Abstract
Abstract. In the context of climate change and rising frequency of extreme hydro-meteorological events around the world, flood risk management and mapping of heavy rainfall-related damages represent an ongoing critical challenge. For decades now, remote sensing has been largely used to investigate spatial and temporal changes in land use and water resources. Today, different satellite products provide fast and crucial knowledge for the study of hydrological disasters over large areas, possibly in remote regions, with high spatial resolution and high revisit frequency. Yet, until now, few works have sought to detect the full range of extreme rainfall-related damages with optical imagery, especially those caused by intense rainwater runoff beyond the direct vicinity of major waterways. The work presented in this paper focuses on the Aude severe weather event of October 15th, 2018, in the South of France, for which more than a thousand claims for agricultural disaster were registered, both related to river overflowing and rainwater runoff.The full resources of ground truths, contextual information, land use as well as digital elevation model (DEM) combined to high resolution and high frequency optical imagery (Sentinel-2, Pléiades) are used to develop an automatic damage detection method based on supervised classification algorithms. Through the combination of several indicators characterizing heterogeneous spectral variations among agricultural plots following the event, a Gaussian process classifier achieved various classification accuracies up to 90% on a large comparable and independent photo-interpreted validation sample. This work builds great expectations for applications in other areas with contrasted climate, topography and land cover.
Highlights
Floods arise in the aftermath of extreme rainfall events
Very few studies have undertaken the detection of the full range of heavy rainfallrelated damages, especially those caused by intense rainwater runoff
Considering that only half of flood-related damages are likely to be associated with river overflowing in France, this work focuses on the Aude 2018 heavy rainfall event to develop an automatic damage detection method nearby and far away from waterways where intense rainwater runoff can be accountable for
Summary
Floods arise in the aftermath of extreme (either in magnitude, rate or duration) rainfall events. Rainwater-related damages can take many forms and emerge in multiple places Because of their intensity and often dramatic social and economic consequences, the vast majority of research and operational activities on flood damage detection mainly focuses on overflowing of major waterways (e.g. Copernicus EMS Rapid Mapping activity in particular through SERTIT). In this context, assessing flood extent from satellite images has been a pressing topic in remote sensing of natural disasters for decades (Rahman, Di, 2017). These disturbances often take place during short, hardly observable time periods, potentially anywhere with little topography and outside the direct vicinity of major waterways
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
