A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

Abstract

This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

Prediction of Flash Flood Susceptibility Using Fuzzy Analytical Hierarchy Process (Fahp) Algorithms and Gis: A Study Case of Guelmim Region in Southwestern of Morocco

“…In recent years, flash flood risk susceptibility researches have been realized by many countries (Abdelkareem 2017;Carpenter et al 1999;Chen, Hill, and Urbano 2009;Curebal et al 2016;Dawod, Mirza, and Al-Ghamdi 2011;Elkhrachy 2015). However, various methods have been used to identify and evaluate flash flood vulnerable areas in Morocco (Boutaleb et al 2018;Echogdali et al 2018;Hakdaoui et al 2019;El Khalki et al 2018;Tramblay et al 2012;Werren et al 2016). And only a few methods have been used to evaluate flash flood susceptibility in Guelmim region.…”

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

“…The proposed approach takes the confidence value of each pixel into account so that it can find the pixels, which have a high probability for the training of the modest adaboost classifier. To monitor wetland areas in an arid Saharan region, Hakdaoui et al [86] collected images from seven satellites (optical and radar) before and after flash flooding. The proposed approach is based on the combination of both spectral and categorical processing to obtain a resultant map of changes.…”

High-Resolution Sar Coherent Change Detection in Urban Environment

“…The ansence of cloud coverage, the possibility to sense the scene night and day, the repeated geometry of acquisition and, the most important, the high sensitivity to fine changes in the spatial structure of the scene are all peculiarity that contribute to SAR images very popular for change detection purposes. Further development in this area was made possible thanks to the Copernicus mission Sentinel-1: starting from 2014 free and open data is available opening the possibility to analyze long time series over urban areas (Washaya et al, 2018, Hakdaoui et al, 2019. The monitoring of urban settlements is an interesting topic in the remote sensing community.…”