Fusion of Remote Sensing and Applied Geophysics for Sinkholes

resources

Article Fusion of Remote Sensing and Applied Geophysics for Sinkholes Identiﬁcation in Tabular Middle Atlas of Morocco (the Causse of El Hajeb): Impact on the Protection of Water Resource

Anselme Muzirafuti 1,2,* , Mustapha Boualoul 1, Giovanni Barreca 2, Abdelhamid Allaoui 1, Hmad Bouikbane 1, Stefania Lanza 2,3, Antonio Crupi 2,3 and Giovanni Randazzo 2,3,4

1 Laboratory of Geoengineering and Environment, Department of Geology, Faculty of Sciences, University of Moulay Ismail, BP. 11201 Zitoune, Meknes 50000, Morocco; [email protected] (M.B.); [email protected] (A.A.); [email protected] (H.B.) 2 Interreg Italia–Malta–Progetto: Pocket Beach Management & Remote Surveillance System, University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy; [email protected] (G.B.); [email protected] (S.L.); [email protected] (A.C.); [email protected] (G.R.) 3 GEOLOGIS s.r.l. Spin Oﬀ, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy 4 Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy * Correspondence: [email protected]; Tel.: +39-3312976306

Received: 3 March 2020; Accepted: 20 April 2020; Published: 24 April 2020

Abstract: The Causse of El Hajeb belongs to the Tabular Middle Atlas (TMA), in which thousands of karst landforms have been identified. Among them, collapse dolines and dissolution sinkholes have been highlighted as a source of environmental risks and geo-hazards. In particular, such sinkholes have been linked to the degradation of water quality in water springs located in the junction of the TMA and Saïss basin. Furthermore, the developments of collapse dolines in agricultural and inhabited areas enhance the risk of life loss, injury, and property damage. Here, the lack of research on newly formed cavities has exacerbated the situation. The limited studies using remote sensing or geophysical methods to determine the degree of karstification and vulnerability of this environment fail to provide the spatial extent and depth location of individual karst cavities. In order to contribute to the effort of sinkhole risk reduction in TMA, we employed remote sensing and geophysical surveys to integrate electrical resistivity tomography (ERT) and self-potential (SP) for subsurface characterization of four sinkholes identified in the Causse of El Hajeb. The results revealed the existence of sinkholes, both visible and non-accessible at the surface, in carbonate rocks. The sinkholes exhibited distinct morphologies, with depths reaching 35 m. Topography, geographic coordinates and land cover information extracted on remote sensing data demonstrated that these cavities were developed in depressions in which agricultural activities are regularly performed. The fusion of these methods benefits from remote sensing in geophysical surveys, particularly in acquisition, georeferencing, processing and interpretation of geophysical data. Furthermore, our proposed method allows identification of the protection perimeter required to minimize the risks posed by sinkholes.

Keywords: karst; doline; dolomite; Sentinel-2; geo-hazards; electrical resistivity tomography; self-potential; water protection

Resources 2020, 9, 51; doi:10.3390/resources9040051 www.mdpi.com/journal/resources Resources 2020, 9, 51 2 of 32

1. Introduction Introduced by Fairbridge [1] as subcircular surface depressions or collapse structures created by the collapse of small subterranean karst cavities [2], sinkholes are fascinating structures in karst landscapes. There are also known as dolines [3], or in general, as karst cavities. However, the use of these two terms is not interchangeable and can be differentiated via their development and their evolution aspects. More specifically, sinkholes are defined as cavities resulting from the collapse or cover dolines developed in carbonate or dissolution rocks, while dolines are cavities defined by their morphogenetical mechanisms and hydrological structures [4]. For carbonate rocks with a fluctuating water table, as the level of water in the aquifer drops, unsupported cover-cavities become instable. Furthermore, continuous decreases in the water table and a diminishing roof thickness by gradual erosion consequently result in the inability of some areas to support heavy charges at the surface. This results in the eventual collapse of the roof. Such a phenomenon can occur in both urban areas due to the presence of new infrastructures or buildings, and in rural areas due to the movement of agricultural trucks. The development of sinkholes (or collapse dolines) enhances the risk of injury and life loss, property damage [2] and groundwater pollution. The Causse of El Hajeb forms part of the large area of Tabular Middle Atlas (TMA), where thousands of karst landforms developed in Lower Liassic dolomite and Middle Liassic Limestone have been identified [5]. These carbonate rocks are highly fractured and are generally affected by two fracture systems (NE-SW and NW-SE) in which snow and rainwater precipitation gradually dissolved and created karst landforms. The development of karst landforms in this region is driven by the tectono-karstification process [6]. Among these karst landforms, collapse and cover-subsidence dolines and dissolution sinkholes have been identified as a source of environmental and geo-hazards risks. Chemical analysis performed on water springs located in the junction of the TMA and Saïs Basin [7–10] revealed that, during periods of high precipitation, the degradation of water quality is linked to superficial pollutants infiltrated into reservoirs through sinkholes located in agricultural areas, thus resulting in the internal gradual erosion of karst systems. The pesticides, herbicides and chemical fertilizers used in farming and agricultural production percolate into the groundwater aquifer and consequently change the composition, color and the taste of water springs. Long periods of drought [11], as well as the extraction of groundwater resources for agricultural activities, have reduced the level of water, enhanced the instability of subterranean karst cavities and ultimately increased the sinkhole development frequency in this region. In addition, newly formed cavities are rarely reported, thus further heightening the vulnerabilities in this environment [12]. Previous studies have applied remote sensing [8,13] and traditional geological surveying [14] in the TMA area, providing the degree of karstification of superficial karstic forms developed in carbonate, marno-calcareous rocks, and clays. However, these studies failed to identify their exact subsurface extent and depth location. Furthermore, a large number of sinkholes have previously been identified within a NE-SW fracture system, which has structured the area into graben and plays a big role in surface water infiltration [5]. In order to improve the qualitative interpretation of aeromagnetic data in the Middle Atlas mountain range, Mhiyaoui et al. [15] reinterpreted maps of the digitized magnetic field determined from aeromagnetic surveys over the Middle Atlas and High Moulouya. Results revealed the relationship between fractures affecting carbonate rocks and the development of karstic landforms in the regions of Azrou, Sefrou, El Hajeb and Boulemane. In particular, a major accident oriented NE-SW and second degree accidents oriented NNW-SSE and ENE-WSW, were identified to merge with karst depression alignments developed in Liassic limestone. Moreover, Quaternary volcanism activity increased the development of cryptokarsts, sinkholes and karst cavities in Jurassic fractured carbonate rocks in the direction of major faults. Recent geophysical research in the Causse of El Hajeb [16] characterized the hydrogeological connection between the TMA and the Saiss basin, with a focus on the mapping of fracture networks involved in the hydrogeological system of Bittit water springs. In particular, complex water flow behavior involving probable water drains composed by fractures, karst cavities and stratification joints Resources 2020, 9, 51 3 of 32 was detected via electrical tomography, suggesting the direct connection between identified karst cavities and the groundwater aquifer in this region. Despite the accurate results produced from studies on karst cavities conducted using either geophysical [17–20] or remote sensing [21–24] methods, the fusion of the two techniques results in advantages in the acquisition, processing, and interpretation of geophysical data. Collecting spatial information on electrodes and surveyed points can particularly enhance sinkhole research. Billi et al. [25] combined calibrated electrical resistivity tomography (ERT) and dynamic penetration measurements with well logs to investigate hidden sinkholes and Karst cavities in the highly-populated geothermal seismic territory of the Travertine plateau. The study identified the presence of sinkholes and caves that failed to be detected by previous studies using digital elevation models (DEMs), aerial photography and field surveys. Youssef et al. [26] used remote sensing images with geophysical data in predicting the susceptible areas for sinkholes that may collapse in the future. The study analyzed low resolution Landsat data to identify surface features related to subsurface sinkholes which were ultimately investigated using fieldwork and geophysical explorations. However, remote sensing measurements are not always precise enough to provide the exact coordinates of every point surveyed on the regional scale. This is attributed to the specific geographic coordinate system used for the measurements or the inadequate spatial resolution of the measurements. In order to contribute to the effort of determining a flexible and cost-effective methodology for sinkhole research and as an attempt to overcome the aforementioned problems of the current methods, we propose a method that integrates geo-electrical and remote sensing techniques. Optical satellite imagery (Sentinel-2 and Google images), GPS coordinate and DEM data were re-projected to the local datum (Merchich Degree) by transforming the 1984 World Geodetic System UTM zone 30N into Lambert Conic Conform based on information from Moroccan cartographic zonation (Morocco Z1). We employ the ERT and Self-Potential (SP) methods adapted to areas dominated by clay, basalts and carbonate rocks, along with well log data provided by local authorities. The equipment used for the geo-electrical surveying is capable of providing resistivity ranges for each of these geological formations [27]. SP instrumentation is suitable for hazardous areas due to its simple manipulation and the limited required manpower during the data acquisition. Furthermore, we investigate the local population with a living radius of 1 km from each sinkhole, with a particular focus on how they can behave and live with these sinkholes in terms of adaptation and mitigation to danger. The current study forms part of a larger research initiative on the adaptation and mitigation of the effects of climate change on water resources in the Fez/Meknes region. This region is particularly important for the Moroccan economy due to its agricultural production of olive oil, wheat, maize, and onion. However, the water availability and quality in this region is deteriorating due to the presence of substances introduced into the karst aquifer via sinkholes and karst landforms. To reduce the consequences of this geo-hazard cycle, both the scientific community and general public have been working together to implement solutions for the reduction of sinkhole risks. As part of this, we performed geophysical surveys around four sinkholes identified as the most hostile to both the local communities and to the environment. The methodology adopted to mitigate the risks posed by the presence of sinkholes in the Causse of El Hajeb provides information that can improve the management of the relationship between the needs of the communities and the presence of sinkholes. The main objectives of this study are as follows: (1) To determine and characterize horizontal and vertical repartition collapse dolines; (2) to provide the exact coordinates of the studied sinkholes; and (3) to identify a location for the sinkhole protection perimeter in order to reduce environmental and geo-hazards risks.

2. Geological and Geomorphological Overview of the Middle Atlas During the Triassic period Morocco was subdivided into several basins [28]. The formation of these Triassic basins was initiated during the Permian period and was accentuated during the Triassic by distension. This subsequently led to the formation of the basins, particularly in grabens and Resources 2020, 9, 51 4 of 32 half-grabens with fracture networks distributed across the directions N60, N90 and N120. This rhombic aspect constituted a permanent geometrical characteristic in the Triassic and Jurassic evolution [29,30]; during the early stages of the Pangea breakup in the upper Triassic, visible at the outcrops. The individualization of the basins was carried out via two stages. First, the sinistral motion along the N90 and N120 directions occurred, causing NW-SE distension compatible with the formation of the NE-SW grabens. This was followed by the accentuation of the directional movement along N90 and N120, again due to sinistral motion [30,31]. The filling of these formed basins began with variable detrital material composed by conglomerates, sandstones and arkoses developed in the High Atlas of Marrakech and in the Middle Atlas in the North of Midelt. The filling continued by depositing evaporite argillites in the Middle Atlas and in the Mesetian domain, where it is possible to distinguish the lower and upper saliferous argillites separated by the basaltic complex. This basaltic complex is formed by a stack of lava flow with deposited sedimentary levels that indicate that these basalts are typical oceanic and continental tholeites [32]. The effusions of the basaltic flows were either in a shallow marine environment or in the open air under a hot and arid climate. This intrusive (sill and dyke) or effusive basaltic complex, associated with the explosive volcanism common throughout North Africa and the eastern coast of North America, is one of the manifestations of the Hercynian basement disintegration linked to Atlantic rifting [33]. During the Jurassic period, the Atlas domain differentiated and evolved into regions separated by the old massif of the High Atlas. The Saouira-Agadir lies in the western region, while the eastern region is occupied by the Atlas chain domain, stretching from the East of the Hercyanian central massif to Mediterranean Sea. The Liassic carbonate platforms experienced a great extension during the Sinemurian and Carixian, represented by thick carbonate sedimentation in subsiding zones which can be subdivided into two litho-stratigraphic units separated by a sedimentary discontinuity [34]. During the Liassic period, the indices of the synsedimentary tectonics were frequent, namely the slip, synsedimentary faults and progressive discordances. These synsedimentary tectonics led to the dislocation of the Liassic carbonate platform and the differentiation of the intracontinental basins (from the Middle Atlas and the High Atlas separated by carbonate forms). The middle Liassic–upper Liassic is characterized by a tectonic activity that yielded a major sedimentary discontinuity, condensed stratigraphic deficiency of the Lower Toarcian. This tectonic activity led to the dislocation of the Liassic carbonated platform and the individualization of the basins of the High Atlas and Middle Atlas. Newly formed basins were surrounded by remnant or sub-emerged horst residual platforms (the Highlands and the Causses) which contained subsidence zones with marly sedimentation that evolved into sub-basin depocentres and high zones. The filling of the basins by an essentially carbonated syntectonic sedimentation initiated at the beginning of the lower Toarcian, following sedimentation marly deposits that included carbonate intercalations on the borders of the basins. In the Middle Atlas, these deposits experienced lateral variations in facies. In particular, black micritic limestones were found in the NE direction, while thin reddish limestones on the flanks of rock wrinkles were identified in the SW. Towards the end of the Lower Bajocian, the underwater seabed decreased in depth and the development of the upper Bajocian carbonate platform initiated. The extensive phase of the Mesozoic led to the opening of the basins occupied today by the Middle Atlas. The TMA region (Figure1) is characterized by a landscape of slightly subhorizontal structures, with highly fractured Liassic limestone and dolomite bedrock. The structures form a large karst plateau along the NE-SW direction at an altitude ranging between 700 to 2200 m bordered to the north by the Saiss basin and to the SE by the Middle Atlas. The TMA is affected by two major fracturing systems in the Mesozoic, Cenozoic and Quaternary formations, related to Hercynian tectonics and a post Hercynian event. A NE-SW oriented longitudinal fracturing system, incorporate Triassic and post-Hercynian bedrock units, similar to existing transversal fracturing system oriented NW-SE to ESE-WNW. It is this phenomenon in the NW-SE extension that has favored the generation of NE-SW fractures [35–37]. The Cenozoic was marked by the N-S compression and WNW-ESE extension that Resources 2020, 9, 51 5 of 32

Resources 2020, 9, x FOR PEER REVIEW 5 of 32 favored the genesis of the N-S to ESE-WNW fractures. During the Middle and Upper Quaternary, theextension second that extensive favored phase the genesis of intense of the volcanic N-S to activity ESE-WNW occurred fractures. (e.g., During the Outigui the Middle and El and Koudiate Upper volcanoes)Quaternary, and the the second generation extensive of the phase Tizi N’Trettenof intense accidentvolcanic [activity38]. These occurred fracturation (e.g., the systems Outigui (NE-SW, and El NW-SEKoudiate and volcanoes) ﬂexures) led and to the the current generation structural of the relations Tizi N’Tretten of the TMA. accident The set [38]. of Tizi These N’Tretten fracturation faults dividessystems the (NE TMA-SW, into NW the-SE North and Westflexures) and Southled to Eastthe curre regions.nt structural The NW regionrelations is formedof the TMA. by the The Causses set of d’ImouzzTizi N’Trettenère, Ain faults Leuh, divides Ifrane the and TMA the into Causse the North d El Hajeb,West and while South southern East regions. part by The the NW Causses region of is Annonceurformed by andthe ElCausses Menzel, d’Imouzzère, Guigou and Ain the OumLeuh, Er-Rbia Ifrane basin.and the Causse d El Hajeb, while southern part by the Causses of Annonceur and El Menzel, Guigou and the Oum Er-Rbia basin.

