Elevation Correction of a Large-Scale DEM Using ICESat-2 Laser Altimetry Data

The digital elevation model (DEM) is a digital representation of the surface elevation, but it contains elevation errors arising from vegetation coverage and terrain undulation. Spaceborne LiDAR, with its large-scale and high-precision elevation measurement capabilities, can effectively correct these errors and has become an important means to improve the elevation accuracy of DEM. In this study, a hybrid incremental regression (HIR) model based on the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimetry data is designed to correct large-scale DEM elevation errors. The model combines a neural network and a decision tree model to efficiently fit the elevation error through stepwise regression, which can be applied to different terrains and landforms over large areas. In the experiment, first, the proposed model was compared with four classic models [multiple linear regression (MLR), random forest (RF), backpropagation neural network (BPNN) and light gradient-boosting machine (LightGBM)] in seven different landform areas, and the results showed that our model was better than other models in terms of accuracy improvement and was applicable to various terrains. Then, the model was applied to China, and the higher precision, large-scale DEM datasets were produced. Compared with the original DEM, the root mean squared error (RMSE) and mean absolute error (MAE) of the optimized DEM were reduced by 1.603 and 1.453 m, respectively. The differences in accuracy under different slopes, land cover types, and vegetation heights were also analyzed, and it was found that the enhancement effect was the best in the high-vegetation cover area. Finally, it is validated using three regions of high-precision validation data, and the results showed that the RMSE had improved to varying degrees, ranging from 0.193 to 2.853 m.