中国科学院水利部水土保持研究所机构知识库

定期的森林结构参数提取是监测评价造林质量的重要方式，对林业规划有着重要的指导意义。基于大疆四旋翼无人机搭载RGB高清相机获取了陕北人工杨树林的高分影像，通过多尺度分割和面向对象提取、点云分类、以及Leica Cyclone中的点云数据图模块处理，实现了陕北黄土高原地区人工杨树林森林结构参数（冠幅、树高）、冠层高度的提取。其中冠幅盖度影像总体分类精度为96%，Kappa系数为0.74。结合实测数据进行验证，东西和南北两个方向冠幅径长的均值回归拟合后的R²达到了0.95，提取的单木树高值与实测值之间具有明显的线性关系，其决定系数约R²=0.80。发现基于RGB相机产生的点云数据可直接获取林地冠层高度模型，但其精度和分辨率较为粗糙，不适合进一步的单木树高提取。研究结果表明，基于无人机高分遥感影像的森林结构参数提取方法高效可靠，适用陕北地区人工林以及林分结构较为单一地区。

 [Background] Regular extraction of forest structure parameters is an important way to monitor and evaluate the quality of afforestation. Northern Shaanxi is one of the main afforestation areas of the Three-North Shelterbelt Project in China. Its artificial forest has the characteristics of low canopy density, large plant spacing and sparse distribution, which provides favorable conditions for field operation and later image processing of UAV. [Methods] Based on DJI's unmanned aerial vehicle (UAV) carrying RGB（red，green and blue） high-definition camera, this work obtained high-resolution images of the artificial poplar forest in northern Shaanxi on September 21, 2019. Basic data such as DOM（digital orthophoto model）, DEM （digital elevation model）and digital point cloud in the study area were obtained by Photoscan image mosaic. Then, with eCognition Developer 6.4's multi-scale segmentation and object-oriented functionality around the DOM of the research area, the single canopy area and diameter length were obtained. CHM（canopy height model）of the study area was obtained by automatically classifying the directly generated point cloud data through Photoscan Pro and calculating based on ArcGIS 10.3.Concurrently, the point cloud data graph module in Leica Cyclone was used to visually interpret the point cloud data generated by high-resolution images and extract the height of a single tree. The method realizes the automatic and rapid extraction of forest structure parameters (crown width, tree height) and CHM of artificial poplar forest in the Loess Plateau area of northern Shaanxi. [Results]The overall classification accuracy of canopy coverage images is 96%, and the Kappa coefficient is 0.74. The R² between the measured east-west crown amplitude and the extracted value is 0.90, and the R² between the measured south-north crown amplitude and the extracted value is 0.91.Combining with the measured data for verification, R² after mean regression fitting of crown width length in the east-west directions and the north-south directions reach 0.95. There is an obvious linear relationship between the extracted height of a single tree and the measured value, and its determination coefficient R²=0.80. The CHM generates after the automatic classification of point cloud data is 82.64 m, and even after the geometric calculation of DSM (digital surface model) and DEM, the CHM height is still 15.83 m. It is found that point cloud data generated by RGB camera can directly obtain the CHM, but its accuracy and resolution are relatively rough, which is not suitable for further extraction of single tree height.[Conclusions] The extraction method of forest structure parameter based on high resolution remote sensing image of UAV is efficient and reliable, and is suitable for artificial forests and areas with relatively single stand structure in northern Shaanxi.