无人机密集匹配点云与机载激光雷达点云的差异分析

为分析无人机密集匹配点云和机载激光雷达点云的异同性，对密集林分（郁闭度０８５）、稀疏林分（郁闭度０５５）和未成林地的２种点云的空间分布进行目视对比分析，并通过２种点云生产的ＤＥＭ（ＵＡＶ＿ＤＥＭ和ＬｉＤＡＲ＿ＤＥＭ）分别对密集匹配点云进行归一化处理，得到２套归一化密集匹配点云数据，将其与激光雷达点云进行统计特征参数配对样本ｔ检验分析。在森林调查监测中，无人机密集匹配点云可直接用于稀疏林分和未成林地的森林参数估测，在既有高精度ＤＥＭ支持下可对密集林分的一些林分参数（如冠层表面高度等）进行估测。To analyze the similarities and differences between UAV dense matching point clouds and airborne LiDAR point clouds, a visual comparative analysis was conducted on the spatial distribution of the two types of point clouds in dense stands (canopy density 0.85), sparse stands (canopy density 0.55), and non-stocked forest land. Moreover, the dense matching point clouds were normalized separately using DEMs generated from the two types of point clouds (UAV_DEM and LiDAR_DEM), resulting in two sets of normalized dense matching point cloud datasets. These datasets were then compared with the LiDAR point clouds via paired-sample t-test analysis of their statistical characteristic parameters. In forest inventory and monitoring, UAV dense matching point clouds can be directly used to estimate forest parameters in sparse stands and non-stocked forest land; with the support of high-precision DEMs, they can also be applied to estimate certain stand parameters of dense stands, such as canopy surface height.

搜集汇总

背景与挑战

背景概述

该数据集通过对比无人机密集匹配点云和机载激光雷达点云的空间分布及统计特征，分析了它们在森林调查监测中的异同性，为稀疏林分和未成林地的森林参数估测提供了直接支持，并在高精度DEM支持下可用于密集林分的冠层表面高度等参数估测。