Identification of karst areas using Remote sensing and GIS method and its expansion in Zalem-Rud sub-basin of Sari in Mazandaran, Iran

Gholi Nataj Malekshah, Mohammad Ali; Jahani, Davood; Mousavi, Seyed Ramzan; Kohansal Ghadimvand, Nader; Vaziri, Seyed Hamid

doi:10.30495/girs.2022.684338

Manuscript ID : GIRS-2105-1911 (R1) Visit : 339 Page: 50 - 71

10.30495/girs.2022.684338

http://dorl.net/dor/20.1001.1.26767082.1401.13.3.4.6

Article Type: Original Research

Identification of karst areas using Remote sensing and GIS method and its expansion in Zalem-Rud sub-basin of Sari in Mazandaran, Iran

Subject Areas : Applications in water resources management

Mohammad Ali Gholi Nataj Malekshah ¹ , Davood Jahani ² , Seyed Ramzan Mousavi ³ , Nader Kohansal Ghadimvand ⁴ , Seyed Hamid Vaziri ⁵

1 - PhD. Student, Department of Geology, Faculty of Sciences, North Tehran Branch, Islamic Azad University, Iran
2 - Associate Professor, Department of Geology, Faculty of Sciences, North Tehran Branch, Islamic Azad University, Iran
3 - Assistant Prof. of Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural resources University, Iran
4 - Assistant Professor, Department of Geology, Faculty of Sciences, North Tehran Branch, Islamic Azad University, Iran
5 - Professor of Department of Geology, Faculty of Sciences, North Tehran Branch, Islamic Azad University, Iran

Received: 2021-05-07 Accepted : 2021-08-26 Published : 2022-09-23

Keywords: GIS, Karst, remote sensing, Hydro-geochemistry, Zalem-Rud sub-basin, Mazandaran, fuzzy logic,

Abstract :

Background and Objective Karst is a composite image of all the roughness, shapes, pores, and phenomena caused by water corrosion, above and below the surface, in various soluble geological formations, which cover about 15% of the world's exposed rocks. Despite the importance of karst areas in the past, today, the study, identification, spatial analysis and management of these areas are of very interest to geologists, hydrologists, hydrogeologists, eco-tourists, geotourists and environmentalists at various scales. Karst phenomenon also has a special place in various dimensions from the point of view of geology and geomorphology, because the causes and methods of dissolution processes and forms and their extension in rocks and minerals are of great importance to East Mazandaran, especially in the study area, is geologically one of the areas with karst process potential and has been less studied due to environmental conditions, especially vegetation and access roads. This study is focused on identifying karst areas and their extent using RS and GIS method in the Zalem-Rud Sari basin in Mazandaran province in northern Iran. In order to identify karst areas and physicochemical characteristics of existing aquifers, it was first necessary to identify karst areas and then assess their physicochemical status. In the first step, using Landsat, ASTER and SRTM satellite data, geological maps, weather information and field visits, factors affecting karstification such as rock types, Fracture lineaments, vegetation, climate, condition of the drainages and the slope of the topography are extracted. It should be noted that in this step, to obtain a better result, fuzzy logic and Hierarchical Analysis Process have been used. In the second step, in order to investigate the physicochemical condition of the aquifer, the chemical parameters of some springs are analyzed and the dissolution parameters, saturation indices, the origin of water-soluble components and the general flow system in the existing aquifers are determined.Materials and Methods In this study, the composition and distribution of rock, based on field visits and georeferenced geological maps of one hundred thousandths of Behshahr and Sari, have been done. Three methods of manual, automatic and semi-automatic have been used to extract the lineaments due to fractures. In the manual method, the lineaments are highlighted and extracted by applying High Pass filters, PCA and the color combination of Landsat 8 satellite data. In the automatic method, the Segment Tracing Algorithm (STA) in PCI software is used. In the STA algorithm, linear pixels are identified based on the degree of gray difference and then converted to vectors based on RADI, GTHR, LTHR, FTHR, ATHR and DTHR parameters. The Normalized Difference Vegetation Index (NDVI) was used to detect and extract vegetation status on OLI sensor data from the Landsat 8 satellite. To generate DEM, photogrammetric techniques were performed on a pair of stereo images of ASTER sensor in Idrisi software based on parametric variables of external and external orientations and ground control points (GCPs). The topographic slope of the area has been calculated by DEM and based on the degree in GIS and its map has been prepared. Based on the weather data of the General Meteorological Department of Mazandaran Province, the weather condition is determined by the isothermal, isohyetal and isoevaporation curves and the type of weather is determined by the De Marton method.Results and Discussion The result of the above activities has been the production of lithological maps, density and distance from fracture lineaments, density and distance from drainages, topographic slope, rainfall and vegetation. Due to the different criteria used in the generated maps and the need for a single, comparable and proportionate criteria for combining information layers, fuzzy logic has been used. In this case, all layers, except lithology, which has a definite boundary and is a function of Boolean logic, are fuzzy and then extracted as fuzzy layers in GIS. On the other hand, because the weight and effectiveness of the eight factors affecting the occurrence of the karst phenomenon are not the same, the Hierarchical Analysis (AHP) method has been used to determine the preference and prioritization of these factors. And the participation rate, in other words, the weight of each criterion with an incompatibility coefficient of less than one percent has been calculated in Expert Choice 12 software. Finally, by the weighted linear combination (WLC) method, fuzzy raster layers based on their effective weight are combined with Compromise Operator or Gamma and the karst distribution map is extracted with different probability coefficients in the study area. In the next step, in order to investigate the physicochemical status of the aquifers and evaluate the karst phenomenon, 18 springs with equal flow and more than three litres per second were selected and evaluated using SPPS, RockWorks2016 and PHREEQC2.6 software. In addition to extracting hydrogeochemical tables and graphs, the saturation index of various minerals has also been calculated.Conclusion The results of this study show that the karst phenomenon has the most spread in the central part and the lineaments resulting from fractures have played the most role in the formation of this phenomenon. The water type of these springs is calcium-magnesium bicarbonate and according to the Gibbs diagram, the role of aquifer rocks in determining the chemical composition of water is very clear. Also, the saturation indices of minerals and the type of flow in groundwater reservoirs are diffuse and diffuse-duct flows. To this research, the use of new remote sensing technology and GIS increases accuracy and speed and reduces costs in karst studies.

