Discrimination of geological units in southern margin of Alborz Mountain in Iran using ASTER satellite imagery

Introduction

Geological mapping is the process of physically going to the field observation and recording the geological information from the rocks that protrudes from the surface of the earth. The information that usually the scientists attempt to find are the boundaries between different structures and rock types, such as fault-lines and evidence of the rocks undergoing deformation (Davis et al., 2011Davis, G. H., Reynolds, S. J., and Kluth, C. F. (2011). Structural geology of rocks and regions. John Wiley & Sons.). Geologic mapping is a scientific field that aims to produce usable maps for various applications, such as quality assessment of ground waters and pollution hazards; land-use planning and land management; forecasting volcano, landslide, and earthquake; describing energy and mineral resources as well as their extraction costs; waste repository location; and general education (Compton, 1985Compton, R. R., and Compton, R. R. (1985). Geology in the Field (p. 416). New York: Wiley.; Soller, 2002Soller, D. R. (Ed.). (2002). Digital Mapping Techniques' 02, Workshop Proceedings: May 19-22, 2002, Salt Lake City, Utah (Vol. 2, No. 370). US Department of the Interior, US Geological Survey.). A powerful tool that can be implemented to improve the process of geological mapping is the technology of remote sensing (Varnes, 1974Varnes, D. J. (1974). The logic of geological maps, with reference to their interpretation and use for engineering purposes. USGS Professional Paper837.; Bernknopf, 1993Bernknopf, R. L. (1993). Societal value of geologic maps (Vol. 1111). DIANE Publishing.; Pour and Hashim, 2015Pour, A. B., and Hashim, M. (2015). Structural mapping using PALSAR data in the central gold belt, Peninsular Malaysia. Ore Geology Reviews, 64, 13-22. 10.1016/j.oregeorev.2014.06.011.; Yang et al., 2018Yang, M., Ren, G., Han, L., Yi, H., and Gao, T. (2018). Detection of Pb–Zn mineralization zones in west Kunlun using Landsat 8 and ASTER remote sensing data. Journal of Applied Remote Sensing, 12(2), 026018-026018. 10.1117/1.JRS.12.026018.).

Remote sensing technology is useful for the explorations of minerals and geothermal energy, geological investigations, and assessment of geotechnical engineering and environmental geology. Remote sensing is also an essential tool for understanding the significant natural hazards pertinent to geology such as earthquakes, floods, avalanches, river channel migration and avulsion, liquefaction, landslides and debris flows, sinkholes, subsidence, tsunamis, and volcanoes (Bhan and Krishnanunni, 1983Bhan, S. K., & Krishnanunni, K. (1983). Applications of remote sensing techniques to geology. Proceedings of the Indian Academy of Sciences Section C: Engineering Sciences, 6, 297-311.). The modern and applicable remote sensing tools include optical images, thermal data, LiDAR and digital elevation models (DEM), microwave and SAR data, hyperspectral remote sensing, and archived aerial photos and satellite images. Therefore, it is possible to perceive the Earth beyond our visual capability and transact the temporal and spatial limitations of earth observations (Prost, 2013Prost, G. L. (2013). Remote sensing for geoscientists: image analysis and integration. New York: CRC Press.).

