【摘要】 冠层高度是森林资源调查的重要因子。传统的森林树高调查方法存在外业调查难度大,效率低等问题。无人机(UAV)的发展为快速估测森林树高提供了手段。以福建省闽清县的杉木Cunninghamia lanceolata人工林为研究对象,通过Eco Drone-UA无人机遥感系统获取研究区遥感影像,利用Pix4D Mapper软件对航拍多光谱影像进行预处理,构建数字表面模型(DSM),利用1∶10 000地形图生成数字高程模型(DEM);基于DSM和DEM叠加相减得到树冠高度模型(CHM),实现杉木树高的提取。结果表明:植被指数和多光谱波段结合随机森林算法能够有效识别真实树冠顶点;利用无人机遥感影像能够实现杉木树高估测,相对误差最小值为0.81%,最大值为23.48%,标准误差为1.48 m,估测精度为90.8%。高程变化对树高估测精度有影响,根据高程大小排序的3组样木实测树高与提取树高的决定系数(R2)分别是0.97, 0.84和0.78,标准误差分别是0.67, 1.17和1.99 m,在高程较高区域树高估测精度明显高于高程相对较低区域。更多还原

【Abstract】 Tree height,an important parameter in a forest resource survey,has been problematic in traditional methods of a forest survey making it difficult and inefficient to conduct further investigations.To utilize the rapid development,in recent years,of unmanned aerial vehicle(UAV) technology as a means for quickly estimating the height of forest trees,tree height data were obtained from Cunninghamia lanceolata plantations in Minqing County,Fujian Province.Remote sensing imagery in the study area was obtained through the Eco Drone-UA drone remote sensing system,setting the flight altitude to 120 m and the flight belt overlap to 50%.Pix4D Mapper software was used to preprocess aerial multispectral images and build a DSM(Digital Surface Model) using kriging interpolation to obtain a DEM(Digital Elevation Model).Based on the estimated idea of the canopy height model(CHM) = digital surface model(DSM)-digital elevation model(DEM),the tree height of C.lanceolata was extracted.Results showed that combining the vegetation index,multispectral bands,and random forest algorithm were effective in identifying the true crown vertex,and it was feasible to use high resolution UAV imagery to extract tree height.The minimum relative error for tree height was 0.81%,the maximum was 23.48%,the estimation accuracy was 90.8%,and the standard error was 1.48 m.At the same time,the measurement of tree height was affected by the DEM with the R2 and root mean squared error(RMSE) value for the least DEM being R2= 0.781,RMSE= 1.99 m,for the next one was R2= 0.84,RMSE= 1.17 m,and for the largest it was R2= 0.966,RMSE= 0.67 m.The accuracy of the measured height of the larger DEM was higher than that of the smaller DEM.Therefore,This approach integrates UVA with random forest,which makes up of the shortcomings of each.In addition,the results also provide a reference guidelines for the tree height.更多还原