基于机载激光雷达的寒温带典型森林高度制图研究

以内蒙古根河市潮查林场境内的寒温带兴安落叶松原始林及其次生林为研究对象,利用机载激光雷达点云数据与地面调查的66个样地数据,采用不同算法计算样地实测树高(Lorey's高、冠幅面积加权树高和算术平均高)分别与基于双正切角树冠识别算法获取的LiDAR估测高(冠幅面积加权树高、算术平均高)和基于点云提取的百分位高构建树高回归模型(冠幅面积加权树高模型、算术平均树高模型和LiDAR百分位树高模型)。对比不同树高模型的训练精度与估测精度的差异,探讨双正切角树冠识别算法对本研究区的适用性;同时了解冠幅面积加权的样地实测树高与Lorey's高对林分平均高代表性的差异,确定最优解释变量,筛选最优树高模型,计算研究区森林高度空间分布图,为后续生物量和碳储量研究提供参考数据。结果表明:样地冠幅面积加权树高的模型训练精度和估测精度与Lorey's高的结果一致性较好,略低于Lorey's高的估测结果。LiDAR百分位树高模型中的50%分位高与样地实测树高相关性显著且回归模型拟合效果较好,其中,以Lorey's高为样地实测树高时模型的R2=0.869、RMSE=1.366m;以冠幅面积加权树高为样地实测树高时模型的R2=0.839、RMSE=1.392m;Lorey's高的50%分位高模型的估测精度最高,各独立验证样本点估测精度均高于85%,平均估测精度为94.73%,最高估测精度可达99.78%,其中混交林平均估测精度(96.72%)高于针叶林的平均估测精度(93.58%)。因此,选择Lorey's高的50%分位高模型计算研究区的森林高度空间分布。 

Typical forest height mapping in cold temperate zone using airborne LiDAR data

Based on the airborne LiDAR point cloud data and 66 sample plots data from field inventory of Chaocha Forest Farm in Genhe City, Inner Mongolia, we set the cold temperate primary and secondary forests as research subjects. The model training accuracy and estimating accuracy of different forest height models were compared ( crown area weighted height model, arithmetic mean height model, LiDAR percentile height model) , which were generated by field-measured forest height ( Lorey's height, crown area weighted height, arithmetic mean height) , LiDAR crown area weighted height, LiDAR arithmetic mean height and LiDAR percentile height, respectively. LiDAR crown area weighted height and LiDAR arithmetic mean height were extracted by double tangent tree crown recognition algorithm, and LiDAR percentile height was extracted from LiDAR point cloud directly. Then we investigated the applicability of the double tangent tree crown recognition algorithm in the study area, revealed the difference between Lorey's height and crown area weighted height, and determined the optimal explanatory variables and selected the optimal forest height model. Afterwards, the optimal tree height model was used to calculate the spatial distribution of forest height in the study area, which would provide reference data for subsequent studies of biomass and carbon storage. The results showed that the training accuracy and estimating accuracy of the field crown area weighted forest height model were well consistent with Lorey's height, yet slightly lower than the results of Lorey's height. And 50% percentile height was well fitted with the field-measured height (R2 = 0.869, RMSE = 1.366 m for Lorey's height, and R2 = 0.839, RMSE = 1.392 m for crown area weighted height) . Estimating accuracy of each independently validated sample plot was all higher than 85%, with the average accuracy of 94.73% and the highest accuracy of 99.78%. The average estimating accuracy for mixed forests was 96.72%, higher than that for coniferous forests, 93.58%. On this basis, we selected the 50% percentile forest height model of Lorey's height as the optimal model and used it to calculate forest height and map its spatial distribution.