A collaborative change detection approach on multi-sensor spatial imagery for desertwetland monitoring after a flash flood in Southern Morocco

© 2019 by the authors. 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

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

© 2020 by the authors. 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

LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides. The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI). The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC&thinsp;=&thinsp;0.918) and the prediction rate (AUC&thinsp;=&thinsp;0.901) of the LR model is higher than that of the ANN model (success rate (AUC&thinsp;=&thinsp;0.886) and prediction rate (AUC&thinsp;=&thinsp;0.877). These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.</p

MAPPING INFECTED AREA AFTER A FLASH-FLOODING STORM USING MULTI CRITERIA ANALYSIS AND SPECTRAL INDICES

This research article is summarize the applications of remote sensing and GIS to study the urban floods risk in Al Mukalla. Satellite acquisition of a flood event on October 2015 in Al Mukalla (Yemen) by using flood risk mapping techniques illustrate the potential risk present in this city. Satellite images (The Landsat and DEM images data were atmospherically corrected, radiometric corrected, and geometric and topographic distortions rectified.) are used for flood risk mapping to afford a hazard (vulnerability) map. This map is provided by applying image-processing techniques and using geographic information system (GIS) environment also the application of NDVI, NDWI index, and a method to estimate the flood-hazard areas. Four factors were considered in order to estimate the spatial distribution of the hazardous areas: flow accumulation, slope, land use, geology and elevation. The multi-criteria analysis, allowing to deal with vulnerability to flooding, as well as mapping areas at the risk of flooding of the city Al Mukalla. The main object of this research is to provide a simple and rapid method to reduce and manage the risks caused by flood in Yemen by take as example the city of Al Mukalla

EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

International audienc

IMPROVING THE LAND SURFACE GENERAL DROUGHT INDEX MODEL

International audienc