Figure 1. Geologic stretch of the TMA of Morocco with red boxes indicating the location of the area of Figure 1. Geologic stretch of the TMA of Morocco with red boxes indicating the location of the area of study on Moroccan as well as on TMA scales. study on Moroccan as well as on TMA scales. As part of the TMA, the Causse of El Hajeb is lithostratigraphically dominated by weathered As part of the TMA, the Causse of El Hajeb is lithostratigraphically dominated by weathered basalts, red clays and sandstones of the Triassic period, carbonate rocks (dolomite and limestone) of basalts, red clays and sandstones of the Triassic period, carbonate rocks (dolomite and limestone) of the Jurassic era, and basalts, clay, and alluvial deposits of the Quaternary era. These formations are the Jurassic era, and basalts, clay, and alluvial deposits of the Quaternary era. These formations are slightly deformed and gently folded in Jurassic carbonate rocks with approximately 20 –50 dip values. slightly deformed and gently folded in Jurassic carbonate rocks with approximately◦ ◦ 20°–50° dip In addition, they are reversely covered with Paleozoic basement units composed of sandstone and values. In addition, they are reversely covered with Paleozoic basement units composed of sandstone and pelite arranged as turbidity sequences [39]. The latter surrounds the southern part of the little Causse of Agourai oriented to the NE-SW and outcrops the SW limit of the Causse of El

Resources 2020, 9, 51 6 of 32 pelite arranged as turbidity sequences [39]. The latter surrounds the southern part of the little Causse of Agourai oriented to the NE-SW and outcrops the SW limit of the Causse of El Hajeb. The carbonate rocks are affected by many fractures oriented NE-SW and NW-SE and define the structural framework of this area, with numerous blocks and narrow distensive structures corresponding to half-grabens [40]. Initial information on the Karstic phenomena in the Atlas Middle Causse was collected from studies carried out in the Atlas Middle Causse [13,14,41]. The development of different karstic forms in the TMA is conditioned in part by the presence of the subhumid to humid climate characterized by low temperatures and by the abundance of winter snow. These conditions favor the dissolution of the highly fractured Liassic dolomitic and limestone rocks and evaporite rocks of the Triassic, with different karstic forms developed in deep layers as well as close to the surface (e.g., avens, sinkholes, galleries, collapse chasms, caves, lapiez, polje and porches). Some of these karst landforms are invisible on the surface, unlike the very frequent sinkholes with diameters ranging from 15 to 12 m. This is often due to the accumulation of snow which led to the collapse of roofs of underground galleries and the pockets of salt dissolution during the Triassic, or due to equipment used in agricultural activities. Accumulated alteration products of carbonate rocks or reddish clay minerals are often located at the bottom of the karst depressions. This accumulation leads to the formation of clay residues that can circulate in the groundwater to the exit at the sources. In regions where the Plio-Quaternary basaltic flows overlaid on calcareous substratum, large sinkholes, with depths ranging from 20 to 30 m, are visible on the surface. This karstic environment hosts an important underground aquifer in which the meteoric water (snow or rain) infiltrates through cracks and voids drains [6,42] and recharges the groundwater aquifer. Groundwater formed in carbonate rocks in the Lower and Middle Liassic rocks arrives on the surface at the level of local lake system in the basins and at the levels of the waters springs (resurgences or exsurgences), particularly at the point of contact with the TMA and Saiss Basin. During periods of intense rainfall, water turbidity is observed [36], reducing water quality. This turbidity can be attributed internally to the leaching of the karstic network in which meteoric water circulates, and also externally, at the level of infiltration within different karst landforms [7,41]. Our study region (Figure2) has an approximate area of 3.5 km 2.4 km and is located in the × Causse of El Hajeb at 37 km, 45 km and 15 km from the regional main cities of Meknes, Fez and El Hajeb respectively. It is an area characterized by rich soil cover and abundant groundwater; these conditions have favored agricultural activities and regional economic development. The presences of fractured and weathered quaternary basalts deposed on major faults affecting Jurassic carbonate rocks have favored the development of subsidence sinkholes in or around agricultural fields. However, anthropogenic activities have increased the frequency of collapsed or subsidence sinkholes developed on superficial carbonate rocks of limited thickness. This can be attributed to the displacement of large basalts by trucks which subsequently apply pressure on the thin underground cavities roofs and eventually trigger their sudden collapse. In addition to this, the use of fertilizers and other types of chemicals alter the water surface composition, infiltrating through faults and fractures to gradually dissolve the carbonate rocks, and ultimately leading to the creation of karst cavities. Resources 20Resources20, 9, x2020 FOR, 9 ,PEER 51 REVIEW 7 of 32 7 of 32

Figure 2. Geological map extract of El Hajeb (1:100,000) indicating the study sites location. The first Figure 2.site Geological represents areamap hosting extract buried of El sinkholes Hajeb filled(1:100 with,000) basalts indicating and dropout the doline study whereas sites location. the Second The first site representssite represents area areahosting hosting buried funnel sinkholes shaped collapse filled sinkhole with withbasalts an approximate and dropout depth doline of 35 m whereas and the Second sitesolution represents sinkhole area with hosting an approximate funnel depth shaped of 50 collapse m. sinkhole with an approximate depth of 35 m and3. Methodologysolution sinkhole and Datasets with an approximate depth of 50 m. The datasets consist of satellite imagery and measurements collected in the field. Satellite images 3. Methodology and Datasets were re-projected using a local coordinate system projection (Table1) in order to minimize cartographic Thedistortions datasets and consist measurement of satel errorslite during imagery the planning and measurements of the fieldwork, as collected well for the in determination the field. Satellite of sinkhole locations. The remotely identified sinkhole locations were validated during the fieldwork. images were re-projected using a local coordinate system projection (Table 1) in order to minimize Furthermore, geophysical surveying was used to examine the vertical and horizontal extent of the cartographicsinkholes. distortions Interpretations and measurement of geophysical data errors were during based on the borehole planning data, geologicof the fieldwork, information andas well for the determinationon site direct of observations. sinkhole locations. The remotely identified sinkhole locations were validated during the fieldwork. Furthermore, geophysical surveying was used to examine the vertical and Table 1. The principle characteristics of the Moroccan Geographic coordinate system (Morocco Z1) horizontal extentused for of the the transformation sinkholes. ofInterpretations satellite imagery, GPS of geo coordinatephysical and DEMdata 84WGSwere UTMbased zone on 30N borehole data, geologic informationprojection system and intoon site Lambert direct Conic observations. Conform.

Morocco Z1: Geographic Coordinate System, Transformation Parameters Table 1. The principleMerchich characteristics Degree of the Moroccan Geographic coordinate system (Morocco Z1) used for the transformationProjection of satellite imagery, GPS coordina Lambertte and Conformal DEM 84WGS Conic UTM zone 30N projection system into LambertDatum Conic Conform. D_Merchich Spheroid “Clarke_1880_IGN”,6378249.2,293.46602 Morocco Z1: GeographicPrime Coordinate Meridian System, “Greenwich”,0.0 Linear unitTransformation “Meter”,1.0 Parameters Merchich AngularDegree unit “Degree”,0.0174532925199433 ProjectionFalse eastingLambert 500000.0 Conformal Conic False northing 300000.0 Central meridian 5.4 − DatumStandard parallel_1 34.86645765555556D_Merchich Standard parallel_2 31.72392564722222 SpheroidScale factorClarke_1880_IGN”,6378249.2,293.46602 0.999625769 Prime MeridianLatitude of origin 33.3Greenwich”,0.0 Linear unit “Meter”,1.0 Angular unit Degree”,0.0174532925199433 False easting 500000.0 False northing 300000.0 Central meridian −5.4 Standard parallel_1 34.86645765555556 Standard parallel_2 31.72392564722222 Scale factor 0.999625769 Latitude of origin 33.3

Resources 2020, 9, 51 8 of 32 Resources 2020, 9, x FOR PEER REVIEW 8 of 32 The Figure3 details the methodology followed in order to identify sinkholes presenting high risks Theto the Figure environment 3 details and the protection methodology zones for followed groundwater in order resources to identify in the Fez sinkholes/Meknes region. presenting high risks to the environment and protection zones for groundwater resources in the Fez/Meknes region.

Figure 3. Flowchart of the methodological steps adopted in this study. Figure 3. Flowchart of the methodological steps adopted in this study. 3.1. Remote Sensing and Geophysical Data Acquisition and Processing 3.1. RemoteSatellite Sensing images and Geophysical can optimally Data retrieve Acquisition information and onProcessing large-scale and remote areas. Unlike direct Satellitefield measurements, images can the optimally acquisition retrieve of satellite information remote sensing on data large does-scale not and require remote administrative areas. Unlike director field local measurementsown authorization., the Contrary acquisition to aerial of photogrammetry satellite remote (using sensing drone), data satellites does have not the require ability to provide land cover and land use information at very large area in one single acquisition. In administrative or local own authorization. Contrary to aerial photogrammetry (using drone), addition, the high temporal resolution, satellite allows the monitoring of natural phenomena as well satellitesas change have detectionthe ability of to landscape. provide We land used cover a multispectral and land Sentinel-2Ause information image acquiredat very large on 30 Marcharea in one single2017 acquisition. (specifications In addition,provided in the Table high2), the temporal Advanced resolution, Space borne Thermalsatellite Emission allows and the Reflection monitoring of naturalRadiometer phenomena global as digital well as elevation change model detection (ASTER-GDEM) of landscape. product We Version used 1a acquiredmultispectral on 17 October Sentinel-2A image2011 acquired (provided on by30 the March Ministry 2017 of Economy,(specifications Trade, andprovided Industry in (METI) Table of 2), Japan the andAdvanced the United Space States borne ThermalNational Emission Aeronautics and Reflectionand Space Administration Radiometer (NASA))global digital and Google elevation Earth image model acquired (ASTER on- 19GDEM) productJuly Version 2019. 1 acquired on 17 October 2011 (provided by the Ministry of Economy, Trade, and Industry3.2. Multispectral (METI) of Japan Sentinel-2A and Image the United States National Aeronautics and Space Administration (NASA)) and Google Earth image acquired on 19 July 2019. The Copernicus Sentinel-2A satellite, along with its twin Sentinel-2B, is part of a satellite series developed by the European Union Earth Observation program. Launched on 23 June 2015 at an 3.2. Multispectral Sentinel-2A Image altitude of 786 km for a 7-year mission, the Sentinel-2A satellite monitors the Earth’s surface and the Thechanges Copernicus resulting Sentinel from natural-2A satellite, phenomena along or human with its activities. twin Sentinel It provides-2B, is multispectral, part of a satellite high to series developedmedium by spatial the European resolution Union images Earth with 13 Observation bands in the visible, program. near Launched infrared (NIR) on and23 June shortwave 2015 at an infrared (SWIR) for a temporal resolution of 10 days (5 days when combined with Sentinel-2B). The data altitude of 786 km for a 7-year mission, the Sentinel-2A satellite monitors the Earth’s surface and the acquired by Sentinel satellites constellation is managed by the European Commission and distributed changes resulting from natural phenomena or human activities. It provides multispectral, high to by the European Space Agency (ESA) via the Copernicus Open Access Hub. The images can be mediumaccessed spatial and resolution downloaded images for free with at https: 13 bands//scihub.copernicus.eu in the visible, /neardhus /infrared#/home for (NIR) regions and located shortwave infraredwithin (SWIR) 84◦ N for and a 56temporal◦ S. resolution of 10 days (5 days when combined with Sentinel-2B). The data acquired by Sentinel satellites constellation is managed by the European Commission and distributed by the European Space Agency (ESA) via the Copernicus Open Access Hub. The images can be accessed and downloaded for free at https://scihub.copernicus.eu/dhus/#/home for regions located within 84° N and 56° S. Sentinel-2 images are frequently applied in many fields, particularly for water management and land protection purposes [43], as well as the management of agricultural plots [44], the identification and monitoring of crops [45] and for the risk management of floods [46], forest fires [47], and coastal water contaminant [48]. Additional applications include geological mapping [49] and the monitoring of geological waste [50]. In the current study, we employed Sentinel-2A imagery in order

Resources 2020, 9, 51 9 of 32

Table 2. Speciﬁcations of the Copernicus Sentinel-2 image employed in the data analysis.

