Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests
Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests
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DOI：
10.3390/rs12172865
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
K. Moe;T. Owari;Naoyuki Furuya;T. Hiroshima;J. Morimoto
中科院分区：
文献类型：
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作者：
K. Moe;T. Owari;Naoyuki Furuya;T. Hiroshima;J. Morimoto
高价值材种在森林经营中具有重要的经济作用。这些树种的个别树木资料对于实际的森林管理和养护是必要的。无人机数字航空摄影测量（UAV-DAP）可以提供精细的空间和光谱信息，以及森林冠层的三维（3D）结构信息。光探测和测距（LiDAR）数据可以进行区域范围的3D树木测绘，并提供准确的森林地面地形信息。在这项研究中，我们评估了UAV-DAP和激光雷达数据的潜在用途，用于估计北方日本混交林中大型高价值木材树种的单株树位置和胸径（DBH）值。我们进行多分辨率分割的UAV-DAP正射影像，以获得个人的树冠。我们使用基于对象的图像分析和随机森林算法将森林冠层分为五类：三个高价值的木材物种，其他阔叶树种，针叶树种。的UAV-DAP技术产生的总体精度值为73%和63%的森林冠层的分类在两个森林管理子舱。此外，我们还通过野外调查、LiDAR和UAV-DAP数据估算了高价值用材树种的单株胸径值。结果表明，UAV-DAP可以预测单木胸径值，与使用现场和LiDAR数据的胸径预测精度相当。本研究的结果是有用的森林管理者在寻找高价值的木材树种和估计树木大小在大型混交林，并可应用于高价值的木材树种的单木管理系统。
High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.