基于无人机影像的银杏单木胸径预估方法

胸径是立木测定的基本因子，自动获取胸径数据是准确高效计算森林蓄积量和生物量的关键。以银杏Ginkgo biloba为研究对象，通过无人机获得影像数据，利用运动恢复结构（SFM）方法生成数字表面模型和正射影像图，进而提取单株银杏的树冠面积（A_c），冠幅（W_c）及树高（H）。3个参数分别与胸径（D_BH）建立一元回归模型（A_c-D_BH，W_c-D_BH，H-D_BH），二元回归模型（A_c&W_c-D_BH，A_c&H-D_BH，W_c&H-D_BH）和三元回归模型（A_c&W_c&H-D_BH）。52组拟合样本的结果显示:A_c&W_c&H-D_BH模型的决定系数（R2）最高为0.825 0，均方根误差（E_RMS）最小为0.959 1。19组检测样本的结果显示:A_c&W_c&H-D_BH模型反演的胸径值误差率为4.20%，小于A类森林资源胸径因子允许的误差值（5%）。研究结果表明:通过无人机采集树冠面积、冠幅和树高3个参数，可计算得到较高精度的胸径值。

Predicting DBH of a single Ginkgo biloba tree based on UAV images

To efficiently calculate and predict forest stock and biomass, diameter at breast height (D_BH), a basic factor of a tree, was used in a regression model. In this study, Ginkgo biloba was used as the research object. Image data was obtained with an unmanned aerial vehicle (UAV), and using the method of structure from motion (SFM), a digital surface model and an orthophoto map were generated. Next, the canopy area (A_C), crown width (W_C) and tree height (H) of G. biloba were extracted. Then, one-way regression models (A_C-D_BH, WC-D_BH, H-D_BH), binary regression models (A_C&W_C-D_BH, A_C&H-D_BH, W_C&H-D_BH), and a ternary regression model (A_C&W_C&H-D_BH) were established. Results of 52 groups of fitted samples showed that the A_C&W_C&H-D_BH model had the highest coefficient of determination (R2 = 0.825 0) and the lowest root mean square error (E_RMS = 0.959 1). Results of 19 groups of test samples showed that the D_BH error rate for the A_C&W_C&H-D_BH model was 4.20%, which was less than the allowable error value (5%) for the A-type forest resource D_BH factor. Thus, a high precision D_BH value could be calculated using the three parameters of canopy area, crown width, and tree height, thereby providing a new idea for automated forest resource surveying and monitoring.