Many image processing techniques have been presented in recent decades for purposes of geological mapping using remote sensing technology, such as band ratio (Inzana et al., 2003Inzana, J., Kusky, T., Higgs, G., and Tucker, R. (2003). Supervised classifications of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of central Madagascar. Journal of African Earth Sciences, 37(1-2), 59-72. 10.1016/S0899-5362(03)00071-X.), correlation coefficient (Kühn et al., 2009Kühn, J., Brenning, A., Wehrhan, M., Koszinski, S., and Sommer, M. (2009). Interpretation of electrical conductivity patterns by soil properties and geological maps for precision agriculture. Precision Agriculture, 10(6), 490-507. 10.1007/s11119-008-9103-z.), principal component analysis (Loughlin, 1991Loughlin, W. P. (1991). Principal component analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing, 57(9), 1163-1169.), optimum index factor (Fal et al., 2019Fal, S., Maanan, M., Baidder, L., and Rhinane, H. (2019). The contribution of Sentinel-2 satellite images for geological mapping in the south of Tafilalet basin (Eastern Anti-Atlas, Morocco).The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 75-82. 10.5194/isprs-archives-XLII-4-W12-75-2019.), decorrelation stretch (Kenea, 1997Kenea, N. H. (1997). Improved geological mapping using Landsat TM data, Southern Red Sea Hills, Sudan: PC and IHS decorrelation stretching. International Journal of Remote Sensing, 18(6), 1233-1244. 10.1080/014311697218386.), and log residual (Hook et al., 1992Hook, S. J., Gabell, A. R., Green, A. A., and Kealy, P. S. (1992). A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies. Remote Sensing of Environment, 42(2), 123-135. 10.1016/0034-4257(92)90096-3.), etc. In this study, the applicability of different image processing techniques including the band ratio, decorrelation stretch, principal components analysis, and minimum noise fraction, as well as false color composition ASTER RGB:468 was investigated for discrimination of different geological units from ASTER Level1T VNIR+SWIR data.

The effectiveness of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data for lithological mapping and discrimination of geological units was demonstrated in many studies during the recent decades (Hewson et al., 2005Hewson, R. D., Cudahy, T. J., Mizuhiko, S., Ueda, K., and Mauger, A. J. (2005). Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia. Remote Sensing of Environment, 99(1-2), 159-172. 10.1016/j.rse.2005.04.025.; Pournamdari et al., 2014Pour, A. B., and Hashim, M. (2015). Structural mapping using PALSAR data in the central gold belt, Peninsular Malaysia. Ore Geology Reviews, 64, 13-22. 10.1016/j.oregeorev.2014.06.011.; Abdelouhed et al., 2022Aboelkhair, H., & Watanabe, Y. (2011). Using remotely sensed multispectral ASTER data for mapping extensive basalt flow around Al Madinah area, Saudi Arabia. In First International Geomatics Symposium in Saudi Arabia, Geomatics Technologies in the City, GTC. 10.13140/RG.2.1.1756.8802.). The suitability of ASTER image for geological applications is mainly due to the spectral characteristics of the ASTER visible/near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) bands, consequently, possibilities to perform different image processing techniques for mapping geological units (Rokni et al., 2011Rokni, K., Marghany, M., Hashim, M., and Hazini, S. (2011, December). Performance evaluation of global and absolute DEMs generated from ASTER stereo imagery. In 2011 IEEE International RF & Microwave Conference (pp. 266-269). 10.1109/RFM.2011.6168745.; Hewson et al., 2017Hewson, R., Robson, D., Carlton, A., and Gilmore, P. (2017). Geological application of ASTER remote sensing within sparsely outcropping terrain, Central New South Wales, Australia. Cogent Geoscience, 3(1), 1319259. 10.1080/23312041.2017.1319259.; Rezaei et al., 2020Rezaei, A., Hassani, H., Moarefvand, P., and Golmohammadi, A. (2020). Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods. Geology, Ecology, and Landscapes, 4(1), 59-70. 10.1080/24749508.2019.1585657.).

Materials and Methods

Study area

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The test site is located in northeastern Iran in Semnan province. The region is surrounded by the mountains and foothills of the North Alborz Mountains which belongs to the Alp-Himalaya orogenic belt. The active morphodynamics in this region are mainly driven by aeolian and fluvial activities. The fluvial activities are prominent as slop deposits, alluvial domains and mega fans (Ullmann et al., 2016Ullmann, T., Büdel, C., Baumhauer, R., and Padashi, M. (2016). Sentinel-1 SAR data revealing fluvial morphodynamics in damghan (Iran): Amplitude and coherence change detection. International Journal of Earth Science and Geophysics, 2(1), 1-14.10.35840/2631-5033/1807.). This region is covered by concrete layers such as cretaceous formations, as well as sandstone and paleogene-related conglomerates (Arabameri et al., 2019Arabameri, A., Roy, J., Saha, S., Blaschke, T., Ghorbanzadeh, O., and Tien Bui, D. (2019). Application of probabilistic and machine learning models for groundwater potentiality mapping in Damghan sedimentary plain, Iran. Remote Sensing, 11(24), 1-35. 10.3390/rs11243015.). Figure 1 shows location of the study area.

Figure 1 Location of the study area 

Reference map

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A geological map scale 1:100000 was used as the reference to evaluate the effectiveness of the applied image processing techniques for discrimination of different geological units in the study area from ASTER imagery. This geological map is displayed in Figure 2.

Figure 2 Geological map of Damghan city (covering the study area) 

Results and Discussion

At first, an evaluation on false color composition (RGB: 468) of ASTER image was implemented. With reference to the geology map of the study area (Figure 2), our inspection indicated that some of the geological units such as Basalt, Tuff, Conglomerate, and Dolomite can be discriminated from the ASTER RGB: 468 color composition, as shown in Figures 3.

Figure 3 False color composition (RGB: 468) of ASTER image 

Subsequently, the band ratio, decorrelation stretch, principal components analysis, and minimum noise fraction techniques were applied to find out their applicability in highlighting different geological units from ASTER imagery. Several band rations that have previously been verified for geological purposes were examined and consequently the ASTER band ratios (4/1, 4/5, 4/6) was found more suitable for detailed lithological mapping in the study area. As displayed in Figure 4, the ASTER band ratios (4/1, 4/5, 4/6) clearly discriminated Dolomite, Tuff, Sandstone, Conglomerate, and specially Basalt from the ASTER data. Besides, the outcome of decorrelation stretch demonstrated greatly discrimination of Limestone, Basalt, Tuff, Conglomerate, and Dolomite from ASTER image (Figure 5). This technique was able to highlight all the geological units in the study area except for Sandstone.

Figure 4 Band ratios (4/1, 4/5, 4/6 as RGB) 

Figure 5 Decorrelation stretch output 

PCA technique also was applied on ASTER image (figure 6). Based on statistical analysis and also percentage of data variation, the results showed that the first PC involved the highest variance with positive loadings at each band. PC1 can extract information about lithological and mineralogical rock units through visual interpretations. The last two PCs include small variance with mostly noise. Therefore, the band combination PC1PC2PC3 that contains about 97.78 percent of total variance was selected. Our investigation revealed that the PCA technique is suitable to discriminate Tuff, Limestone, Basalt, Sandstone, Dolomite, and Conglomerate. However, some Basalt units that were adjacent to Sandstone were not differentiated by this technique.

Figure 6 Principal components analysis (PC1, PC2, PC3 as RGB) 

MNF technique was implemented to highlight lithological units from ASTER imagery, as illustrated in Figure 7. The result show that MNF approach is suitable to highlight Basalt, Tuff, Dolomite, and some areas containing Conglomerate, while it is difficult to discriminate Sandstone and Limestone using this technique.

Figure 7 Minimum noise fraction result 

Finally, the BRs-PCs method was performed on ASTER imagery of the study area and the output image was evaluated to assess the applicability of this method to highlight geological units. In doing so, the PCA technique was applied on the selected band ratios in this study (4/1, 4/5, 4/6 as RGB), and the obtained PCs (BRs-PC1, BRs-PC2, BRs-PC3 as RGB) were analyzed. The result demonstrated the effectiveness of BRs-PCs method for geological mapping. As displayed in Figure 8, this approach provided an enhanced output compared to methods PCA and BR alone, and clearly discriminated the available geological units in the study area.

Figure 8 BRs-PCs (BRs-PC1, BRs-PC2, BRs-PC3 as RGB) 

Overall, as presented in Table 2, the findings of this study indicate that discrimination of Conglomerate, Tuff, Dolomite, and Basalt units from ASTER image at the region of Alborz Mountain was easy, so that all the applied techniques were able to clearly extract these geological units. On the other hand, discrimination of Limestone and Sandstone was difficult and these geological units were not identified by some of the applied techniques. The ASTER band combination 468 that is well known for geological mapping, was also not able to discriminate Limestone and Sandstone.

