Potential Of High Resolution Satellite Optical Imagery To Detect Damages Following Extreme Rainfall Events

Combination of numerous satellite data has lately become available to cover large areas with very high spatial resolution (VHR) and high revisit frequency. Little studies have yet made use of these images to assess and map damages following an extreme rainfall event, in particular those caused by rainwater runoff. We therefore investigate a specific approach to detect these damages as exhaustively as possible from VHR and HR satellite data acquired as closely as possible before and after an intense rainfall event. To do so, we used Pleiades (0.7 m) and Sentinel-2 (10 m) images taken over the Aude region (France) which was heavily affected by a severe storm on October 15th, 2018. We chose to focus on agricultural lands as 1119 claims for agricultural disaster were registered and certified following this event. Post-event VHR Pleiades images were used to identify claimed damages over a subset of agricultural plots as well as to determine contextual information such as the type of damage (erosion, deposit, uprooting). Several indices and spectral filters were then applied to the selected areas from pre and post-event Sentinel-2 images. This exploratory work reveals that certain types of agricultural damage are well detected while others, albeit clearly visible on Pleiades images, are harder to identify with the selected indices and filters on Sentinel-2 images. It also shows the potential of this approach to discriminate the extent of damage that was declared over agricultural areas. This study emphasizes how relevant the use of combined spectral, temporal and contextual information is to detect damages following an extreme rainfall event, in particular those caused by rainwater runoff, thanks to optical imagery, as spectral knowledge alone does not appear to be sufficient. This preliminary work paves the way for further work based on the development of more advanced change detection methods, clues and filters as well as artificial intelligence.