Integration of Remote Sense and Geographic Information Systems in Geological Faults 1 Detection in Crete Island , Greece

8 Fracture systems are of great importance in the field of structural geology. Faults commonly 9 afford easy passage to groundwater and fluids such as hydrothermal fluids and magmas (mineral 10 entrapment over the years) or even contribute in earthquake hazard monitoring. For a geologist it 11 is not always easy to discern such morphotectonic structures at close range (i.e. heavy 12 overgrowth of vegetation). Both remote sensing techniques and spatial modeling (GIS) permit 13 the recognition and better understanding of the brittle tectonics in an area. This study was an 14 effort to delineate the tectonic structures (i.e. fault system) on the Crete Islands by combining 15 Sentienl-2 satellite data and spatial data. For the enhancement and better discrimination of 16 photolineaments primarily recognized on satellite imagery, a variety of enhancement techniques 17 have been applied. The evaluation of a photolineament as a potential fracture zone was based on 18 several factors; the DEM of the study area, the shaded relief, the slopes and corresponding 19 aspects, the drainage network, the geology and general observations on vegetative coverage 20 appearance. The application of these methods revealed several fracture zones, which we 21 recommend being certified by field investigations. Fault-mapping results may be used for a 22 variety of purposes. Indicative places of large concentration of groundwater are of vital 23 importance for subsequent exploitation by areas of need. Furthermore, because the well-known 24 Anatolian fault zone extends over the Northern part of Crete, the present work may provide 25 useful information for further analysis by geophysists and seismologists. 26


Introduction
O' Leary et al. (1976) and Colwell, (1983) gave a good-informing definition of the term lineament.It refers to a mappable, single or composite linear feature of a surface, whose parts are aligned in a rectilinear or slightly curvilinear relationship and which differs distinctly from the pattern of adjacent features and presumably reflects a subsurface phenomenon.
An extensive literature review on the consideration of linear features and lineaments is well presented by Siegal and Gillespie (1980).Suffice to say here, that lineaments identified on aerial photographs and satellite images may represent diverse topographic features (drainage lines), vegetation/soil alignments, coastal lines, crests, ridges, stratigraphic contacts, fold axis (foliation) and seismic zones (Lunkka 1994).
The importance of detecting lineaments lies in the fact that they often represent fault systems.
Faults indicate failure of the crust along a surface, accompanied by the relative movement of the geological units from both sides of that surface (Caumon et al. 2009).Such a zone of structural weakness is a major component in structural geology and may be related to a series of other phenomena.It is noticeable that, in many cases in the past, such a system has been related to seismic events occurring in an area.Locating and identifying the movement is of dual importance to a geologist as the impacts of seismic hazards may encompass of both lives and economic losses (Colwell 1983).
Furthermore, faults identification is of economic importance too.That is for; they commonly afford easy passage to several fluids like water, hydrothermal fluids and magmas (Oelkers et al. 2009).Groundwater on the one hand, can be easily transferred through such channel ways, sometimes over large distances and finally reaching and supplying areas of need.On the other hand, mineral entrapping at some time in the past may be of advantageous exploration use (Elhag and Bahrawi 2014).
In the case of faults detection, integration of Remote Sensing and Geographic Information Systems is an issue of great interest.That is for, photolineaments on satellite images do not always account for failure of the crust.They may well represent the drainage lines of the area (rivers), geological boundaries of formations or even cultural features such as roads.The criteria for faults investigation are discussed in several aspects (Greenbaum 1992;Schulson 2004).
Vegetation may play a great role in photogeology for it may reveal underlying structural features not easily discerned at close range.As far as fracturing is concerned, attention should be given to preferential growth of vegetation along linear-curvilinear surfaces (Rodomsky 2011).The reason for this, as mentioned earlier on, is that fractures often act as channel for underground water.
Water subsequently, increases the moisture content of soil (in relation to surrounding area) and encourages in one sense, a preferential alignment of vegetation along these fracture zones (Singhal and Gupta 2010).Moreover, it is not rare to identify on an image (or photograph) abrupt changes in types of vegetation or even sudden disappearance of a certain plant species.
Particularly in the case where vegetation varies over a surface underlain by the same type of bedrock, fracturing is implied (Phillips et al. 2008).
Faults in some cases, may even throw permeable against impermeable rocks.Water again finds ways along the zone of structural weakness and reaches the Earth's surface by numerous springs (along the contact of the two formations).At these points, high moisture conditions are particularly favorable to intense vegetation growth (Singhal and Gupta 2010).Abrupt changes in slope are commonly associated with brittle tectonics (Agliardi et al. 2001;Agliardi et al. 2009).Attention should be given though to the orientation of slopes with respect to the illumination source.It may have an impact on vegetation and hence confuse the photointerpreter.As Singhal et al. (2010) pointed out, this factor may greatly influence the survival of plants and explained that moisture content along the surface of a given slope varies.
That is for; the parts that are not so well exposed to sun lighting retain less moisture than the more illuminated ones and hence a contrast of low-to-high density vegetation growth occurs.
The knowledge of underlying bedrock type is crucial for the processing of a photogeologist's work.Any truncation and displacement of beds may effectively reveal fracturing (Odling et al. 1999).Abrupt changes in geological formations may also indicate fault zones (Stein 1991).A fault may bring in contact rocks of different petrology and general characteristics that show no physical or inherent association between the two formations.In other words, these formations are not expected to be in contact with each other unless fracturing has occurred at some time in the past (Boggs 2009).
Variations in vegetation discussed above, may be also due to petrological differences of underlying rocks, without necessarily the presence of a fault (Rodomsky 2011).One explanation for such variations is that of bedrock influencing soil composition and consequently the plant species that can exist.
This study was an effort to delineate the brittle tectonics of Crete islands in Kolymvari area (Greece) by using spatial models and digital image processing.The satellite image that was initially provided in the laboratory was enhanced by a variety of methods for the better discrimination of photolineaments.Certainly, not all lineaments detected, were expected to represent failure of the crust (faults) and could be easily confused with roads and rivers.They had to be studied with respect to real-life conditions.In particular, information related to geomorphology (elevation, slopes, aspect, and drainage system) and geology of the region (types of rocks, boundaries of geological formations) was integrated with the satellite data and the final evaluation of a certain lineament, as a potential fault was carried out.

