1.5 cm for DBH and 0.18 m(3) for STV in terms of rmse. Through comparative analysis, our work provided some clues to the performance variation of regression models for extracting 3-D tree parameters.
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