Contribution of remote sensing and GIS to mapping groundwater vulnerability in arid zone: case from Amour Mountains- Algerian Saharan Atlas - Kerzabi, Rachid; Mansour, Hamidi; Yousfi, Somia; Marín-Guerrero, Ana Isabel; Andreo-Navarro, Bartolomé; Bensefia, Kamar-Eddine; Marín-Guerrero, Ana Isabel (Elsevier, 2021)
Protecting groundwater resource from pollution in arid zone is coming an important act for sensing development in this region calling for geomatics tools to characterize the geological and hydrogeological environment. The present work gives a new way to combine remote sensing and geographic information systems to elaborate vulnerability map of Deffa watershed (in Amour Mountains). This region is a good example of arid zones how know an important growth of agriculture, but there is under gap of geological, hydrogeological and soil knowledge. In the first time, we analyzed the Landsat 8-OLI image data with bands combination, ratios composition in RGB and filters to cartography the lithology's contours and lineament map. The false color composition of bands (765, 753, and 543) in RGB given the primary lithological delimitation. Supported by band rationing technique, we produced of 1/50000 geological map. The filter treatments given the lineament map superposed to the first one to realize geo-structural map. In addition, these images served to elaborate pedology map, using Decision Tree (Slope, Redness Index and Lithology parameters). Secondly, we established a GIS including the result map of RS treatment (lithology, lineament and soil maps) and additional spatial information (aquifer type and deep of groundwater surface and precipitations …). In GIS, the vulnerability index are calculated using GOD and PI methods. Both of maps displayed four classes of vulnerability: between Low and Extreme in the first map, and Very low to High vulnerability in the second one. In the some areas, we have controversial values of vulnerability; this leads us to validate these maps using pollution indicators (NO3−, NH4+ and SO42−). The validation displayed that the PI coincides better with special concentrations of pollutants.
Keywords: Aguas subterráneas; Sistemas de información geográfica; Groundwater; Vulnerability; Remote sensing; GIS; Arid zone