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森林叶面积指数(LAI)是描述森林冠层结构和树木生长状况的一个重要指标.本研究以内蒙古依根地区为研究区,在充分考虑机载激光雷达不同回波脉冲之间差异的基础上,对LiDAR点云数据进行分束处理,同时利用传感器与目标之间的距离对LiDAR点云强度进行校正,在由点云校正强度计算传统激光穿透指数(LPI)的同时提出了一个新的激光穿透指数,即单束激光穿透指数(LPI_s),并分别采用LPI和LPI_s两种激光穿透指数在4种不同LiDAR数据采样尺度(直径分别为5、10、15和20 m)下分别应用理论模型和经验模型两种不同的建模方法对森林LAI进行估测,以期通过激光分束来提高森林LAI的估测精度.结果表明:分束激光穿透指数均值(LPI_(mean))估测LAI的效果明显好于未分束LPI的估测效果,且当LiDAR数据采样尺度为15 m时,LPI_(mean)的经验模型(R2=0.80,平均绝对偏差MAD为0.11)和理论模型(R~2=0.77,MAD=0.16)的估测结果均达到最佳.最后综合应用最佳经验模型与理论模型各自的优点绘制了研究区的白桦林LAI分布图.
The leaf area index (LAI) of forest is an important index to describe the canopy structure and tree growth status of the forest.In this study, based on the study area of Yigen in Inner Mongolia, taking into account the differences between different echo pulses of airborne lidar, , LiDAR point cloud data is processed by beam splitting, and the distance between sensor and target is used to correct LiDAR cloud point intensity. At the same time, the traditional laser penetration index (LPI) The laser penetration index (LPI_s) was measured. Four laser penetration indices, LPI and LPI_s, were used respectively on four different LiDAR data sampling scales (5, 10, 15 and 20 m in diameter) The LAI of forest was estimated by using two different modeling methods, theoretical model and empirical model, respectively, in order to improve the estimation accuracy of forest LAI by laser beam splitting.The results showed that LPI mean value, ) Estimated the effect of LAI significantly better than that of unsupervised LPI, and when the LiDAR data sampling scale is 15 m, the empirical model of LPI mean (R2 = 0.80, the mean absolute deviation MAD is 0.11) and the theory Model (R ~ 2 = 0.77, MAD = 0.16 ) Results are best.Finally, the LAI distribution of Betula platyphylla in the study area is drawn based on the respective advantages of the best empirical model and the theoretical model.