Sentinel-2 Bands Spatial Resolution Wavelength (nm) Application Band-1 Ultra blue 60 m 433–453 Aerosol correction Band-2 Blue 10 m 458–523 Aerosol correction, sensitive to vegetation stress, land cove/use mapping Band-3 Green 10 m 543–578 Sensitive to total chlorophyll, land cover/use mapping Band-4 Red 10 m 650–680 Maximum absorption of chlorophyll, land cover/use mapping Band-5 Near infrared (NIR) 20 m 698–713 Consolidation of atmospheric correction/baseline of Fluorescence, land cover/use mapping Band-6 Near infrared (NIR) 20 m 734–748 Atmospheric correction, recovery of aerosol charge, land cover/use mapping Band-7 Near infrared (NIR) 20 m 765–785 Land cover/use mapping Band-8 Near infrared (NIR) 10 m 785–900 Leaf area index, water vapor correction, land cover/use mapping Band-8A Near infrared 20 m 855–875 Water vapor absorption reference, land cover/use mapping Band-9 Shortwave infrared (SWIR) 60 m 930–950 Water vapor absorption reference, land cover/use mapping, atmospheric correction Band-10 Shortwave infrared (SWIR) 60 m 1365–1385 Cirrus cloud detection for atmospheric correction, land cover/use mapping Soil detection, sensitive to above ground forest biomass, cloud/snow/ice separation, land Band-11 Shortwave infrared (SWIR) 20 m 1565–1655 cover/use mapping. Soil mineral mapping, soil erosion monitoring, stressed biomass and soil distinction, wildﬁre Band-12 Shortwave infrared (SWIR) 20 m 2100–2880 mapping, land cover/use mapping Resources 2020, 9, 51 10 of 32

Sentinel-2 images are frequently applied in many fields, particularly for water management and land protection purposes [43], as well as the management of agricultural plots [44], the identification and monitoring of crops [45] and for the risk management of floods [46], forest fires [47], and coastal water contaminant [48]. Additional applications include geological mapping [49] and the monitoring of geological waste [50]. In the current study, we employed Sentinel-2A imagery in order to identify the extent of agricultural areas, the distribution of geological formations, and the location of potential karst landforms. Sentinel-2A data was combined with images from Google Earth and ASTER-GDEM, allowing us to identify land cover types in the Causse of El Hajeb. The ASTER-GDEM (version 2), acquired on 17 October 2011 with a spatial resolution of 30 m, was downloaded from the US geological survey website (http://earthexplorer.usgs.gov).

3.2.1. Remote Sensing Data Processing and Interpretation The detection and extraction of sinkholes via the analysis of remote sensing data was performed using a GIS-environment. Satellite imagery can provide precise information on vegetation, urban development, and bare soil. In the current study, we analyzed the vegetation and the bare soil, with the analysis procedure detailed as follows. In the first step, we determined the geologic formation extent likely to host karst landforms. More specifically, the delineation of the outcrop geological formation was performed using the level-2A ResourcesSentinel-2A 2020, 9, x FOR image PEER using REVIEW SNAP desktop (version 6.0, ESA, Paris, France) and ArcGIS (version 10.1,10 of 32 Esri, Redland, CA, USA). After resizing the spatial resolution of all bands to 10 m via SNAP desktop, combinationArcGIS was using used the for SWIR, the re-projection NIR, red tobands. the local These coordinates bands andwere false used band based combination on the usingreflectance the of land SWIR, cover NIR, types, red whereby bands. These vegetation bands were exhibits used based a peak on the in reflectance the NIR band of land whereas cover types, carbonate whereby rocks vegetation exhibits a peak in the NIR band whereas carbonate rocks exhibits a peak in the SWIR band. exhibits a peak in the SWIR band. The false color RGB image reveals the location of carbonate rocks, The false color RGB image reveals the location of carbonate rocks, basalts, and red clays, as well as the basalts,vegetation and red within clays, agricultural as well as the areas vegetation and numerous within trees agricultural (Figure4). areas and numerous trees (Figure 4).

Figure 4. False-color Sentinel-2 image (bands 11, 8, and 5) demonstrate the principle land cover Figuretypes: 4. False vegetation-color (green Sentinel colors),-2 image basalts (bands (purple), 11, and 8, carbonate and 5) demonstraterocks (pink). Derived the principle from a level-2A land cover types:Sentinel-2A vegetation acquired (green on colors), 30 March basalts 2017 from (purple), the Copernicus and carbonate Open Access rocks Hub. (pink). Derived from a level-2A Sentinel-2A acquired on 30 March 2017 from the Copernicus Open Access Hub.

Second, sinkholes located on the geologic formations were extracted from the ASTER-GDEM using ArcGIS hydrological tool. The fill sinks is commonly used in hydrological studies to determine the flow direction of surface runoff without taking into account the effect of sinks and depression information recorded during DEM data processing. In a karst environment, these sinks and depressions can be related to the presence of cavities or sinkholes. It is possible to identify these karst landforms by subtracting the initial ASTER-GDEM raster from the output ASTER-GDEM raster derived from the fill sink algorithm. This technique has previously been used for the automatic delineation of karst sinkholes on high spatial resolution data [51–53]. In the current study, a 30 m spatial resolution was too course to identify individual sinkholes. Thus, we applied a triangulated irregular network (TIN) derived from ASTER-GDEM via the Delaunay triangulation method. This method has the advantage of forming triangles from nearby points, hence providing a good representation of the topography of a given area [54]. We computed the TIN in order to analyze the ASTER- GDEM karst depressions and compare their locations with the land cover information derived from the S-2A data, the geologic map of El Hajeb (published in 1975 with a scale of 1/100,000), and the Google Earth image (Figure 5). A detailed investigation was subsequently conducted during fieldwork in the study area, whereby the coordinates of the identified sinkholes were recorded using a GPS Garmin. These coordinates were then manually digitalized on TIN topography of the studied area in ArcGIS. In summary, sinkhole identification was conducted in five steps: (1) The identification of carbonate rocks (on geologic maps and multispectral images); (2) the delineation of karst depressions on ASTER- GDEM; (3) the identification of farmlands located in and near the karst depressions; (4) the identification of sinkholes and the recording of their coordinates in the field; and (5) the transformation of coordinates and digitization of sinkholes on the TIN elevation model.

Resources 2020, 9, 51 11 of 32

Second, sinkholes located on the geologic formations were extracted from the ASTER-GDEM using ArcGIS hydrological tool. The fill sinks is commonly used in hydrological studies to determine the flow direction of surface runoff without taking into account the effect of sinks and depression information recorded during DEM data processing. In a karst environment, these sinks and depressions can be related to the presence of cavities or sinkholes. It is possible to identify these karst landforms by subtracting the initial ASTER-GDEM raster from the output ASTER-GDEM raster derived from the fill sink algorithm. This technique has previously been used for the automatic delineation of karst sinkholes on high spatial resolution data [51–53]. In the current study, a 30 m spatial resolution was too course to identify individual sinkholes. Thus, we applied a triangulated irregular network (TIN) derived from ASTER-GDEM via the Delaunay triangulation method. This method has the advantage of forming triangles from nearby points, hence providing a good representation of the topography of a given area [54]. We computed the TIN in order to analyze the ASTER- GDEM karst depressions and compare their locations with the land cover information derived from the S-2A data, the geologic map of El Hajeb (published in 1975 with a scale of 1/100,000), and the Google Earth image (Figure5). A detailed investigation was subsequently conducted during fieldwork in the study area, whereby the coordinates of the identified sinkholes were recorded using a GPS Garmin. These coordinates were then manually digitalized on TIN topography of the studied area in ArcGIS. In summary, sinkhole identification was conducted in five steps: (1) The identification of carbonate rocks (on geologic maps and multispectral images); (2) the delineation of karst depressions on ASTER- GDEM; (3) the identification of farmlands located in and near the karst depressions; (4) the identification of sinkholes and the recording of their coordinates in the field; and (5) the transformation of coordinates and Resources 20digitization20, 9, x FOR of PEER sinkholes REVIEW on the TIN elevation model. 11 of 32

Figure 5.Figure Identification 5. Identiﬁcation of sinkholes of sinkholes by bycomparing comparing (A (A)) a regionalregional geological geological map map of El of Hajeb El Hajeb (B) (B) a false colora false Sentinel color Sentinel-2A-2A image image using using the the SWIR, SWIR, NIR NIR and Red Red bands, bands, (C) ( theC) TIN the derived TIN derived via the via the ASTER-GDEM, and a high spatial resolution Google image containing sinkholes (D,E) identiﬁed in ASTER-GDEM,agricultural and areas. a high spatial resolution Google image containing sinkholes (D,E) identified in agricultural areas.

Geophysical surveys were conducted on the second site in order to study the subsurface behavior of two sinkholes identified close to the farmland.

3.2.2. Geophysical Data Acquisition In order to investigate surface and subsurface extensions of the identified sinkholes, we conducted geophysical surveys using electrical resistivity tomography (ERT) and Self-potential (SP). These non-invasive geoelectrical methods are generally employed in the measurements of electrical properties (electrical resistivity and electrical potential) of lithologic and pedologic formations in karst environments [55–58]. Advancements in equipment and software have increased both the speed and accuracy of the detection of sinkholes across different geologic formations. In particular, the application of non-polarizing electrodes combined with multimeters to measure ground surface electrical potential has previously been analyzed [59] to monitor SP anomalies associated with sinkholes. Furthermore, Zhou et al. [60] evaluated the advantages and disadvantages of different electrode configuration arrays for depth investigation, sensitivity to horizontal and vertical variations, and for the signal strength of electrode configurations by evaluating the resistivity of subsurface materials. The authors detected that images obtained using a dipole-dipole array were of a higher spatial resolution compared to those from the Wenner and Schlumberger arrays in displaying the sinkhole collapse areas. They concluded the dipole–dipole array as the most effective and less costly configuration in mapping karst hazards areas [60]. In the current study, four ERT profiles were acquired using a dipole-dipole array placed around two sinkholes. In addition, two SP surveys were conducted in order to produce a map of the electrical potential distribution at the ground surface. The fusion of ERT and SP was able to provide a clear picture of the sinkhole extension at the surface as well as underground. Geographic coordinates were registered for each measurement in order to enhance the geolocation precision of the studied sinkholes and the geo-electrical measurements. This allowed for the acquisition and processing of geophysical data and for the subsequent interpretation of the results.

Resources 2020, 9, 51 12 of 32

Geophysical surveys were conducted on the second site in order to study the subsurface behavior of two sinkholes identiﬁed close to the farmland.

3.2.2. Geophysical Data Acquisition In order to investigate surface and subsurface extensions of the identified sinkholes, we conducted geophysical surveys using electrical resistivity tomography (ERT) and Self-potential (SP). These non-invasive geoelectrical methods are generally employed in the measurements of electrical properties (electrical resistivity and electrical potential) of lithologic and pedologic formations in karst environments [55–58]. Advancements in equipment and software have increased both the speed and accuracy of the detection of sinkholes across different geologic formations. In particular, the application of non-polarizing electrodes combined with multimeters to measure ground surface electrical potential has previously been analyzed [59] to monitor SP anomalies associated with sinkholes. Furthermore, Zhou et al. [60] evaluated the advantages and disadvantages of different electrode configuration arrays for depth investigation, sensitivity to horizontal and vertical variations, and for the signal strength of electrode configurations by evaluating the resistivity of subsurface materials. The authors detected that images obtained using a dipole-dipole array were of a higher spatial resolution compared to those from the Wenner and Schlumberger arrays in displaying the sinkhole collapse areas. They concluded the dipole–dipole array as the most effective and less costly configuration in mapping karst hazards areas [60]. In the current study, four ERT profiles were acquired using a dipole-dipole array placed around two sinkholes. In addition, two SP surveys were conducted in order to produce a map of the electrical potential distribution at the ground surface. The fusion of ERT and SP was able to provide a clear picture of the sinkhole extension at the surface as well as underground. Geographic coordinates were registered for each measurement in order to enhance the geolocation precision of the studied sinkholes and the geo-electrical measurements. This allowed for the acquisition and processing of geophysical data and for the subsequent interpretation of the results.

Electrical Resistivity Survey Using ERT ERT, an active geophysical prospecting method, measures the electrical resistivity (Equation (1)) following the injection of an electric current into the ground at the level of the electrodes. The circulation of this current depends on the chemical composition, lithology and underground structures of the prospected zone. 1 ρ = (1) σ where ρ (Ωm) is resistivity and σ (S/m) is conductivity. Electrical resistivity surveying is based on Ohm’s Law, which governs the ﬂow of the current injected into the ground. Equation (2) is Ohm´s Law in vector form for the current density ﬂow in a continuous medium [61] and demonstrates the relationship between the current density and the conductivity of soil and geologic formations.

J = σE (2)

2 where J (A m− )is current density and E (V/m) is the electric ﬁeld intensity. The gradient expressed in Equation (3) shows the relationship between electric ﬁeld intensity and electrical potential: E = Φ (3) −∇ where Φ (V) is electrical potential due to point sources. Resources 2020, 9, 51 13 of 32

The combination of Equations (2) and (3) gives the relation of current density with electrical potential and the conductivity expressed as follows:

J = σ Φ (4) − ∇ During the ERT surveying process, the positions of the electrodes are determined by the X, Y, and Z coordinates. These electrodes are the current source (I). The relationship between current density (J) and the current (I) is given by Equation (5):

I J = δ(X Xs)δ(Y Ys)δ(Z Zs) (5) ∇ ∆V − − − where ∆V is the elemental volume surrounding the electrodes and δ is the Dirac delta function. Following Ohm’s Law, where the current is considered to spread in all direction, Equation (4) can be rewritten in terms of the potential distribution in the ground due to a point current source as follows: ( ) 1 I Φ(X, Y, Z) = δ(X Xs)δ(Y Ys)δ(Z Zs) (6) − ∇ × ρ(X, Y, Z) ∇ ∆V − − −

The resolution process in Equation (6) allows us to determine the electrical potential at the ground surface due to point current sources. Apparent resistivity ERT ﬁeld measurements of soil and geologic formations are conducted by injecting a current into the ground through two current electrodes (C1 and C2) and measuring the resulting voltage diﬀerence at two potential electrodes (P1 and P2) expressed by Equation (7) as follows:

∆Φ ρ = K (7) a I × Resources 2020, 9, x FOR PEER REVIEW 13 of 32 where ρa is the apparent resistivity, K is a geometric factor that depends on the arrangement of the electrodes, I is the electrical current and ∆Φ the electrical potential. The Noteapparent that the resistivity instrument is used determined to measure by geologic incorporating and soil formation geometric resistivity factor K. generally For ERT records surveys conductedthe resistance using a values dipole as- follows:dipole array, K is expressed as follows: ∆Φ R = (8) K = π × a × (n) × (In + 1) × (n + 2) (9) where π Theis the apparent constant, resistivity a is horizontal is determined electrode by incorporating spacing (for geometric current source factor K.electrodes For ERT surveysor potential conducted using a dipole-dipole array, K is expressed as follows: measuring electrodes) and n is an integer representing the number of spacing times between current source electrodes and potential measuringK = π a electrodes(n) (n + as1 )shown(n + on2) Figure 6. (9) The final apparent resistivity measured× × by ERT× using× a dipole-dipole array can thus be written as follows:where π is the constant, a is horizontal electrode spacing (for current source electrodes or potential measuring electrodes) and n is an integer representing the number of spacing∆V times between current source electrodes and potentialρₐ = measuringπ × a × ( electrodesn) × (n + as1) shown× (n + on2) Figure× 6 . (10) I

Figure 6. Dipole-dipole array conﬁguration for resistivity surveys for n = 1, where C1 and C2 represent Figurethe 6.current Dipole source-dipole electrodes array and configuration P1 and P2 the for potential resistivity measuring surveys electrodes. for n = 1, where C1 and C2 represent the current source electrodes and P1 and P2 the potential measuring electrodes.

The investigation depth depends on the length of the profile used, and the spatial resolution of a resistivity model section is a function of the distance between the electrodes. During the ERT survey process, the electrode separation (a) is kept constant while n is constantly increased in order to measure the resistivity of deep structures. Potential measurements are performed at several points at different depths for each electrode. The Figure 7 illustrates an example of ERT survey for dipole-dipole configuration with 14 electrodes, during the acquisition of apparent resistivity; two electrodes are taken as the current source while the rest are used for potential measurements. The results obtained from several positions form a pseudosection of the apparent measured resistivity. The measurements are automatically controlled via an algorithm located in the acquisition instrument which selects the appropriate four electrodes for each measurement. During the ERT measurements with a dipole-dipole array, we set the electrode separation to 2 m for three profiles and 5 m for one profile for 64 electrodes. From the measurements, we were able to obtain a 2D subsurface electrical model where the variation of resistivity revealed the location of the sinkholes and different enclosing geologic formations. The acquisition equipment (Figure 8) is manufactured by IRIS instruments and includes a Syscal PRO resistivity meter that measures the apparent resistivity, a 12 volt battery, 64 stainless steel electrodes, four yellow colored cables to connect the electrodes to the resistivity meter, four cable connectors and a Garmin GPS to register the geographic coordinates of the electrodes.

Resources 2020, 9, 51 14 of 32

The final apparent resistivity measured by ERT using a dipole-dipole array can thus be written as follows: ∆V ρ = π a (n) (n + 1) (n + 2) (10) a × × × × × I The investigation depth depends on the length of the profile used, and the spatial resolution of a resistivity model section is a function of the distance between the electrodes. During the ERT survey process, the electrode separation (a) is kept constant while n is constantly increased in order to measure the resistivity of deep structures. Potential measurements are performed at several points at different depths for each electrode. The Figure7 illustrates an example of ERT survey for dipole-dipole configuration with 14 electrodes, during the acquisition of apparent resistivity; two electrodes are taken as the current source while the rest are used for potential measurements. The results obtained from several positions form a pseudosection of the apparent measured resistivity. The measurements are automatically controlled via an algorithm located in the acquisition instrument which selects the appropriate four electrodes for each measurement. During the ERT measurements with a dipole-dipole array, we set the electrode separation to 2 m for three profiles and 5 m for one profile for 64 electrodes. From the measurements, we were able to obtain a 2D subsurface electrical model where the variation of resistivity revealed the location of the sinkholes and different enclosing geologic formations. The acquisition equipment (Figure8) is manufactured by IRIS instruments and includes a Syscal PRO resistivity meter that measures the apparent resistivity, a 12 volt battery, 64 stainless steel electrodes, four yellow colored cables to connect the electrodes to the Resources 20resistivity20, 9, x FOR meter, PEER four REVIEW cable connectors and a Garmin GPS to register the geographic coordinates of 14 of 32 the electrodes.

Figure 7. ERT survey example: The plot of the dipole-dipole apparent resistivity on a pseudosection. Figure 7.Electrical ERT survey potential example: measurements The plot are performedof the dipole at several-dipole positions apparent for every resistivity current source on a pseudosection. location Electrical(from potential a to k). We measurements used a 2 m spacing are distance performed for both at the several current positions electrodes (C1, for C2) every and potential current source electrodes (P1, P2). The separation of current electrodes (I) and potential electrodes (V) is equal to n location (from a to k). We used a 2 m spacing distance for both the current electrodes (C1,× C2) and 2 m. The arrows indicate the orientation of the measurements; the red dots indicate the measurement potentiallocation electrodes at the intersection(P1, P2). The of two separation lines, at 45 degrees, of current to the centerelectrodes of the current(I) and and potential potential electrodes.electrodes (V) is equal to n × 2 m. The arrows indicate the orientation of the measurements; the red dots indicate the measurement location at the intersection of two lines, at 45 degrees, to the center of the current and potential electrodes.

Figure 8. The acquisition equipment manufactured by IRIS and includes battery, resistivimeter, electrodes, coil, cables, and cable connector.

3.2.2.2. SP Field Measurements SP is a passive geophysical prospecting method that measures the distribution of natural electrical potential at the ground surface. This electrical potential is related to the electric charge polarization mechanisms observed in the porous or microcracked medium due to the chemical potential gradient of the carriers of ionic or electronic charges. The origin of this gradient makes it possible to distinguish the SP anomaly in three main categories. We distinguish the SP signal of (1) electrochemical origin (fluid diffusion through pedologic and lithologic contacts), (2) electrokinetic phenomena (underground water flow through pores, fractures, and cavities), and (3) thermal origin (namely subterranean temperature). SP can generally be attributed to underground stream flows in fractured and porous areas such as karstic environments. The SP method has previously been

Resources 2020, 9, x FOR PEER REVIEW 14 of 32

Figure 7. ERT survey example: The plot of the dipole-dipole apparent resistivity on a pseudosection. Electrical potential measurements are performed at several positions for every current source location (from a to k). We used a 2 m spacing distance for both the current electrodes (C1, C2) and potential electrodes (P1, P2). The separation of current electrodes (I) and potential electrodes (V) is equal to n × 2 m. The arrows indicate the orientation of the measurements; the red dots indicate the Resourcesmeasurement2020, 9, 51 location at the intersection of two lines, at 45 degrees, to the center of the current and 15 of 32 potential electrodes.

FigureFigure 8. 8. TheThe acquisition acquisition equipment equipment manufactured by by IRIS IRIS and and includes includes battery, battery, resistivimeter, resistivimeter, electrodes,electrodes, coil, coil, cables, cables, and and cable cableconnector. connector.

SP3.2.2.2. Field MeasurementsSP Field Measurements SPSP is ais passive a passive geophysical geophysical prospecting prospecting method method that measures that measures the distribution the distribution of natural of naturalelectrical potentialelectrical at potential the ground at the surface. ground This surface. electrical This potential electrical is potential related to is the related electric to thecharge electric polarization charge mechanismspolarization observed mechanisms in the observed porous or in microcracked the porous mediumor microcracked due to the medium chemical due potential to the gradient chemical of thepotential carriers gradient of ionic orof electronic the carriers charges. of ionic The or originelectronic of this charges gradient. The makes origin it of possible this gradient to distinguish makes it the SPpossibl anomalye to indistinguish three main the categories. SP anomaly We in distinguish three mainthe categories. SP signal We of distinguish (1) electrochemical the SP signal origin of (fluid (1) dielectrochemicalffusion through origin pedologic (fluid and diffusion lithologic through contacts), pedologic (2) electrokinetic and lithologic phenomena contacts), (underground (2) electrokinetic water flowphenomena through pores,(underground fractures, water and cavities),flow through and (3)pores, thermal fractures, origin and (namely cavities), subterranean and (3) thermal temperature). origin SP(namely can generally subterranean be attributed temperature). to underground SP can generally stream be flows attributed in fractured to underground and porous stream areas flows such in as karsticfractured environments. and porous The areas SP such method as karstic has previously environments. been employed The SP method to determine has previously the location been of subsurface cavities [62] and the preferential groundwater flow pathways in sinkholes [59], and to investigate the vertical infiltration of water from agricultural ditches into sinkholes [63]. Furthermore, Ikard et al. [64] applied the fusion of SP with ERT to identify the occurrence of preferential groundwater seepage in karst terrain. In the current study, we performed SP signal measurements at the ground surface using non-polarizing electrodes connected to a multimeter in voltage mode (with impedance at 10 MΩ) via an electric cable. Equation (11) represents the relationship between the electrical current density at point M and the measured SP signal at non-polarizing electrode P. This electrical potential signal ϕ (mV) can be computed as follows:

Z1 ϕ(P) = K(P, M)jS(M)dV (11)

Ω where jS is the source current density (in both saturated and unsaturated conditions), K(P,M) is the kernel connecting the ϕ(P) signal measured at a set of non-polarizing electrodes P (with respect to a reference electrode) and the source of current at point M in the conducting ground. It is important to note that this kernel depends on a number of factors including the number of measurement stations at the ground surface (Figure9) as well as the distribution of resistivity [ 55]. The total current density is expressed as follows (Equation (12)) [65,66]:

θs j = σ(θ) ϕ + Qv u (12) − ∇ θ where j is the electrical current density (A m2), σ is the electric conductivity of the porous material, ϕ is the SP, Qv is the excess charge of the diﬀuse layer of the pore water per unit pore volume and depends on the permeability of the porous material, θ is the water content, θs is the porosity, and u is the Darcy velocity. Resources 2020, 9, x FOR PEER REVIEW 15 of 32 employed to determine the location of subsurface cavities [62] and the preferential groundwater flow pathways in sinkholes [59], and to investigate the vertical infiltration of water from agricultural ditches into sinkholes [63]. Furthermore, Ikard et al. [64] applied the fusion of SP with ERT to identify the occurrence of preferential groundwater seepage in karst terrain. In the current study, we performed SP signal measurements at the ground surface using non-polarizing electrodes connected to a multimeter in voltage mode (with impedance at 10 MΩ) via an electric cable. Equation (11) represents the relationship between the electrical current density at point M and the measured SP signal at non-polarizing electrode P. This electrical potential signal φ (mV) can be computed as follows: 1 φ(P) = ∫ K(P, M)jS(M)dV (11) Ω where jS is the source current density (in both saturated and unsaturated conditions), K(P,M) is the kernel connecting the φ(P) signal measured at a set of non-polarizing electrodes P (with respect to a reference electrode) and the source of current at point M in the conducting ground. It is important to note that this kernel depends on a number of factors including the number of measurement stations at the ground surface (Figure 9) as well as the distribution of resistivity [55]. The total current density is expressed as follows (Equation (12)) [65,66]: θs j = −σ(θ)∇φ + Qv u (12) θ where j is the electrical current density (A m2), σ is the electric conductivity of the porous material, φ is the SP, Qv is the excess charge of the diffuse layer of the pore water per unit pore volume and Resources 2020, 9, 51 16 of 32 depends on the permeability of the porous material, 휃 is the water content, 휃s is the porosity, and u is the Darcy velocity.

Figure 9. An example of SP measurement station at the ground surface on which a pickaxe (A) is used to dig aa shallowshallow holehole withwith 3030 cmcm ofof depthdepth where where one one non-polarizing non-polarizing electrode electrode (D) (D) connected connected to to a multimetera multimeter (C) (C) and and another another ﬁxed fixed non-polarizing non-polarizing electrode electrode via via an an electrical electrical cable cable (B) (B) is introducedis introduced to taketo take SP SP signal. signal.

3.3. Geophysical Data Processing and Interpretation 3.3.1. ERT Data Processing 3.3.1. ERT Data Processing The apparent resistivity values obtained in the field represent the resistivity of homogeneous The apparent resistivity values obtained in the field represent the resistivity of homogeneous ground, with constant resistance values associated with the same electrode arrangement [55]. In order ground, with constant resistance values associated with the same electrode arrangement [55]. In to obtain the true resistivity of the soil and geologic formations, computer inversion techniques are generally applied to determine the true subsurface resistivity from the apparent resistivity values [67]. These inversion techniques are made up of computer based algorithms. In particular, for ERT surveys, RES2DINV software, developed by Dr. M.H. Loke, is employed for the processing of 2D data. This program applies the conventional smoothness-constrained least-squares algorithm [68] as the default inversion method. More specifically, a 2D model of the subsurface is determined by first creating a large number of blocks and subsequently applying the least-squares inversion scheme to determine the appropriate resistivity for each block. By doing so, the calculated apparent resistivity values agree with the measured apparent resistivity values determined from the field surveying [61]. During the processing of the data, the difference between the calculated and measured apparent resistivity values is computed using, for example, the root-mean-squared (RMS) error (Equation (13)). This difference is reduced by adjusting the resistivity of the model blocks over a number of iterations. The optimum 2D model is chosen at the iteration where the change in the root mean square error (RMSE) is minimal. v t n 1 X RMSE = (ρca ρma)2 (13) n − i=1 where ρca is the calculated apparent resistivity (using RES2DINV), ρma is the measured apparent resistivity (on the field), and n is the number of points surveyed. In the current study, ERT profiles acquired around four sinkholes were processed using the RES2DINV (version 4.8.10. 64-bit, GEOTOMO SOFTWARE, Gelugor, Penang, Malaysia) inversion software to map the electrical resistivity in two dimensions (vertically and horizontally). This updated Resources 2020, 9, 51 17 of 32

version of RES2DINV softwares, dating 2 May 2018, is capable of processing 60,000 acquired data points for a maximum number of 6000 electrodes and 40 maximum model layers.

3.3.2. From SP Data Acquisition to SP Signal Maps The measured SP signals are electrical potentials primarily attributed to the circulation of water in the saturated zone [16] or to the percolation of water seeping into the vadose zone. We used simple equipment consisting of two non-polarizing electrodes, a very high sensitivity multimeter and a coil of electric cable. The measurements values were then written down in a table on a ﬁeld notebook. We chose to make an acquisition of electrical potential via a mesh of 2 m inter-measurements with a ﬁxed and mobile electrode, allowing us to subsequently determine electrical potential variation maps around the sinkholes. In laboratory, SP data was saved in Excel (version 2010, Microsoft Corporation, Redmond, WA, USA) spreadsheets and then processed using the Surfer (version 13, Golden Software LLC Company, Golden, CO, USA) to output the electrical potential maps.

