NDEI-BASED MAPPING OF LAND SURFACE CHANGES
Keywords: NDEI, UAV, Remote sensing, Land surface changes, DEM
Abstract. Topography measurements of land surface elevation changes are essential for geomorphological studies and research on natural hazards. However, conventional topography and remote sensing methods pose several challenges. Fieldwork in remote areas could be insecure or be hard to conduct due to physical barriers. Remote sensing satellite images, in turn, may lack the temporal resolution for monitoring topography changes or be covered by clouds and shadows that reduce their usefulness. Unmanned Aerial Vehicles (UAV) provide high-resolution imagery from remote areas and may give a greater insight into land surface changes. Previous studies have demonstrated the potential of the Normalized Difference Elevation Index (NDEI), derived from UAV surveys, to map changes in topography in different environments, including a quarry zone of sand mines in Colombia, and in the North Pole Ice Cap. As the NDEI is a metric capable to reveal topography disturbances, it could be exploited for rapid assessments of mass movements occurrence. For this research, a fine temporal resolution dataset of monthly NDEI values were used to explore their utility as a mass wasting identification technique. NDEI was used to map changes in the land surface in a quarry zone affected by landslides and mudflows. NDEI values were obtained from the multi-temporal analysis of digital surface models generated from UAV images and processed using Structure from motion (SfM) techniques. Main components of the proposed method are: (i)UAVbased imagery data capture, (ii)data processing using SfM techniques, (iii)NDEI calculation, and (iv)mass movement identification. Results illustrate the feasibility of using UAV-based imagery data to increase the accuracy of land changes information and produce rapid assessments of mass movements occurrence. Results show that the NDEI approach may increase the accuracy of land surface changes assessment and mapping, as well as enable the design of new methodologies to identify natural hazards and risks.