Monitoring and prediction on the seasonal variation of lake water storage using GLAS and landsat images
To address the challenges of monitoring water storage in high-altitude lakes,this study fused ICESat/GLAS and Landsat data to monitor and predict the seasonal variation of lake water storage.Based on Random Forest,GLAS laser foot points were classified into useful and useless foot points,which contributed to distinguish non-aquatic and aquatic objects on lake surfaces.The water bodies were segmented by combining water indices and Otsu's method in Landsat data,which could mitigate the impact of irregular boundaries.To resolve the issues of missing observations and temporal mismatches,a high-temporal-resolution relationship among water level,water surface area,and water storage was established by fusing GLAS and Landsat data.Lastly,the water storage was predicted by LSTM model.Using Nam Co Lake in Tibet as a case study,the feasibility of proposed methodology was tested.During 2003-2009,the water level,the water surface area,and water storage raised every year,which were 1.15 m,25.58 km2 and 2.30 km3 respectively.The predicted result of water storage increment was 1.16 km3 from 2010-2016,which was accorded with the actual situation.Collectively,the proposed methodology could provide crucial data support for dynamic monitoring of ecological environments.
classification of laser foot pointswater storage predictionwater level calculationseasonal monitoringwater body segmentation