【目的】以长沙县明月村油茶林基地为研究区,探讨利用无人机倾斜摄影提取树冠体积进行油茶树高和产量估测的可行性。【方法】基于无人机正射影像和密集匹配点云,提取波段反射率、植被指数、纹理因子、高度特征等遥感变量和冠幅等冠层参数,同时利用克里金法、反距离权重法、自然邻近点法和过滤三角网法分别获取油茶树冠体积,建立多元线性回归、随机森林、K最邻近模型估测油茶树高和产量,并以地面三维激光点云获取的树冠体积、样地实测树高和产量作为实测值分别对估测结果进行精度评价。【结果】过滤三角网是获取油茶树冠体积最有效的方法,其平均相对误差(31.54%)优于反距离权重法(36.73%)、克里金法(37.04%)和自然邻近点法(38.54%)。将树冠体积作为特征变量参与建模后,树高和产量的多元线性回归、随机森林、K最邻近模型的精度均有所提升(树高相对均方根误差分别减小了3.77%、0.78%、0.64%,产量相对均方根误差分别减小了1.32%、0.34%、0.16%)。对比3种估测模型,随机森林模型的决定系数均优于多元线性回归和K最邻近(树高决定系数分别为0.78、0.51和0.19,产量决定系数分别为0.61、0.48和0.24)。研究发现,分别使用估测树高和实测树高参与产量建模的精度无明显差异。【结论】结合树冠体积和树高参与建模可有效提高油茶产量估测精度,研究结果可为区域范围内利用无人机遥感技术开展油茶树高和产量调查提供参考。

【Objective】In this study, the Camellia oleifera base in Mingyue Village, Changsha County, was used as the study area to explore the feasibility of unmanned aerial vehicle (UAV) oblique photography technology for extracting C. oleifera crown volume and estimating the tree height and yield. 【Method】Remote sensing variables such as band reflectance, vegetation indices, texture factors, height characteristics, and crown parameters were extracted from UAV orthophotos and dense matched point clouds. The Kriging, inverse distance weighting, natural neighbor, and filtered triangulation methods were used to obtain the crown volume of C. oleifera. The multiple linear regression, random forest, and K-nearest-neighbor models were established to estimate the height and yield of C. oleifera, and the accuracy of the estimation results was evaluated using the crown volume obtained from the ground 3D laser point clouds and the actual measured height and yield of the sample plots as the measured values. 【Result】The filtered triangulation method was the most effective for obtaining the crown volume with an average relative error of 31.54%, which was better than 36.73% for the inverse distance weighting method, 37.04% for the Kriging method, and 38.54% for the natural neighbor method. The accuracy of all three estimation models for the tree height and yield was improved by using the crown volume as a characteristic variable in the modeling. The relative root mean squared errors (rRMSEs) for tree height were reduced by 3.77%, 0.78% and 0.64%, respectively. In addition, the rRMSEs for the yield were reduced by 1.32%, 0.34% and 0.16%, respectively. The multiple linear regression, random forest and K-nearest-neighbor models were compared, and the coefficients of determination of the random forest model were all better than those of the multiple linear regression and K-nearest-neighbor (the R² of tree height was 0.78, 0.51 and 0.19, and the R² of yield was 0.61, 0.48 and 0.24, respectively). There is no significant difference in the accuracy of using the estimated tree height and measured tree height to participate in yield modeling. 【Conclusion】The combination of crown volume and tree height participation modeling can effectively improve the accuracy of C. oleifera yield estimation, and the results of this study can provide a reference for conducting C. oleifera height and yield surveys using UAV remote sensing technology on a regional scale.