基于机载LiDAR点云估测林分的平均树高
Study on Mean Canopy Height Estimation from Airborne LiDAR Point Cloud Data

以内蒙古上库力农场为研究区,基于高程归一化后的植被点云数据计算了植被点云高度阈值平均值，建立林分平均树高线性回归模型，并进行精度评定。结果表明，模型估测平均树高精度最高为99.81%，最低为87.09%，总体平均精度为94.56%。利用植被点云高度阈值平均值估测林分平均树高具有较高的可靠性。
The mean canopy height is a critical parameter for evaluating forest structure. In this paper, taking the Shangkuli Farm in Inner Mongolia as the study area, the mean canopy height was estimated. The threshold mean of vegetation point height was acquired, which was generated from the vegetation point data set of elevation normalized, and then the linear regression model was established for estimating the mean canopy height in this area. Afterwards, the accuracy of model was evaluated, with 99.81% of the highest accuracy, 87.09% of the lowest accuracy and 94.56% of the average accuracy. The results indicated that the threshold mean of vegetation point height was able to estimate mean canopy height with the higher reliability