Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests
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DOI：
10.3390/f8090343
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发表时间：
2017-09-01
期刊：
影响因子：
2.9
通讯作者：
Yoshida, Shigejiro
中科院分区：
文献类型：
--
作者：
Ota, Tetsuji;Ogawa, Miyuki;Yoshida, Shigejiro
在这里，我们研究了轻型无人机（UAV）摄影测量点云在估算日本温带针叶林管理森林生物物理特性方面的能力，以及光谱信息在估算中的重要性。我们估算了林分体积(V)、Lorey’s平均高度（HL）、平均高度（HA）和最大高度（HM） 4个生物物理特性。我们开发了三个独立的变量集，包括一个高度变量，一个光谱变量，以及一个高度和光谱的组合变量。在上述数据集上添加优势树类型也进行了测试。包括高度变量和优势树型的模型对所有生物物理性质的估计都是最好的。V、HL、HA和HM的最佳模型均方根误差（rmse）分别为118.30、1.13、1.24和1.24。仅包含高度变量的模型产生了第二高的精度。rmse分别为131.74、1.21、1.31、1.32。仅包含光谱变量的模型比包含高度变量的模型的估计精度低得多。因此，轻型无人机摄影测量点云可以准确估计森林生物物理性质，而不一定需要光谱变量。优势树型提高了估计精度。
Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey's mean height (HL), mean height (HA), and max height (HM). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, HL, HA, and HM, were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy.