Terrain environment parameters play a vital role in controlling groundwater movement:its recharge and discharge me-chanisms.Many earlier studies have been conducted relating terrain parameters and groundwater conditio...Terrain environment parameters play a vital role in controlling groundwater movement:its recharge and discharge me-chanisms.Many earlier studies have been conducted relating terrain parameters and groundwater condition using conventional me-thods and remote sensing techniques.This study,however,endeavors to spatially visualize the degree of fluctuation in the ground-water level of Ongur,a minor river basin in different terrain units under different seasons(monsoon and summer) for three histori-cal periods of time using Geographic Information System(GIS) raster analysis.展开更多
To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with dif...To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.展开更多
文摘Terrain environment parameters play a vital role in controlling groundwater movement:its recharge and discharge me-chanisms.Many earlier studies have been conducted relating terrain parameters and groundwater condition using conventional me-thods and remote sensing techniques.This study,however,endeavors to spatially visualize the degree of fluctuation in the ground-water level of Ongur,a minor river basin in different terrain units under different seasons(monsoon and summer) for three histori-cal periods of time using Geographic Information System(GIS) raster analysis.
基金supported by the National Natural Science Foundation of China(grant number 42271438)the Shan-dong Provincial Natural Science Foundation of China(grant no.ZR2020YQ26)a project of the Shandong Province Higher Educational Youth Innovation Science and Technology Program(grant number 2019KJH007).
文摘To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.