3.3.3. Boreholes Data and Geophysical Data Interpretation of the Second Site The Figure 10 shows the location of geophysical measurements (ERT and SP) conducted around the sinkholes developed on the second site with geological formations dominated by basalts, dolomitic Resourceslimestone, 2020, 9, x and FOR dolomites. PEER REVIEW 17 of 32

FigureFigure 10. Position 10. Position map map indicating indicating the the location location ( (BB)) of thethe ERTERT profiles, profiles, SP SP and and the the sinkholes sinkholes of the of the secondsecond site site(A). ( A). As previously mentioned, the SP signal can originate from different sources. Thus, in order toAs determine previously the mentioned, origin of the the measured SP signal SP signal,can originate and to subsequently from different facilitate sources. its interpretation,Thus, in order to determinewe combined the origin the SPof resultsthe measured with those SP derived signal, fromand theto subsequently ERT method. Thisfacilitate allows its for interpretation, an improved we combinedunderstanding the SP results of the underground with those derived polarization from mechanism the ERT method. that is responsible This allows for for the an electrical improved understandingpotentials measured of the atunderground the surface. We polarization assumed that mechanism the dominant that process is contributingresponsible to for the the SP signal electrical potentialswas the measured electro filtration at the electrokinetic surface. We phenomena assumed generatedthat the dominant by the streaming process potential contributing gradient to from the SP signalunderground was the electro water flows filtration through electrokinetic pores, fractures, phenomena and cavities generated in this karst by environment. the streaming potential gradient The from 2D model underground of subsurface water true flowsresistivity through represents pores, the resistivity fractures, values and of geologiccavities formations, in this karst soils, and other materials. An accurate interpretation of the model must be based on the electrical environment. properties of the present materials in order to translate the resistivity model into a geologic model. The 2D model of subsurface true resistivity represents the resistivity values of geologic formations, soils, and other materials. An accurate interpretation of the model must be based on the electrical properties of the present materials in order to translate the resistivity model into a geologic model. To do this, the subsurface materials and geologic formations of the surveyed area must be identified. This information can be obtained by conducting a number of borehole loggings and by determining the subsurface true geologic composition. During the drilling of this borehole, different subsurface geology information (such as the lithology and the thickness of geological formation layers, their structural and composition as well as the water table level is saved in the form of stratigratic logs. The Figures 11–13 represent the logs of boreholes F8, F12 and F48 located near the studied sinkholes (Figure 10). In particular, they contain descriptions of the stratigraphic composition of the surveyed area dominated by basalts, clays, sandy dolomites, fractured limestones, and dolomite. At the drilled point, the presence of cavities was identified by the free fall of the hammer deep in the borehole and was reported on borehole log as shown on the Figures 11–13.

Resources 2020, 9, 51 18 of 32

To do this, the subsurface materials and geologic formations of the surveyed area must be identified. This information can be obtained by conducting a number of borehole loggings and by determining the subsurface true geologic composition. During the drilling of this borehole, different subsurface geology information (such as the lithology and the thickness of geological formation layers, their structural and composition as well as the water table level is saved in the form of stratigratic logs. The Figures 11–13 represent the logs of boreholes F8, F12 and F48 located near the studied sinkholes (Figure 10). In particular, they contain descriptions of the stratigraphic composition of the surveyed area dominated by basalts, clays, sandy dolomites, fractured limestones, and dolomite. At the drilled point, the presence of cavities was identified by the free fall of the hammer deep in the borehole and was reported on borehole log as shown on the FiguresResources 11– 201320. , 9, x FOR PEER REVIEW 18 of 32

FigureFigure 11. Borehole11. Borehole log log F8 fromF8 from 1982 1982 with with ﬁnal final depth depth of of 100 100 m m where where water water table table was was identiﬁed identified at at 74.9 m of74.9 depth. m of depth.

Resources 2020, 9, 51 19 of 32 Resources 2020, 9, x FOR PEER REVIEW 19 of 32

FigureFigure 12. 12.Borehole Borehole log log F12 F12 from from 1986 1986 with with ﬁnal final depth depth of 105of 105 m wherem where water water table table was was identiﬁed identified at 5.6 at m5.6 of m depth. of depth.

Following the determination of subsurface geologic formations of the study area, the locations of the formations must be identified on the 2D true resistivity model. Volcanic rocks such as basalts have a resistivity of 103–1.3 107 (dry) Ωm, sedimentary rocks such as sandstone, limestone and dolomite × have values of 1–6.4 108 Ωm, 50–107 Ωm and 3.5 102–5 103 Ωm respectively, while soil (clay and × × × alluvium) values are measured as 1–100 Ωm and 10–800 Ωm respectively [69]. The resistivity value is a function of various factors, such as the degree of porosity, fracturing and the type and the quantity of material filled in these voids. The combination of this information allows for the identification of

Resources 2020, 9, 51 20 of 32

Resources structures2020, 9, x FOR and typesPEER ofREVIEW geological formations at the subsurface as well as at the horizontal extent of the 20 of 32 studied sinkholes.

Figure 13. Borehole log from 1998 with final depth of 238 m where water table was identified at 136 m Figure of13. depth. Borehole log from 1998 with final depth of 238 m where water table was identified at 136 m of4. Resultsdepth. and Discussion The measurements from the remote sensing and applied geophysical techniques were analyzed Following the determination of subsurface geologic formations of the study area, the locations with the ultimate aim of identifying and protecting the sinkholes in the study area. The study was of the formationsconducted in must order be to contributeidentified to on the the big 2D effort true launched resistivity in the model. region of Volcanic Fez/Meknes rocks for climatesuch as basalts have a change resistivity adaptation of 103 with–1.3 a × focus 107 (dry) on groundwater Ωm, sedimentary resource protection. rocks such First, as karst sandstone, depressions limestone and and dolomiteagricultural have values areas of were 1– identified6.4 × 108 usingΩm, Sentinel-2A,50–107 Ωm ASTER-GDEM and 3.5 × 10 and2–5 × Google 103 Ωm Earth respectively, images. Based while soil (clay andon the alluvium) surface reflectance values and are morphology measured contained as 1–100 in the Ωm imagery, and the 10 interpretation–800 Ωm of respectively visual features [69]. The of the 3-band false color Sentinel-2A image and TIN topographic model conducted in conjunction resistivity value is a function of various factors, such as the degree of porosity, fracturing and the type and the quantity of material filled in these voids. The combination of this information allows for the identification of structures and types of geological formations at the subsurface as well as at the horizontal extent of the studied sinkholes.

4. Results and Discussion The measurements from the remote sensing and applied geophysical techniques were analyzed with the ultimate aim of identifying and protecting the sinkholes in the study area. The study was conducted in order to contribute to the big effort launched in the region of Fez/Meknes for climate change adaptation with a focus on groundwater resource protection. First, karst depressions and agricultural areas were identified using Sentinel-2A, ASTER-GDEM and Google Earth images. Based on the surface reflectance and morphology contained in the imagery, the interpretation of visual

Resources 2020, 9, x FOR PEER REVIEW 21 of 32 features of the 3-band false color Sentinel-2A image and TIN topographic model conducted in conjunction with geologic information and fieldwork measurements allowed us to identify sinkholes and karst cavities at the two study sites. Remote sensing measurements were used to identify the location and horizontal extent of the sinkholes, while geophysical surveys were conductedResources in order2020, 9, 51 to determine their subsurface location and extent. The methods 21 were of 32 able to delineate the soil and geological materials of the study area. Furthermore, geophysical data was acquiredwith at the geologic second information site, whereby and fieldwork we identified measurements collapse allowed and us solution to identify sinkholes sinkholes andlocated karst close to agriculturalcavities fields. at the two study sites. Remote sensing measurements were used to identify the location and horizontal extent of the sinkholes, while geophysical surveys were conducted in order to determine 4.1. Sinkholestheir subsurfaceIdentified Remotely location and extent. The methods were able to delineate the soil and geological materials of the study area. Furthermore, geophysical data was acquired at the second site, whereby Thewe processed identified collapsesatellite and imagery solution reveals sinkholes that located sinkholes close to agriculturalare located fields. in areas occupied by basalts and come into contact with carbonate rocks. In particular, these areas possess fertile and rich soil 4.1. Sinkholes Identified Remotely suitable for agricultural activity. In addition, the presence of fractured limestone and dolomite allow for the waterThe infiltration processed satelliteand retention, imagery reveals thus thatmaking sinkholes the arearea located more in areasattractiv occupiede as byfarmland. basalts and Figure 14 come into contact with carbonate rocks. In particular, these areas possess fertile and rich soil suitable for presents the TIN elevation model computed from the ASTER-GDEM on which we digitalized the agricultural activity. In addition, the presence of fractured limestone and dolomite allow for the water identifiedinfiltration sinkholes and retention,and other thus karst making cavities. the area more Variations attractive in as the farmland. elevation Figure and 14 presents the interpolation the TIN of triangleselevation representing model computednearest point from the elev ASTER-GDEMations indicate on which that wethese digitalized karst landforms the identified are sinkholes developed on large to smalland other depressions. karst cavities. These Variations depressions in the elevation are expressed and the interpolation by subcircular of triangles surface representing features with low elevationnearest located point in elevations areas occupied indicate that by these basalts karst and landforms with aretheir developed contact on with large tocarbonate small depressions. rocks. On-site These depressions are expressed by subcircular surface features with low elevation located in areas observations confirmed the location and characteristics of different karst landforms that were occupied by basalts and with their contact with carbonate rocks. On-site observations confirmed identifiedthe remotely location and (Table characteristics 3). Different of diff erenttypes karst of sinkholes, landforms that such were as identifiedcollapse, remotely solution, (Table and3). dropout doline, wereDifferent observed types of (Figure sinkholes, 15). such as collapse, solution, and dropout doline, were observed (Figure 15).

Figure 14. TIN topography of studied area highlighting the variation in topography and the locations Figure 14. TIN topography of studied area highlighting the variation in topography and the locations of the studies sinkholes. of the studies sinkholes.

Resources 2020, 9, 51 22 of 32 Resources 2020, 9, x FOR PEER REVIEW 22 of 32

Figure 15. Photographs of sinkholes identified during fieldwork observations. (1) Funnel shaped Figurecollapse 15. sinkhole Photographs with an of approximate sinkholes identified depth of 35 during m, (2) solutionfieldwork sinkhole observations. with an ( approximate1) Funnel shaped depth collapseof 50 m, (sinkhole3) buried with sinkholes an approximate filled with basalts, depth of and 35 ( 4 m,) dropout (2) solution doline. sinkhole with an approximate depth of 50 m, (3) buried sinkholes filled with basalts, and (4) dropout doline. Table3 reports the key characteristics of karst landforms identified via the satellite imagery and confirmedTable 3 using reports on-site the key observations. characteristics The distanceof karst landforms from farmland identified value via calculated the satellite from imagery the sinkhole and confirmedor cavity location using representson-site obse thervations. distance The a polluting distance substance from farmland used in agricultural value calculated activities from would the sinkholehave travel or in cavity order location to reach the represents fractured the carbonate distance rocks a polluting to eventually substance endup used in the in groundwater agricultural activitiesaquifer. Knowledge would have of travel the surface in order morphology to reach the and fractured outcrop geologycarbonate can rocks help to to eventually determine theend type up in of theprotection groundwater perimeter aquifer. structure Knowledge required of to the protect surface these morphology sinkholes. and outcrop geology can help to determine the type of protection perimeter structure required to protect these sinkholes. Table 3. Karst landforms characteristics identified in the Causse of El Hajeb. Table 3. Karst landforms characteristics identified in the Causse of El Hajeb. Distance from Karst Landforms Outcrop Geological Formation Surface Morphology Karst DistanceFarmland from Outcrop Geological Formation Surface Morphology Landforms1 Mixt of red clay and carbonate rocks Farmland3 m circular 12 Mixt of red clay Carbonate and carbonate rocks rocks 103 m m circular rhombus 3 Basalts and carbonate rocks 0 m circular 24 Carbonate Red clay and rocks basalts 10 0 mm rhombus circular 35 Basalts and Carbonate carbonate rocks rocks 2000 m m circular circular 46 BasaltsRed clay and and carbonate basalts rocks 1700 m m circularcircular depression 57 Carbonate Carbonate rocks rocks 200 40 mm circular circular 68 Basalts and Carbonate carbonate rocks rocks 170 50 mm circular circulardepression 9 Basalts and carbonate rocks 30 m variable 7 Carbonate rocks 40 m circular 8 Carbonate rocks 50 m circular 4.2. ERT 9 Basalts and carbonate rocks 30 m variable The location of the karst cavities was observed in areas with very low electrical resistivity values 4.2.compared ERT to the whole resistivity model section values. The 2D Resistivity model was chosen for the 7thThe iteration location with of the RMSE karst (absolute cavities errors) was observed of 8.9, 9.4, in 11.3 areas and with 14 for very diff erent low electrical electrical resistivity resistivity valuesprofiles compared conducted to during the whole of the resistivity field surveys. model section values. The 2D Resistivity model was chosen for theFigures 7th iteration 16–19 represent with RMSE the 2D (absolute resistivity errors) model sections of 8.9, 9.4, obtained 11.3 from and 14 the for ERT different profiles conducted electrical resistivityaround sinkholes profiles andconducted cavities during identified of the at field the second surveys. site (Figure 14). The results of the ERT survey obtainedFigures following 16–19 represent seven iterations the 2D reveals resistivity the electrical model sections resistivity obtained of subsurface from the materials, ERT profiles with conductedhigher values around representing sinkholes resistive and cavities geological identified formations at the whereassecond site lower (Figure values 14). represent The results conductive of the ERT survey obtained following seven iterations reveals the electrical resistivity of subsurface

Resources 2020, 9, x FOR PEER REVIEW 23 of 32 Resources 2020, 9, x FOR PEER REVIEW 23 of 32 Resourcesmaterials,2020 with, 9, 51 higher values representing resistive geological formations whereas lower values23 of 32 materials,represent conductive with higher materials. values representing Based on resistivethe borehole geological logging formations information, whereas we werelower ablevalues to representdetermine conductivethe subsurface materials. geological Based formations on the belonging borehole loggingto these materials. information, The weresistivity were able model to determinematerials.allowed us the Basedto subsurfaceidentify on the soil borehole geological materials logging formationsand red information, clays belonging on the we tosurface were these able andmaterials. to inside determine Thethe resistivitysinkhole the subsurface cavities model allowedgeologicalwith values us formations to ranging identify from belongingsoil materials 3 to 60to theseΩm. and materials.Thesered clays sinkholes Theon the resistivity aresurface located modeland in inside allowedhigh the resistive ussinkhole to identify host cavities rocks, soil withmaterialsnamely values dolomitic and ranging red clayslimestone, from on the 3 fractured to surface 60 Ωm. andlimestone, These inside sinkholes theand sinkhole dolomite, are cavities located with resistivity with in high values resistivevalues ranging ranging host from rocks, from 3 to namely60165Ω tom. 4000 dolomitic These Ωm. sinkholes limestone, are located fractured in high limestone, resistive and host dolomite, rocks, namely with resistivity dolomitic values limestone, ranging fractured from 165limestone, toThe 4000 resistivity andΩm. dolomite, model with sections resistivity for the values ERT rangingprofiles fromderived 165 from to 4000 surveyingΩm. conducted around the FunnelThe resistivity shaped modelcollapsemodel sectionssections sinkhole for for theand the ERT theERT profilessolution profiles derived sinkhole, derived from from(Figure surveying surveying 15), exhibit conducted conducted low aroundresistivity around the theFunnelvalues, Funnel shapedindicating shaped collapse that collapse the sinkholeses sinkhole cavities and theand, developed solution the solution sinkhole, in fractured sinkhole, (Figure dolomitic (Figure 15), exhibit 1limestone5), exhibit low resistivity, arelow filledresistivity values, with values,indicatingconductive indicating that materials theses that. cavities,These theses resistivity developedcavities, lowdeveloped in values fractured, shownin dolomiticfractured on the limestone,dolomitic Figure 16 limestone, are are filled attributed with, are conductivefilledto the with clay conductivematerials.filled in the These materialsFunnel resistivity shaped. These sinkhole. low resistivity values, Furthermore, low shown values on the, theshown Figure sinkhole on 16 the, was are Figure attributedobserved 16, are to to attributed become the clay deeper, filled to the in clayand the fillFunnelits edcontinuity in shaped the Funnel was sinkhole. identified shaped Furthermore, sinkhole. in the northwest Furthermore, the sinkhole of Figure the was sinkhole observed17. was tobecome observed deeper, to become and its deeper, continuity and wasits continuity identified was in the identified northwest in the ofFigure northwest 17. of Figure 17.

