DEM Error Correction in Forest Areas Based on Spaceborne LiDAR ICESat-2 Data
In vegetation areas,the DEM products produced by optical remote sensing or InSAR technology cannot reflect the real sub-canopy terrain.The DEM error in forest areas is mainly caused by vegetation coverage and topography.The new generation of spaceborne LiDAR can provide a large number of high-precision terrain control point products,which provides a new opportunity for the correction of DEM errors in forest areas.Based on this,we propose a DEM elevation error correction method over forested areas based on a machine-learning framework considering vegetation coverage and terrain factors.Firstly,the terrain residuals between high-precision terrain control points and DEM are obtained.Secondly,the optical remote sensing data,SAR remote sensing data,and DEM products are used to calculate the characteristic parameters related to vegetation coverage and terrain.Finally,the error correction models of different types of DEM products are established by combining these characteristic parameters with the obtained terrain residual points.We select the study area with mountainous located at the junction of Tennessee and North Carolina to test the proposed method.The results show that compared with the original DEM,the accuracy of DEM corrected by elevation error increases by more than 40％in forest areas with mountainous.
万方数据
维普
