基于机载小光斑LiDAR数据插值的亚热带森林丘陵地形的误差分析

Error analysis of DEM interpolation based on small-footprint airborne LiDAR in subtropical hilly forests

以激光雷达(LiDAR)地面点云数据为数据源,将北亚热带天然次生林下的丘陵地形作为研究对象,分析了6种常用局部插值方法生成DEM的全局误差及其与地形因子、地面插值点密度和地表植被状况的相互关系,并借助随机森林方法进行插值误差不确定性制图。研究表明:各插值表面的预测值总体偏低,其最佳输出空间分辨率为2 m;其中以自然邻近法插值生成的数字地形精度最高且可视化效果最好,而张力样条法的精度最低;全局误差随坡度增大而逐渐提升,随地面插值点密度提高逐渐降低;幼龄和中龄天然次生林所在区域地形插值的误差较大而成熟林的误差最大,灌木区全局误差不高但误差变异较大。同时,以LiDAR提取的植被参数与地形插值误差表现了较好的相关性,而归一化植被指数(NDVI)与误差之间的相关不明显,这表明以LiDAR数据提取植被参数在NDVI易饱和地区也可以较好地反映地形插值精度。

In this research,ground LiDAR point cloud was used as a data source; hilly terrain under the northern subtropical secondary forest was set as a research object. Six commonly used zonal interpolation methods were applied to create DEMs,global error and the relationship between the errors and levels of the terrain factor,ground interpolation point density and surface vegetation cover were analyzed. Then the uncertainty map of interpolation error was created by the random forest method. The research results demonstrated that： The predicted values on the interpolation surface were generally underestimated and the best output resolution was 2 m; the natural neighbor interpolation method showed the highest accuracy and the best visual quality,but the tension spline method had the poorest accuracy; the global error increased corresponding to the increment of slope but decreased by the increment of ground interpolation point density; the terrain interpolation errors were relatively high under the young and middle age natural secondary forest but received the highest error under mature forest. The global errors under shrubs were not high but the variations of errors were high.Meanwhile,there existed a relative high correlation between LiDAR-derived forest parameters and the errors of terrain interpolation,while no significant relationship was found between NDVI and the global errors; this again proved that LiDAR-derived forest variables could better reflect the terrain interpolation accuracy in the NDVI saturation regions.