Mapping the Potential of Groundwater Resources in Hard Formations Using Geographic Information System and Remote Sensing, Case Study: Northwest of Shahroud

In recent years, rapid population growth has led to increase per capita water use in various sectors including agriculture and industry and a growing gap between water demand and water supply has emerged. Therefore, identifying and tracking changes in groundwater resources as an alternative and reliable source of surface water resources are so important to region located in the Middle East with dry weather and large volumes of drought and climate changes. In the current study, the potential and evaluation of water resources are investigated in the west and northwest formations of Shahroud in an area of about 480 square kilometres using remote sensing and spatial information techniques. In this region, due to the presence of carbonate rocks as well as the function of erosion and tectonic forces in parts of the region, relatively suitable aquifers have been formed. For this purpose, information layers including lineament, lithology, slope, aspect, waterways, precipitation and type of precipitation were evaluated by Analytical Hierarchy Process (AHP) method. In this study, information layers were prepared using the ability of RS and GIS in three main stages: extraction of geological map, extraction of area lineaments and extraction of other information layers. The proposed methodology applied two different types of remote sensing sensors including Landsat 8 as optical data and Sentinel-1 as radar data. In preparing lithology map with emphasis on calcareous formations, the proposed method applied four techniques including independent component analysis (ICA), minimum noise fraction (MNF), band ratio (BR) and color composition on Landsat 8. Using the pixel purity index, the endmemberes were extracted and the obtained maps were classified by Support Vector Machine (SVM) and maximum likelihood (ML). Among the classification methods, SVM has a higher ability to classify than ML and identified formations with higher accuracy in the region. Finally, tree decision making system was used to improve the classification of images. The geology of the area was extracted in four classes focusing on calcareous formations. The map was compared with the geological map prepared by the surveying organization and the following results were obtained: Kappa coefficient 0.83, Accuracy of Ku and Jd Formation (calcareous formations) with 99.2% accuracy, Shemshak Formation (Js) with 80.2% accuracy, Lalun Formation (Cl) with 81.2% accuracy and Alloviume (Q) with 78% accuracy were identified. Subsequently, lineaments area were extracted using integration of Landsat 8 and Sentinel 1 radar remote sensing data based on semiautomatic methods. Based on the results, the lineaments were in good agreement with the faults in terms of orientation and numbers at different lengths. Also, the densities of the lineaments extracted in different formations of the geological map of the region had 99% compliance with the densities of faults in the formations. Therefore, by combining band6 of Landsat8, VV and VH polarization Sentinel1, the area lineaments can be extracted with high accuracy. Other thematic layers were extracted by using remote sensing and GIS Techniques using 30 m SRTM. It was also observed that the groundwater potential map is mainly controlled by precipitation, lithology, and lineament density factors.