Figure 16. Resistivity model section for ERT profile P1, representing the variation in electrical Figure 16. Resistivity model section for ERT profile P1, representing the variation in electrical resistivity Figureresistivity 16. values Resistivity obtained model following section the for inversion ERT profile of apparent P1, representing resistivity thevalues variation for seven in iterations electrical values obtained following the inversion of apparent resistivity values for seven iterations with absolute resistivitywith absolute values error obtained of 14.0. following This profile the was inversion acquired of apparentin the southeast resistivity of the values first forsinkhole seven identifiediterations error of 14.0. This profile was acquired in the southeast of the first sinkhole identified at 64 m from withat 64 absolute m from error the firstof 14.0. electrode. This profile The was continuity acquired of in the the vertical southeast anomaly of the firs demonstratest sinkhole identified that the the first electrode. The continuity of the vertical anomaly demonstrates that the development of atdevelopment 64 m from of the the firstsinkhole electrode. in carbonate The continuity rocks, reaching of the a vertical 30 m depth anomaly from demonstrates the surface where that the the sinkhole in carbonate rocks, reaching a 30 m depth from the surface where the cavity is visible developmentcavity is visible of (Figurethe sinkhole 15). in carbonate rocks, reaching a 30 m depth from the surface where the (Figure 15). cavity is visible (Figure 15).

Figure 17. Resistivity model section for ERT profile P2, representing the variation of electrical resistivity valuesFigure obtained 17. Resistivity following model the inversion section forofapparent ERT profile resistivity P2, representing values for seven the iterations variation with of electrical absolute Figureerrorresistivity of 179.4. .values Resistivity This profileobtained model was following acquired section the in for the inversion ERT northwest profile of apparent of P2, the representing first resistivity sinkhole thevaluesidentified variation for atseven 70 of m iterations electrical from the resistivityfirstwith electrode.absolute values error Vertical obtained of 9.4. anomaly This following profile analysis the was demonstratesinversion acquired of in apparent the the no developmentrthwest resistivity of the of values first the sinkholesinkhole for seven inidentified carbonateiterations at withrocks,70 m absolute reachingfrom the error deep first of into electrode.9.4. fracturedThis profile Vertical dolomitic was anomalyacquired limestone. analysisin the no rthwest demonstrates of the first the sinkhole development identified of the at 70sinkhole m from in carbonate the first rocks, electrode. reaching Vertical deep anomaly into fractured analysis dolomitic demonstrates limestone. the development of the sinkholeThe resistivity in carbonate model rocks, section reaching of the ERTdeep profilesinto fractured obtained dolomitic for the limestone. survey conducted in the northwest of sinkholeThe resistivity (2) indicate model a number section of of sinkholes the ERT notprofiles observed obtained at the for surface. the survey These conducted sinkholes exhibit in the lownorthwest resistivityThe resistivity of sinkhole values, model (2) indicating indicate section a that number of theythe ERT areof sinkholes filledprofiles with not obtained conductive observed for at the materials, the survey surface. well conducted These developed sinkholes in the in northwestdepthexhibit and low of are sinkhole resistivity hosted by(2) valuesfractured indicate, indicating a dolomite number andof that sinkholes limestone.they are not The filledobserved results with atof conductivethe the surface. ERT survey These materials (Figures sinkholes, well 18 exhibitanddevelop 19) presented low in resistivity depth resistivity and values are low hosted values, indicating by for fractured a sinkhole that they dolomite well-developed are filled and limestone. with bellow conductive fractured The results materials limestone. of the, well ERT The developresistivitysurvey (Figuresed models in depth 18 were and and derived 19)are presenthosted using cellsby resistivity fractured with a widthlow dolomite values of 2.0 andm for (Figure a limestone. sinkhole 18) and Thewell 5.0 - resultsdeveloped m (Figure of the19 bellow). ERT The surveysinkholesfractured (Figures limestone. are denoted 18 andThe by resistivity 19)the low present resistivity models resistivity were anomaly derived low at values the using 26 for m cells anda sinkholewith 64 m a horizontalwidth well -ofdeveloped 2.0 distance m (Figure bellow marks 18) fracturedfromand 5.0 NE m tolimestone. (Figure the SW 19). for The The Figure resistivity sinkholes 18. Other models are denoted underground were derivedby the cavities low using resistivity cells can bewith identifiedanomaly a width at atof the 1852.0 26 m m (Figure and and 232 64 18) m andhorizontal 5.0 m (Figure distance 19). marks The fromsinkholes NE to are the denoted SW as seen by the on thelow Figureresistivity 19. Theseanomaly sections at the demonstrate 26 m and 64 the m

Resources 2020, 9, x FOR PEER REVIEW 24 of 32

Resources 2020, 9, x FOR PEER REVIEW 24 of 32 Resourceshorizontal2020 , distance9, 51 marks from NE to the SW for Figure 18. Other underground cavities can24 of be 32 identified at 185 m and 232 m horizontal distance marks from NE to the SW as seen on the Figure 19. horizontal distance marks from NE to the SW for Figure 18. Other underground cavities can be These sections demonstrate the connectivity of the two sinkholes, with greater depths from the SE to identified at 185 m and 232 m horizontal distance marks from NE to the SW as seen on the Figure 19. connectivitythe NW. In addition, of the two the sinkholes, fractured with limestone greater in depths Figure from 18 theis affected SE to the by NW. a sinkhole In addition, of approximately the fractured These sections demonstrate the connectivity of the two sinkholes, with greater depths from the SE to limestone30 m in size in that Figure is accessible 18 is aﬀected at the by surface. a sinkhole This of shows approximately the complexity 30 m inof sizethis thatkarst is system. accessible at the the NW. In addition, the fractured limestone in Figure 18 is affected by a sinkhole of approximately surface. This shows the complexity of this karst system. 30 m in size that is accessible at the surface. This shows the complexity of this karst system.

Figure 18. Resistivity model section for ERT profile P3 representing the variation of electrical Figureresistivity 18. valuesResistivity obtained model following section for the ERT invers profileion P3of representingapparent resistivity the variation values of for electrical seven resistivityiterations Figure 18. Resistivity model section for ERT profile P3 representing the variation of electrical valueswith absolute obtained error following of 11.3. the inversion This profile of apparent was acquired resistivity in the values northwest for seven of iterations the second with sinkhole, absolute resistivity values obtained following the inversion of apparent resistivity values for seven iterations errorapproximately of 11.3. This 30 profilem in size was and acquired identified in the at northwest70 m from ofthe the first second electrode. sinkhole, Vertical approximately anomaly analysis 30 m in with absolute error of 11.3. This profile was acquired in the northwest of the second sinkhole, sizeshows and that identified the sinkhole at 70 mis fromdeveloped the first in electrode.carbonate Verticalrocks and anomaly reaches analysis deep into shows fractured that the dolomitic sinkhole approximately 30 m in size and identified at 70 m from the first electrode. Vertical anomaly analysis islimestone. developed In inaddition, carbonate a second rocks sinkhole and reaches was deep identified into fractured 32 m from dolomitic the first electrode. limestone. In addition, a shsecondows that sinkhole the sinkhole was identified is developed 32 m from in carbonate the first electrode. rocks and reaches deep into fractured dolomitic limestone. In addition, a second sinkhole was identified 32 m from the first electrode.

Figure 19. Resistivity model section for ERT profile P4 representing the variation of electrical resistivity Figure 19. Resistivity model section for ERT profile P4 representing the variation of electrical values obtained following the inversion of the apparent resistivity values for seven iterations with resistivity values obtained following the inversion of the apparent resistivity values for seven Figureabsolute 19. error Resistivity of 8.9. This model profile section was acquired for ERT in the profile northwest P4 representing of the second the sinkhole variation identified, of electrical at 185 iterations with absolute error of 8.9. This profile was acquired in the northwest of the second sinkhole resistivitym from the values first electrode. obtained Vertical following anomaly the analysis inversion shows of the that apparent the sinkhole resistivity is developed values in for carbonate seven identified, at 185 m from the first electrode. Vertical anomaly analysis shows that the sinkhole is iterationsrocks and with reaches absolute deep intoerror fractured of 8.9. This limestone. profile was In addition, acquired twoin the small northwest cavities of were the second identified sinkhole at 190 developed in carbonate rocks and reaches deep into fractured limestone. In addition, two small identified,m and 240 m at from 185 m the from first t electrode.he first electrode. Vertical anomaly analysis shows that the sinkhole is cavities were identified at 190 m and 240 m from the first electrode. developed in carbonate rocks and reaches deep into fractured limestone. In addition, two small 4.3. SP cavities were identified at 190 m and 240 m from the first electrode. 4.3. SP The analysis of the ERT measurements allows us to determine the vertical extent of subsurface 4.3.sinkholes SPThe analy andsis buried of the cavities, ERT measurements while the results allows of us the to SP determine measurements the vertical provide extent their of subsurface horizontalsinkholes and extant. buried SP signal cavities, measurements while the results conducted of the around SP measurements identified sinkholes provide were their processed subsurface and The analysis of the ERT measurements allows us to determine the vertical extent of subsurface presentedhorizontal onextant. the maps SP signal of electrical measurements potential conducted (Figures 20 around and 21 ).identified Two maps sink wereholes produced were processed from the sinkholes and buried cavities, while the results of the SP measurements provide their subsurface data,and presented with areas on of 756the mmaps2 and of 2760 electrical m2. The potential results exhibit (Figures several 20 and anomalies, 21). Two with maps very were high produced SP values horizontal extant. SP signal measurements conducted around identified sinkholes were processed linkedfrom the to thedata, level with of areas the sinkholes of 756 m (more2 and than2760 10 m mV).2. The These results high exhibit values several are a resultanomalies, of materials with very that and presented on the maps of electrical potential (Figures 20 and 21). Two maps were produced fillhigh the SP sinkhole, values linked which to are the essentially level of the composed sinkholes of (more clays andthan soil 10 erosionmV). These materials. high values The continuity are a result of from the data, with areas of 756 m2 and 2760 m2. The results exhibit several anomalies, with very theseof materials anomalies that revealsfill the sinkhole, the subsurface which extent are essentially of the sinkholes composed from of the clays SE and to the soil NW. erosion materials. high SP values linked to the level of the sinkholes (more than 10 mV). These high values are a result The continuity of these anomalies reveals the subsurface extent of the sinkholes from the SE to the of materials that fill the sinkhole, which are essentially composed of clays and soil erosion materials. NW. The continuity of these anomalies reveals the subsurface extent of the sinkholes from the SE to the NW.

Resources 2020, 9, 51 25 of 32 Resources 2020, 9, x FOR PEER REVIEW 25 of 32

Figure 20. Map of SP signal ( A) measured around the funnelfunnel shaped collapse sinkhole ( B) with high values (10 to 20 mV) indicating the horizontal extent ofof thethe sinkholesinkhole whereaswhereas thethe lowlow valuesvalues (6(6 toto −1212 − mV) indicate the extent of the hosting rock. The black circle indicates the position of the sinkhole as observed to the ground surface.

Figure 21. Map of SP signal (A) measured around the solution sinkhole (B) with high values (10 to Figure 21. Map of SP signal (A) measured around the solution sinkhole (B) with high values (10 to 20 20 mV) indicating the horizontal extent of the sinkhole while the low values (6 to 7 mV) indicate mV) indicating the horizontal extent of the sinkhole while the low values (6 to −7− mV) indicate distribution of the hosting rock. The black circle indicates the accessible part of the sinkhole from the distribution of the hosting rock. The black circle indicates the accessible part of the sinkhole from the ground surface. ground surface.

