ORIGINAL PAPER Fracture network mapping using Landsat-8 OLI, Sentinel-2A, ASTER, and ASTER-GDEM data, in the Rich area (Central High Atlas, Morocco) Ibtissame Bentahar 1 & Mohammed Raji 1 & Hicham Si Mhamdi 2 Received: 17 May 2020 /Accepted: 16 July 2020 # Saudi Society for Geosciences 2020 Abstract Geological mapping using remote sensing is one of the most important applied methods in natural resources exploration. The objectives of this study are mapping and analyzing of fractures distribution in the Rich area in order to understand the influence of lithology and geodynamics on fracture density. For this purpose, we relied on automatic lineament extraction using four types of satellite imagery: Landsat OLI, Sentinel 2A, ASTER L1B, and ASTER Global Digital Elevation Model (GDEM) in order to extract the maximum of lineaments affirmed significative in structural interpretations. After image corrections, the processing of these images is based on the highlighting of structural lineaments and their automatic extraction using the algorithm line of Geomatica software. The validation of linear structures was made based on existing data. The finding showed that each produced map shows systematically a similarity in terms of concentration and orientation with three preferential system-oriented NE-SW, NEE-SSW, E-W, and NNE-SSW. Lineaments mainly follow that of major fault zones, with a high concentration in the North- East part of the study area. This might be due to the importance of the Alpine orogeny deformation as well as the diapirism phenomenon of the Triassic formations in the hiner zone of the Atlas belt. However, the observation shows that the number and total length of structural lineaments could be extracted by using the sentinel 2A then Landsat OLI, ASTER-GDEM, and ASTER L1B set. The automatic extraction allows better mapping of structural lineaments. It shows a good agreement and more infor- mation compared with previous geological data, confirming the efficiency of applied techniques in geological studies. Keywords Central High Atlas . Structural lineaments . Remote sensing . Automatic lineament extraction Introduction Structural mapping is a presentation of the different structures on the Earth’s surface, including linear structural features (fold axis, faults…). Mapping these structures allows understand- ing the dynamics of the movements of the Earth’s crust. Moreover, mapping of structural lineaments is important in geological exploration because it shows the flow of fluid through in these fractures, analysis of these fractures is used to discover reservoirs of groundwater and mineral deposits, and petroleum, as well as to understand the dynamics of the study area (Gad and Kusky 2007; Hamdani 2019; Kumar and Bhandary 2015). Conventional methods used in mapping structural lineaments do not detect all lineaments present in the study area. The integration of remote sensing in structural lineament mapping has contributed to improving the quality of the maps. In which it revealed the abundance of linear structures and significantly improved the knowledge of the study areas, as well as this method is very applicable in areas of difficult access and mountainous. The lineament extraction using remote sensing can be grouped into automatic extraction where the extraction is per- formed by the algorithm based on enhancements including edge detection and filters then extraction (Adiri et al. 2017; Si Mhamdi et al. 2016). The manual extraction or visual in- spection is performed by tracing manually by users on the color composites or band ratios of datasets or using directional filters that enhance the edge (El Alaoui El Moujahid et al. 2016; Kassou et al. 2012). Responsible Editor: Biswajeet Pradhan * Ibtissame Bentahar bentaharibtissame@gmail.com 1 Laboratory of Geodynamics of Old Belts, Department of Geology, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco 2 Department of Geosciences, Faculty of Sciences and Techniques Errachidia, University Moulay Ismail of Meknes, Meknes, Morocco Arabian Journal of Geosciences (2020) 13:768 https://doi.org/10.1007/s12517-020-05736-6