Downscaling the spatial resolution of satellite imagery based on morphometric parameters to estimate the Topographic...

Digital elevation models (DEMs) play a key role in extracting morphometric factors like fill sink, flow accumulation, profile, flow width, slope, plan curvature, aspect, and total catchment to estimate the Topographic Wetness Index (TWI) that provides key information for modelling and predicting hazards related to mass movement or landsliding. The range and accuracy of information, including the topographic feature, can influence the quality of results, significantly impacting the severity and likelihood of occurrence of mass movement. Therefore, it plays a crucial role, especially in mountainous regions. This research aims to investigate, evaluate, and identify the optimal downscaling methodology for DEMs and assess its impact on DEMs at different spatial resolutions. Morphometric factors derived from the DEM were examined using six distinct methodologies: kriging, nearest neighbor, majority, bilinear, bi-cubic, and the Hopfield Neural Network (HNN). Three geospatial databases containing a 20 m, 12.50 m, and 1.50 m resolution DEM were used for analysis. Six downscaled topographic maps were generated: kriging, nearest neighbor, majority, bilinear, bi-cubic, and the HNN. Results validated from field elevation survey points have an accuracy of 1.50 m from total stations and the Global Positioning System (GPS). By predicted actual values at corresponding locations, field survey points named GPS, and total station data having vertical accuracy of 1.50 m, were used as a standalone reference source to validate the downscaled results. Consequently, we strongly endorse downscaling DEM employing the HNN technique to obtain the most precise morphometric parameters for fill sink, flow accumulation, profile, flow width slope, plan curvature, aspect, and total catchment to assess TWI maps. The result shows RMSE accuracy improved accuracy approximately 25% to 75% and 93%, respectively, when progressing from low to medium resolution (30, 20, and 12.50 m) DEMs. It is concluded that all the mentioned techniques, Bi-cubic, HNN, Nearest Neighbor, Majority, Bilinear, and Kriging, can improve the overall accuracy of DEM. However, Bi-cubic and HNN methods demonstrate superior accuracy relative to the Bilinear, Nearest Neighbor, Majority, and Kriging methods. This study attempts to address a gap in past research by evaluating and selecting the most effective downscaling approaches for DEMs and testing their accuracy to enhance topographical features at various spatial resolutions in mountainous regions.