The specified band ratios could not extract Limestone from ASTER image. In contrast, although PCA technique highlighted all the geological units in the study area, but some errors was appeared in extraction of Basalt using this technique. But BRs-PCs approach, because considered the advantages of both PCA and BR techniques, provided a superior output compared to PCA and BR methods alone, and also better result compared to other applied techniques in this study. The BRs-PCs approach successfully discriminated all the geological units from ASTER imagery.

The findings of this study were confirmed by the results of previous studies in different regions. In the study of Aali et al. (2022Aali, A. A., Shirazy, A., Shirazi, A., Pour, A. B., Hezarkhani, A., Maghsoudi, A., and Khakmardan, S. (2022). Fusion of remote sensing, magnetometric, and geological data to identify polymetallic mineral potential zones in Chakchak Region, Yazd, Iran.Remote Sensing, 14(23), 6018. 10.3390/rs14236018.), suitability of the PCA and BR techniques was demonstrated for detection of the geological units such as Sandstone, Dolomite, Conglomerate, Limestone and Marl in the Chakchak region, Yazd province, Iran. Pournamdari et al. (2014Pournamdari, M., Hashim, M., and Pour, A. B. (2014). Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, south Iran. Advances in Space Research, 54(4), 694-709. 10.1016/j.asr.2014.04.022.) also proved applicability of these techniques for lithological mapping of Soghan ophiolitic complex in south of Iran. Aboelkhair and Watanabe (2011Aboelkhair, H., & Watanabe, Y. (2011). Using remotely sensed multispectral ASTER data for mapping extensive basalt flow around Al Madinah area, Saudi Arabia. In First International Geomatics Symposium in Saudi Arabia, Geomatics Technologies in the City, GTC. 10.13140/RG.2.1.1756.8802.) sucsessgully performed the PCA and DS techniques for mapping Basalt flow in Madinah area, Saudi Arabia. Moreover, Sekandari et al. (2020Sekandari, M., Masoumi, I., Beiranvand Pour, A., M Muslim, A., Rahmani, O., Hashim, M., and Aminpour, S. M. (2020). Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 spectral imagery for exploration of carbonate-hosted Pb-Zn deposits in the Central Iranian Terrane (CIT). Remote Sensing, 12(8), 1239. 10.3390/rs12081239.) executed the PCA, MNF and BR techniques for the extraction of lithological units such as Dolomite, Limestone, Basalt, Sandstone, Conglomerate and Tuff in the central part of the Kashmar–Kerman tectonic zone, the Central Iranian Terrane (CIT). Similarly, in the study of Othman and Gloaguen (2017Othman, A. A., and Gloaguen, R. (2017). Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq. Journal of Asian Earth Sciences, 146, 90-102. 10.1016/j.jseaes.2017.05.005.), the applicability of PCA, BR and classification techniques was verified for mapping the geological units such as Conglomerate, Limestone, Tuff and reddish green Shales in the Bardi-Zard area in north-east Iraq, a part of the Zagros Fold – Thrust Belt. As shown in the above studies, the principal component analysis and band ratio were the most widely used techniques for geological mapping using satellite data.

Conclusion

In this study, the applicability of several image processing techniques, including the band ratio (BR), decorrelation stretch (DS), principal components analysis (PCA), minimum noise fraction (MNF), and the ASTER false color composition RGB: 468, was evaluated for the extraction of geological units from ASTER satellite imagery in southern margin of Alborz Mountain in Iran. In addition, a method based on Principal Components of Band Ratios (BRs-PCs) was proposed for the discrimination of geological units from ASTER imagery. The results indicated that the ASTER RGB:468 and MNF technique were not able to clearly highlight Limestone and Basalt from ASTER image, while the DS technique successfully discriminated all the geological units except for Sandstone. In addition, the results revealed that the specified band ratios could not extract Limestone from ASTER image. In contrast, PCA technique highlighted all the geological units in the study area, however a failure was appeared in discrimination of Basalt using this technique. Nonetheless, the BRs-PCs approach, because considered the advantages of both PCA and BR techniques, provided a superior output compared to these techniques alone, and successfully discriminated all the geological units from ASTER imagery. The study concluded that the BRs-PCs approach may be useful for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.