Study Area
The hydrological basin of the study area is situated in the western part of Crete Island and is referred to Municipality of Kolymvari (Figure 1).The landscape structure is fundamentally mountainous, resulting in plain only near the coast.The area has a sub-humid Mediterranean climate with an annual average temperature of 19.96°C (Elhag and Bahrawi 2016).The watershed of Kolymvari is mainly an agricultural area where the most common cultivations are olive trees, citrus trees, vineyards, and vegetables.The area has also light industrial activities such as olive mills, wineries, and other agricultural factories.In the coastal zone of Keritis watershed, there are many touristic units (Papafilippaki et al. 2007).However, the possible flow paths were all directed to Kolymvari Stream, which is, therefore, the surface water body (Elhag et al. 2017).From geotectonic point of view, it has not been easy to classify.Lack of fossils in many cases and difficulties in comprehending the exact geo-processes involved in the formation of the Aegean Sea during the Pliocene and Pleistocene, have been an issue under investigation from many geologists over the past years.

Remote Sensing Data
The goal of digital image processing was to improve such spectral responses and generate images more interpretable than the original ones, where photolineaments would be better

Lineaments Evaluation by RS means
A variety of enhancement methods have been applied to digital raw data in order to improve the spectral characteristics of objects and emphasize the photolineaments of the area under investigation.The original bands have been contrast-stretched for DNs occupy more gray levels than before, in accordance with their frequency of occurrence (histogram equalize stretching).
According to Oskoei and Huosheng (2010), the Isotropic Laplacian, non-directional convolution  Grey level threshold and Contrast stretching, Sentienl-2, B5 has turned out to be the most appropriate one, for in this near infrared channel, land areas are highly reflected whereas water areas are highly absorbed (Elhag 2017) displayed over a dynamic range of 65536 gray levels (Lillesand et al. 2014).In many cases however, only a small portion of these 65536 levels that are available by devices is utilized.The radiometric ranges (minimum and maximum values of DN values) in each of the six ( 6) utilized bands (Sentienl-2 B2-B7) of the original dataset are corrected.
Spatial Enhancement, the concept is based on a moving window (the so-called Kernel window) that in short, contains an array of weighting factors, moves successively over all the pixels of a single black and white image (individual band) and ascribes new weighted DN-values as a result of the weighted original ones (Gupta 2017).A high pass filter, in order to emphasize high frequency features or else local spatial detail (Aiazzi et al. 2002).The filter size was 5*5 and was added back to the fourth band of the original band so that low frequency brightness information was not totally lost.
Principal Component Analysis (PCA) is a unique mathematical transformation, designated to reduce such redundancy in multispectral data (Kaarna et al. 2000).The idea is to compress all the information contained in the n-original bands, into fewer than N-new channels/components (Lillesand et al. 2014).
The high interband correlations (very close to 1) suggested that the best way to proceed with this work was by applying a Principal Component Analysis (Table 1).No pair of bands presented covariance =0 i.e. no pair of bands were completely independent to each other.Moreover, as covariance > 0, data appeared to be positively correlated (Elhag 2016).
Rationing, it involves the division as well as more complex functions (additions, subtractions, multiplications) between the DNs of two single bands (Wu et al. 2008).The technique is indicative for both preserving the spectral reflectance characteristics of surficial matter and masking brightness variations derived from illumination conditions and topographic effects (Soulakellis et al. 2006).0,687812 0,7474 0,813061 0,607007 1 0,956571297 Band 7 0,767209 0,815756 0,879578 0,468485 0,9566 1 False Color Composite (FCC), one band at a time can be displayed in each of the three colorguns; generating false -color composite images (objects do not appear in their natural colors) that are undoubtedly more interpretable to human's eye than black and white images.For the present work several combinations have been tried between original bands, stretched ones, the principal component products as well as the ratio images (Pohl and Van Genderen 1998).The scope was to produce a FCC image where Photolineaments would be discriminated to a satisfactory degree for their further digitizing on screen.Table 2 summaries the used band combinations and its uses.

Lineaments Evaluation by GIS means
Basically, not all photolineaments detected using Remote Sensing techniques are represented as fracturing (Karnieli et al. 1996).Cultural features such as roads had to be readily recognizable on satellite data to avoid their confusion with faults and lead to false photointerpretation results.
Schematic flow chart of the implemented procedure in spatial image analysis is illustrated in Figure 3.No clear discrimination of the geology of the area was achieved.

FCC6 PC1, B5convolved, B7stretched
Less certain discrimination of the geological boundaries/geology of the area.No actual discrimination was made.
FCC7 PC1, PC2, PC3 no prediction of the color characterizing each feature was easily made.
The overthrust in the northern part was readily differentiated from the rest of the study area.FCC8 PC1, PC2, B3 The use of two PCs did not give noteworthy results.Bright green pixels represented vegetative coverage, whereas dark tones of green delineated to a limited extend the drainage network.The elongated feature was not enhanced in this product.FCC9 PC1, B5convolved, PC3 Topography was better expressed than in the previous case (PC1 in combination with PC2).Several lineaments were readily seen (for nondirectional filtering of B5 component).FCC10 B7enhanced, B5convolved, B3 No particular enhancement was observed compared to the previous products.

FCC11 PC1, B5convolved, B5
Several photolineaments were enhanced, topography was well expressed while stratigraphic contacts were not well delineated.

