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首页> 外文会议>Mexican conference on pattern Recognition >A Segmentation Method for Tree Crown Detection and Modelling from LiDAR Measurements
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A Segmentation Method for Tree Crown Detection and Modelling from LiDAR Measurements

机译：基于LiDAR测量的树冠检测和建模分割方法

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A watershed segmentation algorithm is proposed for automatic extraction of tree crowns from LiDAR data to support 3-d modelling of forest stands. A relatively sparse LiDAR point cloud was converted to a surface elevation in raster format and a canopy height model (CHM) extracted. Then, the segmentation method was applied on the CHM and results combined with the original point cloud to generate models of individual tree crowns. The method was tested in 200 circular plots (400 ro2) located over 50 sites of a conservation area in Mexico City. The segmentation method exhibited a moderate to perfect detection rate on 66% of plots tested. One major factor for a poor detection was identified as the relatively low sampling rate of LiDAR data with respect to crown sizes.
机译：提出了一种分水岭分割算法，用于从LiDAR数据中自动提取树冠，以支持林分的3维建模。将相对稀疏的LiDAR点云转换为栅格格式的表面高程，并提取了冠层高度模型（CHM）。然后，将分割方法应用于CHM，并将结果与​​原始点云组合以生成单个树冠的模型。该方法在墨西哥城一个保护区的50个地点的200个圆形样地（400 ro2）中进行了测试。分割方法在66％的测试地块上显示出中等至完美的检测率。识别不佳的一个主要因素是相对于牙冠尺寸而言，相对较低的LiDAR数据采样率。

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