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Open AccessArticle

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

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Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Center, Scientific Institute, Mohamed V University in Rabat. Av. Ibn Batouta B.P 703, Rabat 10106, Morocco
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The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
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Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209, Neungdong-roGwangin-gu, Seoul 05006, Korea
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Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Korea
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Image Processing Laboratory (LTI), IRGM, P.O. Box 4110, Yaounde, Cameroon
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Department of Earth Sciences, University of Yaounde I, P.O. Box 812, Yaounde, Cameroon
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1042; https://doi.org/10.3390/rs11091042
Received: 3 April 2019 / Revised: 23 April 2019 / Accepted: 28 April 2019 / Published: 1 May 2019
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
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. View Full-Text
Keywords: categorical processing; collaborative change detection; remote sensing; GIS; wet land monitoring; Morocco categorical processing; collaborative change detection; remote sensing; GIS; wet land monitoring; Morocco
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MDPI and ACS Style

Hakdaoui, S.; Emran, A.; Pradhan, B.; Lee, C.-W.; Nguemhe Fils, S.C. A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco. Remote Sens. 2019, 11, 1042.

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