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Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning

文献类型： 外文期刊

作者： Chen, Xinxin 1 ; Jiang, Kang 1 ; Zhu, Yushi 1 ; Wang, Xiangjun 2 ; Yun, Ting 1 ;

作者机构： 1.Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China

2.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Hainan, Peoples R China

3.Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China

关键词： deep learning; individual tree crown segmentation; Airborne LiDAR data; computer graphics

期刊名称：FORESTS （ 影响因子：2.633； 五年影响因子：2.804 ）

ISSN：

年卷期： 2021 年 12 卷 2 期

页码：

收录情况： SCI

摘要： Accurate individual tree crown (ITC) segmentation from scanned point clouds is a fundamental task in forest biomass monitoring and forest ecology management. Light detection and ranging (LiDAR) as a mainstream tool for forest survey is advancing the pattern of forest data acquisition. In this study, we performed a novel deep learning framework directly processing the forest point clouds belonging to the four forest types (i.e., the nursery base, the monastery garden, the mixed forest, and the defoliated forest) to realize the ITC segmentation. The specific steps of our approach were as follows: first, a voxelization strategy was conducted to subdivide the collected point clouds with various tree species from various forest types into many voxels. These voxels containing point clouds were taken as training samples for the PointNet deep learning framework to identify the tree crowns at the voxel scale. Second, based on the initial segmentation results, we used the height-related gradient information to accurately depict the boundaries of each tree crown. Meanwhile, the retrieved tree crown breadths of individual trees were compared with field measurements to verify the effectiveness of our approach. Among the four forest types, our results revealed the best performance for the nursery base (tree crown detection rate r = 0.90; crown breadth estimation R-2 > 0.94 and root mean squared error (RMSE) < 0.2m). A sound performance was also achieved for the monastery garden and mixed forest, which had complex forest structures, complicated intersections of branches and different building types, with r = 0.85, R-2 > 0.88 and RMSE < 0.6 m for the monastery garden and r = 0.80, R-2 > 0.85 and RMSE < 0.8 m for the mixed forest. For the fourth forest plot type with the distribution of crown defoliation across the woodland, we achieved the performance with r = 0.82, R-2 > 0.79 and RMSE < 0.7 m. Our method presents a robust framework inspired by the deep learning technology and computer graphics theory that solves the ITC segmentation problem and retrieves forest parameters under various forest conditions.

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