Mapping Groundwater Prospective Zones Using Remote Sensing and Geographical Information System Techniques in Wadi Fatima, Western Saudi Arabia
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Integration of remote sensing (RS) and GIS methods has allowed for the identification of potential water resource zones. Here, climatic, ecological, hydrologic, and topographic data have been integrated with microwave and multispectral data. Sentinel-2, SRTM, and TRMM data were developed to characterize the climatic, hydrologic, and topographic landscapes of Wadi Fatima, a portion of western Saudi Arabia that drains to the Red Sea. The physical characteristics of Wadi Fatima’s catchment area that are essential for mapping groundwater potential zones were derived from topographic data, rainfall zones, lineaments, and soil maps through RS data and GIS techniques. Twelve thematic factors were merged with a GIS-based knowledge-driven approach after providing a weight for every factor. Processing of recent Sentinel-2 data acquired on 4 August 2023 verified the existence of a zone of vegetation belonging to promising areas of groundwater potential zones (GPZs). The output map is categorized into six zones: excellent (10.98%), very high (21.98%), high (24.99%), moderate (21.44%), low (14.70%), and very low (5.91%). SAR CCD derived from Sentinel-1 from 2022 to 2023 showed that the parts of no unity are in high-activity areas in agricultural and anthropogenic activities. The model predictions were proven with the ROC curves with ground data, existing wells’ locations, and the water-bearing formations’ thickness inferred from geophysical data. Their performance was accepted (AUC: 0.73). The outcomes of the applied methodologies were excellent and important for exploring, planning, managing, and sustainable development of resources of water in desert areas. The present study successfully provided insights into the watershed’s hydrologic, climatic, vegetated variation, and terrain database information using radar, optical, and multi-temporal InSAR data. Furthermore, the applied multi-criteria overlay technique revealed promising areas for groundwater abstraction, which can be applied elsewhere in various environmental situations.Keywords:
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This study provides the first evaluation of the potential of both the Landsat-8 Operational Land Imager (OLI) sensor images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data for automated lineament extraction in the south side of the Marrakech High Atlas (Telouet–Tighza area). After image corrections, enhancement methods such as principal component analysis, band composite (BC), and directional filtering were adopted to create new images that provided high visibility of linear structures. The new Landsat BC image used in this study was selected based on the calculation of the optimum index factor and correlation index. In addition to the Landsat image, the SRTM DEM was used to detect structural lineaments in the area by generating shaded relief images. Multisource data, such as band ratio image, geological maps, and fieldwork, were used to eliminate the nongeological lineaments extracted. The results indicate that an automated method was applied successfully for lineament mapping in this area, by detailing the main tectonic faults. Moreover, new lineaments are identified and are validated by fieldwork. Structural lineaments extracted show compatibility in their direction, length, distribution, and density with the tectonic evolution of the study area. A total of 2945 lineaments were extracted with major ENE–WSW and predominant E–W directions. The new structural map shows more structural information compared with the geological map of this area and exemplifies the performance of Landsat-8 OLI bands and SRTM data in this kind of study.
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Abstract Lineament maps drawn from several LANDSAT images of a part of north Wales and western England display considerable variation in the number of lineaments identified. Analysis of the maps shows that it is not the case that maps with fewer lineaments are simply subsets of those with many lineaments. Rather, each map contains a high proportion of lineaments that are unique to it. Despite these differences, the same preferred lineament orientation is identified from almost all maps. These results imply that all available LANDSAT imagery may usefully contribute to a lineament analysis, little value may be placed on the density of lineaments seen on any one image and preferred lineament orientation is relatively easy to identify. It is concluded that guarded use may be made of lineament analysis in geology. Lineament maps may be employed to suggest hypotheses rather than to test them.
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