References:

Aganbati SA. 2004. Geology of Iran. Geological Survey of Iran. First edition. 640 p. (In Persian).

Alavi M. 1991. Sedimentary and structural characteristics of the Paleo-Tethys remnants in northeastern Iran. Geological Society of America Bulletin, 103(8): 983-992. doi:https://doi.org/10.1130/0016-7606(1991)103<0983:SASCOT>2.3.CO;2.

Alonso-Contes CA. 2011. Lineament mapping for groundwater exploration using remotely sensed imagery in a karst terrain: Rio Tanama and Rio de Arecibo basins in the northern karst of Puerto Rico. Michigan Technological University. doi:https://doi.org/10.37099/mtu.dc.etds/309.

Asadzadeh S, de Souza Filho CR. 2016. A review on spectral processing methods for geological remote sensing. International Journal of Applied Earth Observation and Geoinformation, 47: 69-90. doi:https://doi.org/10.1016/j.jag.2015.12.004.

Balıka F, Alkışa A, Alkış YKZ. 2004. Validation of radargrammetric DEM generation from radarsat images in high relief areas in Edremit region of Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(Part XXX).

Birk S, Liedl R, Sauter M. 2004. Identification of localised recharge and conduit flow by combined analysis of hydraulic and physico-chemical spring responses (Urenbrunnen, SW-Germany). Journal of Hydrology, 286(1): 179-193. doi:https://doi.org/10.1016/j.jhydrol.2003.09.007.

Bonham-Carter GF, Bonham-Carter G. 1994. Geographic information systems for geoscientists: modelling with GIS. vol 13. Elsevier. 398 p.

Dafny E, Tawfeeq KJ, Ghabraie K. 2015. Evaluating temporal changes in hydraulic conductivities near karst-terrain dams: Dokan Dam (Kurdistan-Iraq). Journal of Hydrology, 529: 265-275. doi:https://doi.org/10.1016/j.jhydrol.2015.07.048.

Daoxian Y. 1997. Sensitivity of karst process to environmental change along the PEP II transect. Quaternary International, 37: 105-113. doi:https://doi.org/10.1016/1040-6182(96)00012-2.

Demicco RV, Klir GJ. 2003. Fuzzy logic in geology. Elsevier. 347 p.

Drever JI. 1988. The geochemistry of natural waters, vol 437. Prentice hall Englewood Cliffs. 436 p.

Ebrahimi O, Ahmadi M, Shahabi H, Asgari S. 2018. Evaluation of karst features using principal component analysis (PCA): a case from Zarneh and Kergan, Western Iran. Carbonates and Evaporites, 33(4): 625-635. doi:https://doi.org/10.1007/s13146-017-0373-2.

Elez J, Cuezva S, Fernandez-Cortes A, Garcia-Anton E, Benavente D, Cañaveras JC, Sanchez-Moral S. 2013. A GIS-based methodology to quantitatively define an Adjacent Protected Area in a shallow karst cavity: The case of Altamira cave. Journal of Environmental Management, 118: 122-134. doi:https://doi.org/10.1016/j.jenvman.2013.01.020.

Gibbs RJ. 1970. Mechanisms controlling world water chemistry. Science, 170(3962): 1088-1090.

Ho P-G. 2009. Geoscience and remote sensing. BoD–Books on Demand, 610 p.

Kaufmann G, Romanov D. 2016. Structure and evolution of collapse sinkholes: Combined interpretation from physico-chemical modelling and geophysical field work. Journal of Hydrology, 540: 688-698. doi:https://doi.org/10.1016/j.jhydrol.2016.06.050.

Khanlari G, Momeni AA. 2012. Geomorphology, hydrogeology and the study of factors affecting to karst development in Garin area, west of Iran. Geography and Territorial Spatial Arrangement, 2(3): 61 -73. (In Persian).

