GEDI与Tandem-X DEM估测密林林下地形性能评价

针对密林情况下，GEDI数据与现有的Tandem-X DEM数字地面模型估测林下地形精度没有进行整体评价问题，拟以密林情况作为主要分析场景，通过提取GEDI L2A数据产品对应光斑的经纬度、林下地形信息与数据质量筛选参数，开展数据质量筛选，用以估测基于GEDI数据的林下地形数据，与Tandem-X DEM数据估测密林情况下研究区林下地形开展比较，并进一步探究冠层高度、森林覆盖度与植被类型对估测精度的影响。GEDI与Tandem-X DEM的R²分别为0.99和0.98,GEDI估测林下地形结果的RMSE、Average与STD分别6.49、-1.92、4.42 m, Tandem-X DEM估测林下地形结果的RMSE、Average与STD分别为18.15、14.63、7.35 m。GEDI数据在混交林和稀疏草原情况下RMSE与Average分别变化8.05 m和6.04 m, Tandem-X DEM数据在常绿针叶林与农田/天然植被情况下，RMSE与Average变化幅度为21.63、26.43 m。实验结果表明，GEDI与Tandem-X DEM数据与机载验证...

Evaluation of Underforest Terrain Performance Estimation Using GEDI and Tandem-X DEM Data in Dense Forests

In the case of dense forests, the accuracy of estimating underforest terrain using GEDI data and existing Tandem-X DEM digital terrain models has not been comprehensively evaluated. Aiming to focus on the dense forest situation as the main research object and using airborne data as real validation data. By extracting the longitude and latitude of the corresponding LiDAR spot, underforest terrain information, and data quality screening parameters of the GEDI L2A data product, to estimate underforest terrain data based on GEDI data. Compared with Tandem-X DEM data to estimate the underforest terrain under dense forest conditions, and further explore the effects of canopy height, forest coverage, and vegetation type on estimation accuracy. The R2 values of GEDI and Tandem-X DEM were 0.99 and 0.98, respectively. The RMSE, Average, and STD values of GEDI for estimating underforest terrain were 6.49m, -1.92m, and 4.42m, respectively. The RMSE, Average, and STD values of Tandem-X DEM for estimating underforest terrain were 18.15m, 14.63m, and 7.35m, respectively. In GEDI data, RMSE and Average were changed by 8.05m and 6.04m respectively in the case of mixed forest and sparse grassland, and in Tandem-X DEM data, RMSE and Average were changed by 21.63m and 26.43m respectively in the case of evergreen coniferous forest and farmland/natural vegetation. The experimental results indicated that there was a strong correlation between GEDI and Tandem-X DEM data and airborne validation data, and GEDI performed better evaluation criteria than Tandem-X DEM data. The surface vegetation types performed greater impact on the estimation of underforest terrain than canopy height and vegetation coverage.