星载激光雷达GEDI数据林下地形反演性能验证

【目的】新一代天基测高系统全球生态系统动力学调查(GEDI)对森林观测及经营具有重要意义，为探究GEDI V2(GEDI第2版)数据反演林下地形的性能，利用机载雷达数据验证林下地形反演精度，并探究反演精度的影响因素。【方法】分别以美国西波拉森林与中国帽儿山森林为研究对象，利用G-liht及帽儿山高精度机载雷达数据验证GEDI V2数据在针叶林及针阔叶混交林下反演地形的性能，并分析不同光束强度、光斑时间、坡度及植被覆盖度对地形反演精度的影响。【结果】美国西波拉针叶林地区地形反演精度均方根误差(RMSE)为2.33 m,平均绝对误差(MAE)为1.48 m;帽儿山针阔叶混交林地区地形反演精度RMSE为4.49 m, MAE为3.33 m。随着坡度、植被覆盖度增大，两种森林类型地形反演精度均降低。【结论】GEDI V2数据反演针叶林林下地形精度要优于针阔叶混交林，强光束优于覆盖光束，湿润地区白天效果更优，干旱地区黑夜效果更优；平缓地区数据使用效果极好，陡峭地区精度降低；中低植被覆盖度区域地形反演精度较高，高植被覆盖区域地形测定性能有所下降。

Verification of performance of understory terrain inversion from spaceborne lidar GEDI data

【Objective】 The new generation of the space-based altimetry global ecosystem dynamics investigation (GEDI) system is of great significance to forest observation and management. In order to explore the performance of GEDI version 2 data (V2 data) inversion of understory topography, this study uses airborne radar data to verify the accuracy of understory topography inversion, and explores the factors affecting the accuracy.【Method】 Taking the Cibola forest in the United States and the Maoer Mountain forest in China as the research objects, the performances of GEDI V2 data in coniferous forests and mixed coniferous and broad-leaved forests were verified using G-liht and Maoer Mountain high-precision airborne radar data. The effects of different beam intensities, spot times, slopes and vegetation coverage on the accuracy of terrain inversion were analyzed.【Result】 The root mean square error (RMSE) of topographic inversion accuracy in the Cibola taiga area of the United States was 2.33 m, and the average absolute error (MAE) was 1.48 m. The RMSE value of the topographic inversion accuracy in the coniferous and broad-leaved mixed forest area of Maoer Mountain was 4.49 m, and the MAE value was 3.33 m. With the increase in slope and vegetation coverage, the topographic inversion accuracy of the two forest types decreased.【Conclusion】 The GEDI V2 data inversion accuracy of understory topography in coniferous forests was higher than that of mixed coniferous and broad-leaved forests. Strong beams were better than coverage beams, and the accuracy was higher during the daytime in humid areas, and better at night in arid areas. The accuracy of steep areas was reduced, the terrain inversion accuracy was higher in areas with medium and low vegetation coverage, and the performances of terrain determination in areas with high vegetation coverage were decreased.