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Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine

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Earth Observation Department, Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Center, Scientific Institute, Mohamed V University, 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 2007, New South Wales, Australia
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Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-ro, Gwangin-gu, Seoul 05006, Korea
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Le Groupe de Recherche pour la Protection des Oiseaux au Maroc (GREPOM/BirdLife) Residence Oum Hani IV, Salé 11160, Morocco
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Association “Nature Initiative”, Avenue Mohamed Fadel Semlali, BP 79, Ad-Dakhla 11060, Morocco
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StatsN’Maps, Private Consulting Firm, Dallas, TX 75287, USA
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Laboratory of Natural Resources Management, Department of Geography, University of Yaoundé I, Yaoundé, PoBox 755, Cameroon
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Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Korea
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Department of Geology & Geophysics, College of Science, King Saud Univ., P.O. Box 2455, Riyadh 11451, Saudi Arabia
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 131; https://doi.org/10.3390/rs12010131
Received: 28 October 2019 / Revised: 13 December 2019 / Accepted: 25 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Remote Sensing for Climate Change Studies)
Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks. View Full-Text
Keywords: Google Earth Engine; permanent salt water cavities change; remote sensing; Sebkha; southern Morocco Google Earth Engine; permanent salt water cavities change; remote sensing; Sebkha; southern Morocco
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Hakdaoui, S.; Emran, A.; Pradhan, B.; Qninba, A.; Balla, T.E.; Mfondoum, A.H.N.; Lee, C.-W.; Alamri, A.M. Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine. Remote Sens. 2020, 12, 131.

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