基于LiDAR离散点云能量信息的针叶林叶面积指数反演方法

A method of leaf area index retrieval for coniferous forests based on LiDAR discrete point cloud intensity information

叶面积指数是森林的重要结构参数,对于研究与植被叶片相关的生物物理活动具有重要意义。为了提高针叶林叶面积指数的估测精度,以吉林省长春市净月潭国家森林公园为研究区,通过对小光斑激光雷达离散点云进行滤波分类处理、拟合波形数据,从中提取5个能量参数,分别用于估测针叶林样方的叶面积指数,通过分析得出I2预测模型最好,R=0.911,P=0.968。结果表明小光斑激光雷达离散点云的能量信息能够较好地估计针叶林的叶面积指数,未来应加大小光斑激光雷达能量参数的应用。

Leaf area index is an important structure parameter of forest. It plays a significant role in the researches associated with vegetation biophysical activities. In order to improve the estimation precision of forest leaf area index of coniferous forest, by taking Jingyuetan National Forest Park in Changchun city as the studied area, the discrete spot excimer laser radar points cloud of the studied area were treated in filter classification and fitting the waveform data, thus extracting five energy parameters. The five parameters were used to estimate the leaf area index of coniferous forest. The analysis results show that the I2 model was the most suitable, whose relativity was 0.911 and the precision was 0.968. It is suggested that the energy information of small footprint LiDAR points cloud can be well used to estimate leaf area index of coniferous forest, and the small footprint LiDAR energy parameters should widely used in the future.