Mapping Prospective Areas of Water Resources and Monitoring Land Use/Land Cover Changes in an Arid Region Using Remote Sensing and GIS Techniques

Abstract

Groundwater is a vital water resource for economic, agricultural, and domestic purposes in arid regions. To reduce water scarcity in arid regions, recently, remote sensing and GIS techniques have been successfully applied to predict areas with prospective water resources. Thus, this study attempted to spatially reveal groundwater potential zones (GWPZs) and to conduct change detection on the desert fringes of Wadi Asyuti, a defunct tributary of Egypt’s Nile basin in eastern Sahara. Eleven influential groundwater factors generated from remote sensing imagery, and geological, hydrological, and climatic conditions were combined after giving a weight to each factor through a GIS-based Analytical Hierarchy Process (AHP) coupled with the weighted overlay technique (WOT). The results revealed six distinctive zones with scores ranging from very low (10.59%) to excellent (3.03%). Thirty-three productive groundwater wells, Interferometry Synthetic Aperture Radar (InSAR) coherence change detection (CCD), a land use map derived from Sentinel-2, and the delineated flooding zone derived from Landsat-8 data were used to validate the delineated zones. The GWPZs indicated that 48% of the collected wells can be classified as consistent to excellent. The Normalized Difference Vegetation Index (NDVI) and image classification were applied to the multi-temporal Landsat series and Sentinel-2 along with the InSAR CCD data derived from Sentinel-1 images to reveal dramatic changes in land use/land cover (LU/LC) in terms of agricultural and other anthropogenic activities in the structurally downstream area, which is the most promising area for future developments. Overall, the integration of radar and multispectral data through the GIS technique has the ability to provide valuable information about water resources in arid regions. Thus, the tested model is a promising technique, and such information is extremely significant for the guidance of planners and decision makers in the area of sustainable development.

Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry

“…Among them, the use of machine learning to optimize the waveform of radar signals will further improve the ability of radar detection, classification, identification, localization, and tracking [169]. At present, for the monitoring of small water areas, the "Land Pollution" in the radar waveform is the main source of error, and it has become possible to use machine learning to reduce or even eliminate "Land Pollution" [170]. Research on machine learning and processing methods of satellite altimetry signals.…”

An Integrated Remote Sensing and GIS-Based Technique for Mapping Groundwater Recharge Zones: A Case Study of SW Riyadh, Central Saudi Arabia

“…GIS and remote sensing (RS) have the potential to be employed for groundwater investigation, as well as for regional predictions [35,36]. Big geographical data may be aggregated and analyzed to forecast and find new water sources by using GIS-based technology and/or data-driven methodologies [37,38]. Several research projects have demonstrated the practicality of combining GIS and RS to locate possible groundwater resource locations [35,[39][40][41].…”

Using Remote Sensing to Assess the Vegetation Cover of a Protected Salt Marsh Subjected to Artificial Recharge and Groundwater Abstractions during the Period 1925–2022 (Alicante, SE Spain)

“…Remote sensing and image processing mapping have been widely applied in the environmental sciences since the beginning of their development [11], and they continue to be employed in works on water management and vegetation cover. For example, to reduce water scarcity in arid regions, remote sensing and GIS techniques have been successfully applied to predict areas with potential water resources [12,13]. Combined remote sensing, GIS (Geographic Information System)-based frequency ratios, and evidential belief delineate and quantify prospective groundwater areas in a model.…”