FCC13 PC1, B6, B3
Topographic sense was lost.Some vegetation alignments and abrupt changes in green tonal variations.FCC14 PC1, B5, B3 topography was very well expressed whereas objects appeared in very natural colors and tones.The drainage network was well delineated, and several lineaments were detected.The same difficulty met in previous combinations in discriminating between the diverse geological formations.features that are sensitive to these bands.More particularly, the spectral responses of the vegetation biomass that is present in the scene (Sentinel-2, B5) contrasted to the soil-vegetation differentiations (Sentinel-2, B2).
As far as PC3 is concerned, the output image is of less significance, as no clear discrimination is available.Information related to the correlation between the bands and the extracted PC's are also given in Table 5. Factor loadings R, were computed in Excel by using the following formula: where x refers to the xth channel and p to the pth component (Johnson and Wichern 2002).
Table 5. Factor loadings PC1 PC2 PC3 Band-2 0.778556 -0.48975 -0.35283 Band-3 0.841781 -0.33679 -0.39796 Band-4 0.891965 -0.31568 -0.2877 Band-5 0.623355 0.716576 -0.31203 Band-6 0.985093 0.058921 0. 155217 Band-7 0.968296 -0.14133 0. 119375 The correlation coefficient R extends in a range of -1 to 1.This is nothing more than unitnormalization so that loadings are between -1 and 1 (Johnson and Wichern 2002).The closer the coefficient is to -1 or 1, the more significant is the contribution of a channel to the corresponding PC.Similarly, a loading close to zero is an indication of practically no contribution of a band to the corresponding PC.Thus, PC1 is highly correlated to all bands but mostly to Sentinel-2, B6 and Sentinel-2, B7.PC2 on the other hand, is highly correlated to Sentinel-2, B5 and may be used instead of the latter for a color-composite image.For vegetation is highly reflected in near infrared and low reflected in visible red, (Sentinel-2, B4)/ (Sentinel-2, B5) ratio generated an image where vegetation appeared in dark tones.In an analogous way, due to the comparatively high reflectance of vegetation in visible green to the lower reflectance in mid infrared, vegetation in (Sentinel-2, B3)/ (Sentinel-2, B6) image appeared in light tones.In both ratios, topography has been eliminated and the area gave the impression of being flat.The two images however, as well as the NDVI product can be safely viewed together for the detection of vegetation biomass.Positive NDVI values, correlate with vegetation while null values correspond to rocks and soil (Johnson and Wichern 2002).
That is for Sentinel-2, B7 is important in geology for the discrimination of geologic rock type whilst one of Sentinel-2, B4's purposes is the detection of geological boundaries where no particular enhancement was noticeable though.
Efforts to use others than Sentinel-2, B2 band in the ratio applied resulted in negative pixel values (black pixels within the study area) and hence, could not be used for further analysis.In comparison with the rest of the ratios, ((Sentinel-2, B2) 2 +( Sentinel-2, B5) 2 ) 1/2 product delineated in a better way vegetation amount while several lineaments were emphasized.
Edge enhancement methods resulted in the enhancement of individual bands (black and white images) to a satisfactory degree.However, due to the low capability of human's eye to discern the slight spectral responses of objects in gray scaling, the need for multispectral imagery (display of more than one bands at a time) resulted in the creation of False Color Composite products (FCC).A variety of RGB combinations have been tested.FCC14 however, appeared to be the most appropriate one for photolineaments discrimination.PC1 accounted for topography (essential element in photointerpretation) whereas R5 reflected the spectral characteristics of vegetation amount (for Sentinel-2, B5 component of ratio R5).Unfortunately, despite the contribution of Sentinel-2, B7 in the same ratio, stratigraphic contacts were not easily detected except for FCC14 for the area under investigation.

