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在分析现存点云处理方法的特性后,通过改进三角网构网算法的算法机制,提出了一种基于空间分割的分块优先级机制的三角网表面重建算法,用于重构树冠表面,实现树冠体积的准确提取.通过可视化方法对比了多种算法的点云构网效果,以实验区选定的30棵树为研究对象,利用T-LiDAR获取树冠点云数据,通过人工方法、传统算法和本文的改进算法计算树冠体积,对这些结果进行了对比分析.分析发现:四种方法之间均显示出较好的相关性(R2>=0.831),其中所提出的改进Delaunay方法拥有理想的精度,较好稳定性和最少的耗费时间.实验结果表明,提出的算法在点云(尤其是T-LiDAR数据)树冠的体积提取中具有很大的优势.结合T-LiDAR数据还可以实现树冠表面积和生物量等树冠因子的高精度快速提取.
After analyzing the characteristics of existing point cloud processing methods, this paper proposes a triangulation mesh surface reconstruction algorithm based on spatial partitioning and block prioritization mechanism, which is used to reconstruct the crown surface and achieve Tree crown volume extraction.Compared with the visual effects of many algorithms, the 30 trees selected in the experimental area were selected as research objects, and the data of crown point cloud were obtained by T-LiDAR. The artificial algorithm, the traditional algorithm And the improved algorithm in this paper, the crown volume was calculated and the results were compared and analyzed.It was found that there was a good correlation between the four methods (R2> = 0.831), and the proposed improved Delaunay method possessed the ideal Accuracy, good stability and least time-consuming.The experimental results show that the proposed algorithm has a great advantage in the volumetric extraction of the canopy (especially the T-LiDAR data) canopy.Combination of T-LiDAR data also can achieve the crown Surface area and biomass and other canopy factor high-precision rapid extraction.