4.4. Protection Zoning of Sinkholes In natural environments, the solubility of calcite and dolomite minerals depends on pH, which is largely controlled by carbon dioxide (CO2) partial pressure [70]. In agricultural areas, the is largely controlled by carbon dioxide (CO2) partial pressure [70]. In agricultural areas, the

Resources 2020, 9, 51 26 of 32

4.4. Protection Zoning of Sinkholes In natural environments, the solubility of calcite and dolomite minerals depends on pH, which is largely controlled by carbon dioxide (CO2) partial pressure [70]. In agricultural areas, the chemicals and fertilizers used for the protection and growth of crops change the surface runoff pH, which in turns influencesResources the 2020 solubility, 9, x FOR PEER of REVIEW these minerals. Cavities formed by the gradual dissolution of26 carbonate of 32 rocks may lead to the development of ground subsidence or sinkhole formations. While infrastructure chemicals and fertilizers used for the protection and growth of crops change the surface runoff pH, damagewhich or in the turns loss influence of humans the live solubility can occur of these during minerals. the development Cavities formed of by sinkholes, the gradual hydrogeological dissolution hazardsof carbonate are more rocks common may lead in karst to the environments. development of ground subsidence or sinkhole formations. While Theinfrastructure key factors damage controlling or the karstification loss of human processes live can occur in the during TMA the are developmen discussedt in of detail sinkholes,in [9 ,71] and includehydrogeological the following: hazards are lithology, more common geomorphological in karst environments settings. and adjacent aquifers, water table fluctuationsThe key factors (variations controlling in the karstification piezometric processes level), in faultsthe TMA and are structures discussed in aff detailecting in carbonate[9,71] rocksand (tectono-structures), include the following: Mediterranean lithology, geomorphological climate and precipitation settings and adjacent regimes, aq surfaceuifers, water runo tableff and its physicochemicalfluctuations (variationsproperties, in and the regional piezometric land level), cover faults and land and use. structures affecting carbonate rocks (tectono-structures), Mediterranean climate and precipitation regimes, surface runoff and its Amraoui et al. [9] and Howell et al. [10] investigated the hydrochemistry of the principle water physicochemical properties, and regional land cover and land use. springs located in the piedmont of TMA and identified the contribution of sinkholes (from the surface Amraoui et al. [9] and Howell et al. [10] investigated the hydrochemistry of the principle water as wellsprings as from located internal in the gradual piedmont erosion) of TMA to the and turbidity identified of the these contribution springs. of sinkholes (from the Internalsurface as erosion well as from and deformationalinternal gradual processeserosion) to arethe turbidity generally of controlledthese springs. by different factors [72] includingInternal water erosion table fluctuations and deformational (variations processes in theare generally piezometric controlled level), by hydraulic different factors gradient [72] and mechanicalincluding properties water table of carbonate fluctuations rocks (variations (which may in the control piezometric dissolution level), processes), hydraulic gradient subsurface andkarst and voidmechanical networks proper generatedties of carbonate by dissolution, rocks (which and surface may control run-o ff dissolutionand its physicochemical processes), subsurface properties flowingkarst in andsinkholes void networks and cavities. generated by dissolution, and surface run-off and its physicochemical Figureproperties 22 depicts flowing thein sinkholes derived and protection cavities zones to weaken the key effects controlling karstification Figure 22 depicts the derived protection zones to weaken the key effects controlling processes in the study area, and for the eventual reduction of the impact of sinkholes on the turbidity karstification processes in the study area, and for the eventual reduction of the impact of sinkholes observed on the main water sources in the Fez/Meknes region. The zones indicate areas which require on the turbidity observed on the main water sources in the Fez/Meknes region. The zones indicate protection,areas whic withh anthropicrequire protection, activities with prohibited anthropic withinactivities the prohibited zones. In within particular, the zones. they In indicate particular, highly vulnerablethey indicate areas surrounding highly vulnerable the sinkholes, areas surrounding and their the protection sinkholes, will and minimize their protection the effects will ofminimize agricultural activitytheand effects land of agricultural reclaiming activity for agricultural and land reclaiming needs conducted for agricultural in the needs Causse conducted of El Hajeb. in the Causse of El Hajeb.

FigureFigure 22. Map 22. Map indicating indicating the the location location of of geophysical geophysical measurements (SP (SP and and ERT) ERT) and and the sinkholes the sinkholes studiedstudied (B) as (B well) as well as the as the zoom zoom in in indicating indicating the the locationlocation of of landcover landcover (farmland (farmland and and non non-agriculture-agriculture carbonatecarbonate rocks) rocks) and and the the proposed proposed protection protection zoneszones (A).). Note Note that that these these sinkholes sinkholes are arelocated located in the in the contactcontact of intensive of intensive agricultural agricultural area area and and carbonate carbonate rocks.rocks. The The protection protection of ofthese these sinkholes sinkholes requires requires the implementationthe implementation of protection of protection zone zone covering covering their their wholewhole horizontal horizontal extent. extent. The results obtained in the current study highlight the advantage of combining multiple methods for natural hazards studies. In particular, remote sensing imagery is able to provide surface

Resources 2020, 9, 51 27 of 32

The results obtained in the current study highlight the advantage of combining multiple methods for natural hazards studies. In particular, remote sensing imagery is able to provide surface land cover information with the exact location of diﬀerent karst landforms over large areas, while ERT measurements can supply us with subsurface vertical information of sinkholes according to variations in depth. Furthermore, SP measurements complement the ERT results by providing the horizontal extent of subsurface cavities. This information can be applied to determine protection zones for the identiﬁed sinkholes.

4.5. Assessment of Measurements and Sinkhole Delineation Precisions By taking 22.2 Ωm as the model resistivity value at the limit of sinkholes and their hosting rocks, we can determine the error propagation of the resistivity chosen for sinkhole delineation on model section of each ERT proﬁles. The relationship between the obtained model resistivity and the measured resistivity can be expressed as follow (Equation (14)):

ρ model = (ρ measured absolute error)(Ωm) (14) ± While the absolute errors indicate the precision of equipment used to take the measurement, the accuracy of the results can be evaluated by examining the relative uncertainty. The relative uncertainty is the ratio between absolute error and the value of modeled resistivity multiple by 100 (Equation (15)). The large percentage of relative uncertainty means less precision of the measured resistivity. This can be explored to evaluate the accuracy of the results for each ERT proﬁle.

Absolute error 100 Relative uncertainty = × (%) (15) ρ model

The Table4 shows uncertainty parameters examined to determine the precision of the resistivity model section for ERT profiles and eventually its usage for the delineation of sinkhole. We noticed that for ERT profiles (P1 and P3) acquired too close to the sinkholes, absolute errors and relative uncertainty are very high. This can be explained by electrical property of materials filled in the sinkhole cavities where their variations can be affecting the precision of equipment. On the Figure 23 we can observe the resistivity values which can be mistaken during the delineation of the sinkhole for each profile. We noticed that the delineation of the sinkhole for ERT profile (P1) requires particular attention due to the highly variableResources 20 resistivity20, 9, x FOR PEER between REVIEW the sinkhole and the hosting rocks. 28 of 32

Figure 23.FigureError 23. propagation Error propagation of electrical of electrical resistivity resistivity for for different different ERT ERT profiles, profiles, this figure this shows figure the shows the margin ofmargin error of (low error and (low high and high resistivity resistivity values) values) forfor sinkhole sinkhole delineation delineation on resistivity on resistivity model section. model section.

5. Conclusions The protection of sinkholes requires precise information on their location, shape, subsurface extent, and host rocks in order to determine a protection perimeter. The current study examines the potential of remote sensing and applied geophysics in mapping sinkholes and karst cavities. More specifically, Sentinel-2; ASTER-GDEM and Google Images were combined with fieldwork and GPS information to identify sinkholes in the Causse of El Hajeb. Electrical resistivity and electrical potential, acquired using ERT and SP respectively, provided subsurface extent and host rock information of the identified sinkholes. The results provide insights into the questions raised by the influence of water table fluctuations and their influence on karst aquifers. Subsurface sinkhole locations, their filling materials and host rocks suggest the presence of a complex subsurface network of karst conduits with numerous underground cavities developed in this area. This confirms the vulnerability of karst environments, where the development of sinkholes presents environmental and geo-hazards risks. We conclude that the combination of remote sensing and applied geophysics can effectively be applied for the sinkhole risk mitigation process, where satellite images and electrical resistivity can delineate geological formations and anthropic activities for both large and small areas. In addition, precision accuracy assessment provided the degree of confidence for subsurface sinkhole delineation process. Groundwater pollution risks posed by these sinkholes should be mitigated by establishing and implementing a protection perimeter, as well as reducing anthropic activities conducted nearby. The results of this study can aid decision makers involved in water management and water protection in implementing protection zones for the studied sinkholes. In addition, future research will be conducted on the sinkholes identified on the first site and also employ corrective measures by mobilizing local population. For this we recommend measures including the reduction of groundwater withdrawal, which contributes to the decline of the water table, as well as rational agricultural practices and water usage.

Resources 2020, 9, 51 28 of 32

Table 4. Precision measurement parameters estimated on the level of sinkhole.

Precision Parameters P1 P2 P3 P4 Absolute error (Ωm) 14.0 9.4 11.3 8.9 Relative uncertainty (%) 63.3 42.3 50.9 40.0

5. Conclusions The protection of sinkholes requires precise information on their location, shape, subsurface extent, and host rocks in order to determine a protection perimeter. The current study examines the potential of remote sensing and applied geophysics in mapping sinkholes and karst cavities. More specifically, Sentinel-2; ASTER-GDEM and Google Images were combined with fieldwork and GPS information to identify sinkholes in the Causse of El Hajeb. Electrical resistivity and electrical potential, acquired using ERT and SP respectively, provided subsurface extent and host rock information of the identified sinkholes. The results provide insights into the questions raised by the influence of water table fluctuations and their influence on karst aquifers. Subsurface sinkhole locations, their filling materials and host rocks suggest the presence of a complex subsurface network of karst conduits with numerous underground cavities developed in this area. This confirms the vulnerability of karst environments, where the development of sinkholes presents environmental and geo-hazards risks. We conclude that the combination of remote sensing and applied geophysics can effectively be applied for the sinkhole risk mitigation process, where satellite images and electrical resistivity can delineate geological formations and anthropic activities for both large and small areas. In addition, precision accuracy assessment provided the degree of confidence for subsurface sinkhole delineation process. Groundwater pollution risks posed by these sinkholes should be mitigated by establishing and implementing a protection perimeter, as well as reducing anthropic activities conducted nearby. The results of this study can aid decision makers involved in water management and water protection in implementing protection zones for the studied sinkholes. In addition, future research will be conducted on the sinkholes identified on the first site and also employ corrective measures by mobilizing local population. For this we recommend measures including the reduction of groundwater withdrawal, which contributes to the decline of the water table, as well as rational agricultural practices and water usage.

Author Contributions: Methodology, A.M., M.B.; validation, M.B., H.B., G.R., A.C., G.B., S.L., A.M.; software, A.A., A.M.; Writing—original draft, A.M., S.L., G.R.; writing—review & editing, A.C., G.B., G.R., S.L., M.B., A.M.; supervision, M.B., G.R. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Regional Council of Fès-Meknès in the framework of calls for Research Development and Innovation projects granted in September 2016. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Fairbridge, R.W. The Encyclopaedia of Geomorphology; Reinhold Book Corporation: New York, NY, USA, 1968. 2. Intrieri, E.; Gigli, G.; Nocentini, M.; Lombardi, L.; Mugnai, F.; Casagli, N. Sinkhole monitoring and early warning: An experimental and successful GB-InSAR application. Geomorphology 2015, 241, 304–314. [CrossRef] 3. Nisio, S.; Caramanna, G.; Ciotoli, G. Sinkholes in Italy: First results on the inventory and analysis. Geol. Soc. Spec. Public 2007, 279, 23–45. [CrossRef] 4. Sauro, U. Dolines and sinkholes: Aspects of evolution and problems of classiﬁcation. Acta Carsol. 2003, 32, 41–52. [CrossRef] 5. Muzirafuti, A.; Boualoul, M.; Randazzo, G.; Lanza, S.; Allaoui, A.; El Ouardi, H.; Habibi, M.; Ouhaddach, H. The use of remote sensing for water protection in the karst environment of the Tabular Middle Atlas/the causse of El Hajeb/Morocco. In Earth Observation Advancements in a Changing World; Chirici, G., Gianinetto, M., Eds.; AIT Series, Trends in Earth Observation; Italian Society of Remote Sensing: Firenze, Italy, 2019. Resources 2020, 9, 51 29 of 32

6. Hinaje, S.; Aït Brahim, L. Les bassins lacustres du Moyen Atlas (Maroc): Exemple d’activité tectonique polyphasée associée à des structures d’effondrement. Comun. Inst. Geol. Mineiro 2002, 89, 283–294. 7. Miche, H.; Saracco, G.; Mayer, A.; Qarqori, K.; Rouai, M.; Dekayir, A.; Chalikakis, K.; Emblanch, C. Hydrochemical constraints between the karst tabular Middle Atlas Causses and the Saïs basin (Morocco): Implications of groundwater circulation. J. Hydrogeol. 2018, 26, 71–87. [CrossRef] 8. Allaoui, A. Contribution des Etudes Structurales, Géophysiques et Hydrochimiques à la Compréhension des Ecoulements des Eaux Souterraines du Causse d’Agouray vers le Bassin de Saïss, (Maroc) [Contribution of Structural, Geophysical and Hydrochemical Studies to the Comprehension of Groundwater floWs from the Causse d’Agouray to the Saïss Basin, (Morocco)]. Ph.D. Thesis, Moulay Ismail University, Meknes, Morocco, 2019. 9. Amraoui, F.; Razack, M.; Bouchaou, L. Turbidity dynamics in karstic systems. Example of Ribaa and Bittit springs in the Middle Atlas (Morocco). Hydrol. Sci. J. 2003, 48, 971–984. [CrossRef] 10. Howell, B.; Fryar, A.; Benaabidate, A.E.; Bouchaou, L.; Farhaoui, M. Variable responses of karst springs to recharge in the Middle Atlas region of Morocco. Hydrogeol. J. 2019, 27, 1693–1710. [CrossRef] 11. Esper, J.; Frank, D.C.; Büntgen, U.; Verstege, A.; Luterbacher, J.; Xoplaki, E. Long-term drought severity variations in Morocco. Geophys. Res. Lett. 2007, 34, L17702. [CrossRef] 12. Akdim, B.; Amyay, M. Environmental vulnerability and agriculture in the karstic domain: Landscape indicators and cases in the Atlas Highlands, Morocco. Int. J. Speleol. 1999, 28B, 119–138. [CrossRef] 13. Aouragh, M.H.; Essahlaoui, A.; Ouali, A.E.; Hmaidi, A.E.; Kamel, S. Groundwater potential of Middle Atlas plateaus, Morocco, using fuzzy logic approach, GIS and remote sensing. Geomat. Hazards Risk 2017, 8, 194–206. [CrossRef] 14. Tennevin, M. Paysages karstiques du Moyen Atlas septentrional. Méditerranée 1978, 32, 23–32. [CrossRef] 15. Mhiyaoui, H.; Manar, A.; Remmal, T.; Boujamaaoui, M.; El Kamel, F.; Amar, M.; El Amrani, I. Deep quaternary volcanic structures in the central Middle Atlas (Morocco): Contributions of aeromagnetic mapping (structures profondes du volcanisme quaternaire du Moyen Atlas central (Maroc): Apports de la cartographie aéromagnétique. Bull. Inst. Sci. Rabat Sci. T. 2016, 38, 111–125. 16. Qarqori, K.; Rouai, M.; Moreau, F.; Saracco, G.; Dauteuil, O.; Hermitte, D.; Boualoul, M.; Le Carlier de Veslud, C. Geoelectrical tomography investigating and modeling of fractures network around Bittit spring (Middle Atlas, Morocco). Int. J. Geophys. 2012, 489634, 13. [CrossRef] 17. Kaufmann, G.; Romanov, D.; Tippelt, T.; Vienken, T.; Werban, U.; Dietrich, P.; Mai, F.; Borner, F. Mapping and modeling of collapse sinkholes in soluble rock: The Münsterdorf site, northern Germany. J. Appl. Geophys. 2018, 154, 64–80. [CrossRef] 18. Ceru,ˇ T.; Dolenec, M.; Gosar, A. Application of ground penetrating Radar Supported by Mineralogical- Geochemical Methods for Mapping Unroofed Cave Sediments. Remote Sens. 2018, 10, 639. 19. Zini, L.; Calligaris, C.; Forte, E.; Petronio, L.; Zavagno, E.; Boccali, C.; Cucchi, F. A multidisciplinary approach in sinkhole analysis: The Quinis Village case study (NE-Italy). Eng. Geol. 2015, 197, 132–144. [CrossRef] 20. Krawczyk, M.; Polom, U.; Trabs, S.; Dahm, T. Sinkholes in the city of Hamburg—New urban shear-wave reflection seismic system enables high-resolution imaging of subrosion structures. J. Appl. Geophys. 2012, 78, 133–143. [CrossRef] 21. Barbara, T.; Halima, A.; Abdessamad, C.; Fatima, E.B.; Mohamed, C. Remote Sensing and GIS Contribution to the Investigation of Karst Landscapes in NW-Morocco. Geosciences 2014, 4, 50–72. 22. Christoph, S.; Olaf, B.; Bernhard, E. Combining digital elevation data (SRTM/ASTER), high resolution satellite imagery (Quickbird) and GIS for geomorphological mapping: A multi-component case study on Mediterranean karst in Central Crete. Geomorphology 2009, 112, 106–112. 23. Kim, Y.J.; Nam, B.H.; Youn, H. Sinkhole Detection and Characterization Using LiDAR-Derived DEM with Logistic Regression. Remote Sens. 2019, 11, 1592. [CrossRef] 24. Galve, J.P.; Castañeda, C.; Gutiérrez, F.; Herrera, G. Assessing sinkhole activity in the Ebro Valley mantled evaporite karst using advanced DInSAR. Geomorphology 2015, 229, 30–44. [CrossRef] 25. Billi, A.; Fillippis, L.; Poncia, P.P.; Sella, P.; Faccenna, C. Hidden Sinkholes and Karst cavities in Travertine plateau of the highly-populated geothermal seismic territory (Tivoli, central Italy). Geomophology 2016, 255, 63–80. [CrossRef] 26. Youssef, A.M.; El-Kaliouby, H.M.; Zabramawi, Y.A. Integration of remote sensing and electrical resistivity methods in sinkhole investigation in Saudi Arabia. J. Appl. Geophys. 2012, 87, 28–39. [CrossRef] Resources 2020, 9, 51 30 of 32