Koike K, Nagano S, Ohmi M. 1995. Lineament analysis of satellite images using a Segment Tracing Algorithm (STA). Computers & Geosciences, 21(9): 1091-1104. doi:https://doi.org/10.1016/0098-3004(95)00042-7.

Kresic N. 1995. Remote sensing of tectonic fabric controlling groundwater flow in Dinaric karst. Remote Sensing of Environment, 53(2): 85-90. doi:https://doi.org/10.1016/0034-4257(95)00042-Y.

Litwin L, Andreychouk V. 2008. Characteristics of high-mountain karst based on GIS and Remote Sensing. Environmental Geology, 54(5): 979-994. doi:https://doi.org/10.1007/s00254-007-0893-5.

Liu F, Song X-f, Yang L, Zhang Y, Han D, Ma Y, Bu H. 2015. Identifying the origin and geochemical evolution of groundwater using hydrochemistry and stable isotopes in the Subei Lake basin, Ordos energy base, Northwestern China. Hydrology and Earth System Sciences, 19(1): 551-565. doi:https://doi.org/10.5194/hess-19-551-2015.

Malczewski J. 2000. On the use of weighted linear combination method in GIS: common and best practice approaches. Transactions in GIS, 4(1): 5-22. doi:https://doi.org/10.1111/1467-9671.00035.

Meijerink AM, Bannert D, Batelaan O, Lubczynski M, Pointet T. 2007. Remote sensing applications to groundwater, vol 16. Unesco Paris, 312 p.

Mohammadi Z, Alijani F, Rangzan K. 2014. DEFLOGIC: a method for assessment of groundwater potential in karst terrains: Gurpi Anticline, southwest Iran. Arabian Journal of Geosciences, 7(9): 3639-3655. doi:https://doi.org/10.1007/s12517-013-0958-6.

Mohammadizad R, Arfania R. 2017. Advanced investigation of remote sensing to geological mapping of Zefreh region in central Iran. Open Journal of Geology, 7(10): 1509. doi:https://doi.org/10.4236/ojg.2017.710101.

O’Driscoll MA, DeWalle DR. 2006. Stream–air temperature relations to classify stream–ground water interactions in a karst setting, central Pennsylvania, USA. Journal of Hydrology, 329(1): 140-153. doi:https://doi.org/10.1016/j.jhydrol.2006.02.010.

Pei J, Wang L, Huang N, Geng J, Cao J, Niu Z. 2018. Analysis of Landsat-8 OLI imagery for estimating exposed bedrock fractions in typical karst regions of Southwest China using a karst bare-rock index. Remote Sensing, 10(9): 1321. doi:https://doi.org/10.3390/rs10091321.

Pirasteh S. 2006. The role of lineaments in karstification-Pabdeh anticline Zagros Fold Belt: an application of remote sensing and geographic information system. Geographical Journal of Territory (Sarzamin), 3(11): 51-68. (In Persian).

Saaty TL. 2001. Fundamentals of the analytic hierarchy process. In: The analytic hierarchy process in natural resource and environmental decision making. Springer, pp 15-35.

Shahmoradi S, Ghafarian Malamiri HR, Amini M. 2021. Extraction of soil moisture index (TVDI) using a scatter diagram temperature/vegetation and MODIS images. Journal of RS and GIS for Natural Resources, 12(1): 38-62. doi:http://dorl.net/dor/20.1001.1.26767082.1400.12.1.3.4. (In Persian).

Shuster ET, White WB. 1971. Seasonal fluctuations in the chemistry of lime-stone springs: A possible means for characterizing carbonate aquifers. Journal of Hydrology, 14(2): 93-128. doi:https://doi.org/10.1016/0022-1694(71)90001-1.

Srivastava PK, Mukherjee S, Gupta M, Islam T. 2014. Remote sensing applications in environmental research. Springer. doi:https://doi.org/10.1007/978-3-319-05906-8.

Su YH, Zhu GF, Feng Q, Li ZZ, Zhang FP. 2009. Environmental isotopic and hydrochemical study of groundwater in the Ejina Basin, northwest China. Environmental Geology, 58(3): 601-614. doi:https://doi.org/10.1007/s00254-008-1534-3.

Uromeihy A. 2000. The Lar Dam; an example of infrastructural development in a geologically active karstic region. Journal of Asian Earth Sciences, 18(1): 25-31. doi:https://doi.org/10.1016/S1367-9120(99)00026-7.

Xing L, Guo H, Zhan Y. 2013. Groundwater hydrochemical characteristics and processes along flow paths in the North China Plain. Journal of Asian Earth Sciences, 70-71: 250-264. doi:https://doi.org/10.1016/j.jseaes.2013.03.017.

Yu L, Porwal A, Holden E-J, Dentith MC. 2012. Towards automatic lithological classification from remote sensing data using support vector machines. Computers & Geosciences, 45: 229-239. doi:https://doi.org/10.1016/j.cageo.2011.11.019.

_||_