基于无人机激光雷达的大兴安岭林区高精度数字高程模型重建OA

This study integrated auxiliary information estimation and super-resolution reconstruction techniques to per-form regional-scale fine-resolution(0.5 m)DEM reconstruction,using the forest and buildings removed Copernicus digi-tal elevation model(30 m)as auxiliary data and unmanned aerial vehicle laser scanning(ULS)DEM as the target vari-able.Three methods-traditional bicubic interpolation,simple linear regression(SLR),and deep residual networks(ResNet)-were employed for high-precision DEM reconstruction.The optimal method was then applied to reconstruct the 0.5 m DEM(DEM_0.5)of the Greater Khingan forest region.Independent in-situ validation points were used to compare DEM_0.5 with the original FABDEM and the bicubic-interpolated DEM(DEM_b).Results indicated that the SLR method achieved the best balance between reconstruction efficiency and quality.Its root mean square error(RMSE),mean absolute error(MAE),bias,and 90%quantile absolute error(LE90)were all superior to FABDEM and bicubic interpolation,with time efficiency comparable to bicubic interpolation and significantly higher than ResNet.At independent validation points,the DEM_0.5 reconstructed by SLR showed the best performance in RMSE,MAE,Bias,and LE90,decreasing by 15.0%,15.8%,26.5%,and 12.1%relative to FABDEM,and by 7.8%,9.4%,12.2%,and 8.3%relative to bicubic interpolation,respectively.Slope-aspect analysis further showed that SLR recon-struction outperformed FABDEM across all eight slope directions and exceeded bicubic interpolation in six directions(N,NE,E,SE,W,NW),demonstrating higher stability and superior terrain detail recovery.In conclusion,the SLR-based reconstruction method is the most suitable for fine-resolution(0.5 m)DEM generation in the Greater Khingan re-gion.Its lightweight,interpretable,and easily applicable framework effectively balances reconstruction cost,feasibil-ity,and accuracy,providing a practical solution for low-cost,high-efficiency acquisition of high-quality regional DEMs.