文章基本信息

标题：Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management
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作者：Yongyan Zhu ; Seongwoo Jeon ; Hyunchan Sung 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2020
卷号：12
期号：23
页码：10150
DOI：10.3390/su122310150
语种：English
出版社：MDPI, Open Access Journal
摘要：Forest spatial information is regularly established and managed as basic data for national forest planning and forest policy establishment. Among them, the grade of vegetation conservation shall be investigated and evaluated according to the value of vegetation conservation. As the collection of field data over large or remote areas is difficult, unmanned aerial vehicles (UAVs) are increasingly being used for this purpose. Consequently, there is a need for research on UAV-monitoring and three-dimensional (3D) image generation techniques. In this study, a new method that can efficiently collect and analyze UAV spatial data to survey and assess forests was developed. Both UAV-based and LiDAR imaging methods were evaluated in conjunction with the ground control point measurement method for forest surveys. In addition, by fusing the field survey database of each target site and the UAV optical and LiDAR images, the Gongju, Samcheok, and Seogwipo regions were analyzed based on deep learning. The kappa value showed 0.59, 0.47, and 0.78 accuracy for each of the sites in terms of vegetation type (artificial or natural), and 0.68, 0.53, and 0.62 accuracy in terms of vegetation layer structure. The results of comparative analysis with ecological natural maps by establishing vegetation conservation levels show that about 83.9% of the areas are consistent. The findings verified the applicability of this UAV-based approach for the construction of geospatial information on forests. The proposed method can be useful for improving the efficiency of the Vegetation Conservation Classification system and for conducting high-resolution monitoring in forests worldwide.
关键词：unmanned aerial vehicles;LiDAR;CNNs;forest assessments;vegetation conservation classification