27. Rizzo, E.; Giampaolo, V.; Capozzoli, L.; Grimaldi, S. Deep Electrical Resistivity Tomography for the Hydrogeological Setting of Muro Lucano Mounts Aquifer (Basilicata, Southern Italy). Geofluids 2019, 2019, 6594983. [CrossRef] 28. Baudon, C.; Fabuel-Perez, I.; Redfern, J. Structural style and evolution of a Late Triassic rift basin in the Central High Atlas, Morocco: Controls on sediment deposition. Geol. J. 2009, 44, 677–691. [CrossRef] 29. Michard, A. Elément de géologie marocaine. Notes et Mém. Serv. Géol. Maroc. 1976, 252, 408. 30. Laville, E.; Petit, J.P. Role of synsedimentary strike-slip faults in the formation of the Moroccan Triassic basins. Geology 1984, 12, 424–427. [CrossRef] 31. Fedan, B. Evolution géodynamique d’un basin intra-plaque sur décrochement: Le Moyen Atlas Maroc Durant le Méso-Cénozoïque. Trav. Inst. Sci. Rabat. 1989, 18, 1–142. 32. Manspeizer, W.; Puffer, J.H.; Cousminer, H.L. Separation of Morocco and eastern North America: A Triassic-Liassic stratigraphic record. Geol. Soc. Am. Bull. 1978, 89, 901–920. [CrossRef] 33. Warme, J.E. Evolution of the Jurassic High-Atlas Rift, Morocco: Transtension, Structural and Eustatic Controls on Carbonate Facies, Tectonic Inversion. Guidebook, AAPG Field Seminar, 24 September–1 October 1989. Colorado School of Mines, Golden, Colorado, Publication Number 9. 1989. Available online: http://www.worldcat.org/title/evolution-of-the-jurassic-high-atlas-rift-morocco-transtension- structural-and-eustatic-controls-on-carbonate-deposition-tectonic-inversion/oclc/20552972 (accessed on 30 June 2016). 34. Wilmsen, M.; Neuweiler, F. Biosedimentology of the Early Jurassic post-extinction carbonate depositional system, central High Atlas rift basin, Morocco. Sedimentology 2008, 55, 773–807. [CrossRef] 35. Mattauer, M.; Tapponier, P.; Proust, F. Sur les mécanismes de formation des chaines intracontinentales. L’exemple des chaines atlasiques du Maroc. Bull. Soc. Geol. Fr. 1977, 7, 521–526. [CrossRef] 36. Arboleya, M.; Teixell, L.A.; Charroud, M.; Julivert, M. A structural transect through the High and Middle Atlas of Morocco. J. Afr. Earth Sci. 2004, 39, 319–327. [CrossRef] 37. Qarqori, K. Contribution à l’étude du Réservoir Discontinu et Karstique des Causses Moyen-Atlasiques et de sa Jonction Avec le Bassin de Saïs par Télédétection Spatiale et Imagerie Géophysique [Contribution to the study of the discontinuous and karstic reservoir of the Middle Atlasic Causses and its junction with the Saïs Basin by Remote Sensing and Geophysical Imagery]. Ph.D. Thesis, Moulay Ismail University, Meknes, Morocco, 2015. 38. Gomez, F.; Barazangi, M.; Bensaid, M. Active tectonism in the intracontinental Middle Atlas Mountains of Morocco: Simultaneous crustal shortening and extension. J. Geol. Soc. Lond. 1996, 153, 389–402. [CrossRef] 39. Bouya, N.; El-Ouardi, H.; Habibou, E.H. Geologic interpretation of the aeromagnetic survey in the Agourai area (Central Morocco). Centr. Europ. Geol. 2013, 56, 381–395. [CrossRef] 40. El-Ouardi, H.; Boualoul, M.; Ouhaddach, H.; Habib, M.; Muzarafuti, A.; Allaoui, A.; Amine, A. Fault analysis and its relationship with karst structures affecting lower Jurassic limestone in the Agourai plateau (Middle Atlas, Morocco). Geogaceta 2018, 63, 119–122. 41. Amraoui, F. Contribution à la Connaissance des Aquifères Karstiques: Cas du Lias de la Plaine du Saïs et Du Causse Moyen Atlasique Tabulaire (Maroc). [Contribution to the Knowledge of Karstic Aquifers: Case of the Lias of the Saïs Plain and the Middle Atlas Moyen Causse (Morocco)]. Ph.D. Thesis, Université Hassan II Ain Chock, Casablanca, Morocco, 2005. 42. Shalev, E.; Lyakhovsky, V.; Yechieli, Y. Salt dissolution and sinkhole formation along the Dead Sea shore. J. Geophys. Res. 2006, 111, 1–12. [CrossRef] 43. Bousbih, S.; Zribi, M.; El Hajj, M.; Baghdadi, N.; Lili-Chabaane, Z.; Gao, Q.; Fanise, P. Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens. 2018, 10, 1953. [CrossRef] 44. Mazzia, V.; Khaliq, A.; Chiaberge, M. Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Appl. Sci. 2020, 10, 238. [CrossRef] 45. Nguyen, M.D.; Baez-Villanueva, O.M.; Bui, D.D.; Nguyen, P.T.; Ribbe, L. Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon). Remote Sens. 2020, 12, 281. [CrossRef] Resources 2020, 9, 51 31 of 32

46. Goffi, A.; Stroppiana, D.; Brivio, P.A.; Bordogna, G.; Boschetti, M. Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features. Int. J. Appl. Earth Obs. Geoinf. 2020, 84, 101951. [CrossRef] 47. Pulvirenti, L.; Squicciarino, G.; Fiori, E.; Fiorucci, P.; Ferraris, L.; Negro, D.; Gollini, A.; Severino, M.; Puca, S. An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data. Remote Sens. 2020, 12, 674. [CrossRef] 48. Muzirafuti, A.; Randazzo, G.; Lanza, S.; Crupi, A.; Cascio, M.; Barreca, G.; Gregorio, F. Sentinel-2 images for the monitoring of dissolved contaminants on Sicily’s South-east coast. In Proceedings of the 21th EGU General Assembly, Vienna, Austria, 7–12 April 2019. 49. Van der Meer, F.D.; van der Werff, H.M.A.; van Ruitenbeek, F.J.A. Potential of ESA’s Sentinel-2 for geological applications. Remote Sens. Environ. 2014, 148, 124–133. [CrossRef] 50. Mielke, C.; Boesche, N.; Rogass, C.; Kaufmann, H.; Gauert, C.; de Wit, M. Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: Hyperion/OLI and EnMAP/Sentinel-2. Remote Sens. 2014, 6, 6790–6816. [CrossRef] 51. Wu, Q.; Deng, C.; Chen, Z. Automated delineation of karst sinkholes from lidar-derived digital elevation models. Geomorphology 2016, 266, 1–10. [CrossRef] 52. Doctor, D.H.; Young, J.A. An Evaluation of Automated GIS Tools for Delineating Karst Sinkholes and Closed Depressions from 1-Meter LIDAR-Derived Digital Elevation Data. In Proceedings of the 13th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst, Carlsbad, NM, USA, 1 May–15 August 2013; pp. 449–458. 53. Boualla, O.; Mehdi, K.; Zourarah, B. Collapse dolines susceptibility mapping in Doukkala Abda (Western Morocco) by using GIS matrix method (GMM) Model. Earth Syst. Environ. 2016, 2, 1–18. 54. Ivanov, V.Y.; Vivoni, E.; Bras, R.L.; Entekhabi, D. Catchment hydrologic response with a fully distributed triangulated irregular network model. Water Resour. Res. 2004, 40, 1–23. [CrossRef] 55. Jardani, A.; Revil, A.; Boleve, A.; Dupont, J.P.; Barrash, W.; Malama, B. Tomography of the Darcy velocity from self-potential measurements. Geophy. Res. Lett. 2007, 34, 1–6. [CrossRef] 56. Jardani, A.; Dupont, J.P.; Revil, A. Self-potential signals associated with preferential ground water flow pathways in sinkholes. J. Geophys. Res. 2006, 111, 1–13. [CrossRef] 57. Cheng, Q.; Tao, M.; Chen, X.; Binley, A. Evaluation of electrical resistivity tomography (ERT) for mapping the soil–rock interface in karstic environments. Environ. Earth Sci. 2019, 78, 439–452. [CrossRef] 58. Cheng, Q.; Chen, X.; Tao, M.; Binley, A. Characterization of karst structures using quasi-3D electrical resistivity tomography. Environ. Earth Sci. 2019, 78, 285–296. [CrossRef] 59. Jardani, A.; Revil, A.; Santos, F.; Fauchard, C.; Dupont, J.P. Detection of preferential infiltration pathways using joint inversion of self-potential and EM-34 conductivity data. Geoph. Prospect. 2007, 55, 1–11. [CrossRef] 60. Zhou, W.; Beck, B.F.; Adam, A.L. Effective electrode array in mapping karst hazards in electrical resistivity tomography. Environ. Geol. 2002, 42, 922–928. [CrossRef] 61. Loke, M.H. Tutorial: 2D and 3D Electrical Imaging Surveys; University of Alberta: Edmonton, AB, Canada, 2004. Available online: https://sites.ualberta.ca/~{}unsworth/UA-classes/223/loke_course_notes.pdf (accessed on 1 January 2020). 62. Jardani, A.; Revil, A.; Dupont, J.P. 3D self-potential tomography applied to the determination of cavities. Geophys. Res. Lett. 2006, 33, L13401. [CrossRef] 63. Boleve, A.; Revil, A.; Janod, F.; Mattiuzzo, J.L.; Jardani, A. Forward modeling and validation of a new formulation to compute self-potential signals associated with ground water flow. Hydrol. Earth Syst. Sci. 2007, 11, 1661–1671. [CrossRef] 64. Ikard, S.; Pease, E. Preferential groundwater seepage in karst terrane inferred from geoelectric measurements. Near Surf. Geophs. 2019, 17, 43–53. [CrossRef] 65. Revil, A.; Linde, N.; Cerepi, A.; Jougnot, D.; Matthai, S.; Finsterle, S. Electrokinetic coupling in unsaturated porous media. J. Colloid Interface Sci. 2007, 313, 315–327. [CrossRef][PubMed] 66. Boleve, A.; Revil, A.; Janod, F.; Mattiuzzo, J.L.; Jardani, A. A new formulation to compute self-potential signals associated with ground water flow. Hydrol. Earth Syst. Sci. 2007, 4, 1429–1463. 67. Loke, M.H.; Chambers, J.E.; Rucker, D.F.; Kuras, O.; Wilkinson, P.B. Recent developments in the direct-current geoelectrical imaging method. J. Appl. Geophys. 2013, 95, 135–156. [CrossRef] Resources 2020, 9, 51 32 of 32

68. deGroot-Hedlin, C.; Constable, S. Occam’s inversion to generate smooth, two dimensional models form magnetotelluric data. Geophysics 1990, 55, 1613–1624. [CrossRef] 69. Telford, W.M.; Geldart, L.P.; Sheriﬀ, R.E. Applied Geophysics. Chapter 5 Electrical Properties of Rocks and Minerals; Cambridge University Press Retrieval: Cambridge, UK, 1990. 70. Gutiérrez, F.; Cooper, A.H.; Johnson, K.S. Identiﬁcation, prediction and mitigation of sinkhole hazards in evaporite karst areas. Environ. Geol. 2008, 53, 1007–1022. [CrossRef] 71. Martin, J. Le Moyen Atlas Central. Etude Géomorphologique. [The Central Middle Atlas. Geomorphological Study]. Ph.D. Thesis, Université Paris Diderot - Paris 7, Paris, France, 1976. 72. Waltham, T.; Bell, F.; Culshaw, M. Sinkholes and Subsidence; Springer: Berlin/Heidelberg, Germany, 2005; p. 382.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).