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• W3122830743 abstract "Accurate segmentation of individual tree crowns (ITCs) from airborne light detection and ranging (LiDAR) data remains a challenge for forest inventories. Although many ITC segmentation methods have been developed to derive tree crown information from airborne LiDAR data, these algorithms contain uncertainty in processing false treetops because of foliage clumps and lateral branches, overlapping canopies without clear valley-shape areas, and sub-canopy crowns with neighbouring trees that obscure their shapes from an aerial perspective. Here, we propose an approach to crown segmentation using computer vision theories applied in different forest types. First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied for accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach in the subtropical forests within China. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marker-controlled watershed method, especially in complicated intersections of multiple crowns. A high performance was demonstrated for three pure Eucalyptus plots (a treetop detection rate r ≥ 0.95 and crown width estimation R 2 ≥ 0.90 for canopy trees; r ≥ 0.85 and R 2 ≥ 0.88 for sub-canopy trees) and three plots dominated by Chinese fir ( r ≥ 0.95 and R 2 ≥ 0.87 for canopy trees; r ≥ 0.79 and R 2 ≥ 0.83 for sub-canopy trees). Finally, in a relatively complex forest park containing a wide range of tree species and sizes, a high performance was also achieved ( r = 0.93 and R 2 ≥ 0.85 for canopy trees; r = 0.70 and R 2 ≥ 0.80 for sub-canopy trees). Our method demonstrates that methods inspired by the computer vision theory can improve on existing approaches, providing the potential for accurate crown segmentation even in mixed forests with complex structures • Computer vision theory was applied to individual tree crown segmentation. • Dual Gaussian filters yielded a better performance in treetop detection with a DSM. • The energy function controlled the water expansion in error-prone regions. • The reliability of our method was verified with field data of various forest plots." @default.
• W3122830743 created "2021-02-01" @default.
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• W3122830743 date "2021-04-01" @default.
• W3122830743 modified "2023-10-03" @default.
• W3122830743 title "Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach" @default.
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• W3122830743 doi "https://doi.org/10.1016/j.rse.2021.112307" @default.
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