GIS Lineaments Evaluation
The height values were divided in 15 classes and then coded in colors in order to better highlight the relief.The artificial illumination of DEM was from a Northwest direction (315°) and from 45° altitude.From statistical point of view, almost one third (1/3) of the area is characterized by slopes less than 8.7° whilst only 4,8% accounts for slopes >43°.As far as the orientation of slopes is concerned, very few surfaces have a northern-northeastern tendency.The scenery denotes all kinds of aspect of slopes.
The geology of the area under investigation with the potential tectonics as resulted from the integration of the enhanced satellite data and the spatial information.The corresponding area show that most of the faults detected on FCC 14 (Figure 4) have been already mapped whereas several more apply for field cross-checking.The integration of Remote Sensing and Geographic Information Systems has proven to be a reliable.method of fault mapping to a satisfactory degree.Only at one side of the mainstream, smaller tributaries develop, which is a very good example of such a valley is presented while the relative displacement of beds at the sides of the streamline, safely enhances the suspicion of underlying fracturing.that no confusion would be made with faults.The same was true for drainage lines and geological boundaries, which in many cases were strongly emphasized and could easily result in false identification.
The best combination has proven to be for the first Principal Component (PC1 in red color-gun, accounting for 82.27% of total scene variance), ratio product R5 = ((Sentinel-2, B5) 2 +( Sentinel-2, B7) 2 ) 1/2 , and one of the original bands (Sentinel-2, B3).For Sentinel-2, B5's contribution in R5, the spectral characteristics of vegetation amount were emphasized (vegetation is particularly responsive in Sentinel-2, B5).Hence, the preferential growth of vegetation along linear features, suggested in many cases underlying fracturing.Despite Sentinel-2, B7's contribution in the same ratio, the spectral responses of geological formations were not highlighted except for very few cases of apparent displacement of beds.PC1 on the other hand accounted for the topographic information that was lost due to ratio's contribution.Spatial modeling (DEM and products) was crucial for the evaluation of photolineaments detected on FCC14 (PC1, R5, in Sentinel-2, B3).The Digital Elevation Model of the area under investigation and the derived maps of slope and aspect enabled several times to imply for fracturing (wherever abrupt changes of slope and aspect were observed at the sides of a particular photolineament).
The drainage network has proven to be particularly helpful and informative on the underlying structures.Rift valleys and displacement of rivers' routes were common phenomena within the scene of observation.In many cases parallel vegetation alignments differing from the vegetation outcrop of the surrounding area (abrupt tonal differences) further assured us about possible failure of the crust.Of course, the analysis was not based on the number of criteria satisfied each time.A fault associated with more criteria than another does not necessarily make it a fracture zone of more significance.
In comparison with the conventional methods of mapping (field investigations) most of the faults recognized by automated watershed delineation, have already been mapped during field investigations.Several of the zones found, were not discerned on the FCC14.That was due to the following parameters that someone needs to bear always in mind before proceeding with analogous to the present researches.
Faults mapping by using satellite data integrated with spatial information (GIS), may lead to quite noteworthy results.Experience is crucial for an accurate photointerpretation.In this case study, most of the faults that have been mapped in the past during fieldwork investigations were also identified on the satellite image.Some of the photolineaments discerned but were not readily seen on FCC14.That should be for all factors explained earlier on.Lastly, for all photolineaments that were identified on the satellite image and raised suspicion of failure of the crust, in-situ data collection for verification of the results is strongly recommended, in order to be total aware of the tectonics of the area.
Geosci.Instrum.Method.Data Syst.Discuss., https://doi.org/10.5194/gi-2018-47Manuscript under review for journal Geosci.Instrum.Method.Data Syst.Discussion started: 2 October 2018 c Author(s) 2018.CC BY 4.0 License.discerned.The digital image of Sentienl-2A (Tile Number: T34SGE, with 0% cloud cover) dating 12/03/2018 and covering the pilot area, has been subject to several enhancement techniques.There were two basic types of distortions that had to be reckoned with, prior to any of the enhancement methods; radiometric distortions and geometric distortions.

Figure 1 .
Figure 1.Location of the study area Geosci.Instrum.Method.Data Syst.Discuss., https://doi.org/10.5194/gi-2018-47Manuscript under review for journal Geosci.Instrum.Method.Data Syst.Discussion started: 2 October 2018 c Author(s) 2018.CC BY 4.0 License.filtering as well as edge-enhancement technique, have been applied to Sentienl-2 (for vegetation and geological features are particularly responsive in these two wavelength bands).Principal Component Analysis aimed to compress information in fewer bands than the original ones, uncorrelated to each other.Images generated by rationing, accounted for the real spectral characteristics and physical properties of objects (topography and illumination effects on the brightness values of pixels, have been eliminated).Schematic flow chart of the implemented procedure in digital image analysis is illustrated in Figure 2.

Figure 4 .
Figure 4. Fault detection using False Color Composition FCC14

273A
part of this zone has been already mapped.It is clear though, that the zone extends over a 274 larger area than the one found and mapped during field investigations.275 Several more examples that enforced the belief of underlying failure as was initially suspected 276 during photointerpretation, and undoubtedly the drainage network has proven to be an 277 indispensable tool in the evaluation of photolineaments.278 Geosci.Instrum.Method.Data Syst.Discuss., https://doi.org/10.5194/gi-2018-47Manuscript under review for journal Geosci.Instrum.Method.Data Syst.Discussion started: 2 October 2018 c Author(s) 2018.CC BY 4.0 License.