Application of remote sensing and GIS analysis for identifying groundwater potential zone in parts of Kodaikanal Taluk, South India

doi:10.1007/s11707-012-0347-6

Front Earth Sci

0, Vol.

Issue () : 65-75 https://doi.org/10.1007/s11707-012-0347-6

RESEARCH ARTICLE

Application of remote sensing and GIS analysis for identifying groundwater potential zone in parts of Kodaikanal Taluk, South India

Murugesan BAGYARAJ¹, Thirunavukkarasu RAMKUMAR¹(

), Senapathi VENKATRAMANAN¹(

), Balasubramanian GURUGNANAM²

1. Department of Earth Sciences, Annamalai University, Annamalai Nagar 608002, India; 2. Department of Geology, Gandhigram Rural Institute, Deemed University, Gandhigarm 624302, India

Download: PDF(969 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Groundwater potential zones were demarcated with the help of remote sensing and Geographic Information System (GIS) techniques. The study area is composed rocks of Archaean age and charnockite dominated over others. The parameters considered for identifying the groundwater potential zone of geology slope, drainage density, geomorphic units and lineament density were generated using the resource sat (IRS P6 LISS IV MX) data and survey of India (SOI) toposheets of scale 1:50000 and integrated them with an inverse distance weighted (IDW) model based on GIS data to identify the groundwater potential of the study area. Suitable weightage factors were assigned for each category of these parameters. For the various geomorphic units, weightage factors were assigned based on their capability to store ground-water. This procedure was repeated for all the other layers and resultant layers were reclassified. The reclassified layers were then combined to demarcate zones as very good, good, moderate, low, and poor. This groundwater potentiality information could be used for effective identification of suitable locations for extraction of potable water for rural populations.

Keywords remote sensing GIS slope geomorphic lineament Kodaikanal

Corresponding Author(s): RAMKUMAR Thirunavukkarasu,Email:tratrj@gmail.com; VENKATRAMANAN Senapathi,Email:venkatramanansenapathi@gmail.com

Issue Date: 05 March 2013

Cite this article:

Murugesan BAGYARAJ,Thirunavukkarasu RAMKUMAR,Senapathi VENKATRAMANAN, et al. Application of remote sensing and GIS analysis for identifying groundwater potential zone in parts of Kodaikanal Taluk, South India[J]. Front Earth Sci, 0, (): 65-75.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0347-6
https://academic.hep.com.cn/fesci/EN/Y0/V/I/65

Fig.1 Flow chart of the present study

Fig.2 Geology of the study area

Fig.3 Spatial distribution map of geomorphological features of the study area

Fig.4 Spatial distribution map of slope

Fig.5 Map showing lineament trend of the study area

Fig.6 Spatial distribution map of lineament density

Fig.7 Map showing drainage map of the study area

Fig.8 Spatial distribution map of drainage density

Tab.1 Thematic map rank and weightages

Tab.2 Groundwater potential zones of the study area

Fig.9 Groundwater potential zone map of the study area

1	Anithamary I, Ramkumar T, Venkatramanan S (2012). Application of statistical analysis for the hydrogeochemistry of saline groundwater in Kodiakarai, Tamilnadu. Journal of Coastal Research , 28 (1A): 89–98
2	Asadi S S, Vuppala P, Reddy M A (2007). Remote sensing and GIS techniques for evaluation of groundwater quality in municipal corporation of Hyderabad (Zone-V), India. Int J Environ Res Public Health , 4(1): 45–52 doi: 10.3390/ijerph2007010008 pmid:17431315
3	Bahuguna IM, Nayak S, Tamilarasan V, Moses J (2003). Groundwater prospective zones in basaltic terrain using remote sensing. J Indian Soc Remote Sens , 31(2), 101–105
4	Butler M, Wallace J, Lowe M (2002). Ground-water quality classification using GIS contouring methods for Cedar Valley, Iron County, Utah. In: Proceedings of Digital Mapping Techniques, 2002 Workshop, US Geological Survey Open-File Report 02–370
5	Central Groundwater Board (CGWB) (2008). District Groundwater Brochure, Kodikanal, Dindigul district, Tamilnadu. Technical Report 1–23
6	Chi K H, Lee B J (1994). Extracting potential groundwater area using remotely sensed data and GIS techniques. In: Proceedings of the Regional Seminar on Integrated Applications of Remote Sensing and GIS for Land and Water Resources Management, Bangkok . Bangkok: Economic and Social Commission for Asia and the Pacific, 64–69
7	Dawoud M A, Darwish M M, El-Kady M M (2005). GIS-based groundwater management model for western Nile delta. Water Resour Manage , 19(5): 585–604 doi: 10.1007/s11269-005-5603-z
8	El-Hames A S, Al-Ahmadi M, Al-Amri N (2011). A GIS approach for the assessment of groundwater quality in Wadi Rabigh aquifer, Saudi Arabia, Environ. Earth Sci , 63(6): 1319–1331 doi: 10.1007/s12665-010-0803-0
9	Hrkal Z (1992). Acidification of groundwater in the Bohemian Massif. Nor Geol Unders Bull , 422 (7): 97–102
10	Hrkal Z (2001). Vulnerability of groundwater to acid deposition, Jizerske Mountains, northern Czech Republic: construction and reliability of a GIS-based vulnerability map. Hydrogeol J, 9(4): 348–357 doi: 10.1007/s100400100141
11	Jaiswal R K, Mukherjee S, Krishnamurthy J, Saxena R (2003). Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development—an approach. Int J Remote Sens , 24(5): 993–1008 doi: 10.1080/01431160210144543
12	Karanth K R (1987). Ground Water Assessment Development and Management. New York: McGraw-Hill
13	Krishnamurthy J, Venkatesa Kumar N, Jayaraman V, Manivel M (1996). An approach to demarcate groundwater potential zones through remote sensing and a GIS. Int J Remote Sens , 17(10): 1867–1884 doi: 10.1080/01431169608948744
14	Leblanc M, Favreau G, Tweed S, Leduc C, Razack M, Mofor L (2007). Remote sensing for groundwater modeling in large semiarid areas: Lake Chad basin, Africa. Hydrogeol J, 15(1): 97–100 doi: 10.1007/s10040-006-0126-0
15	Lokesha N, Gopalakrishna G S, Honne Gowda H,Gupta A K (2005). Delineation of groundwater potential zones in a hard rock terrain of Mysore district, Kamatak using IRS data and GIS techniques. J Indian Soc Remote Sens , 33(3): 405–412
16	Magesh N S, Chandrasekar N, Soundranayagam J P (2012). Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF Techniques. Geoscience Frontiers , 3(1): 189–196
17	Mathes E S, Rasmussen T C (2006). Combining multivariate statistical analysis with geographic information systems mapping: a tool for delineating groundwater contamination. Hydrogeol J , 14(8): 1493–1507 doi: 10.1007/s10040-006-0041-4
18	Mohammed A, Balasubramanian A, Kondoh A, Rokhmatuloh R, Mustafa A J (2003). Hydrogeomorphological mapping using remote sensing techniques for water resource management around palaeochannels, geoscience and remote sensing symposium, IGARSS–APOS; 03. In: Proceedings of the 2003 IEEE International, Toulouse, France
19	Münch Z, Conrad J (2007). Remote sensing and GIS based determination of groundwater dependent ecosystems in the Western Cape, South Africa. Hydrogeol J, 15(1): 19–28 doi: 10.1007/s10040-006-0125-1
20	Pius A, Jerome C, Sharma N (2012). Evaluation of groundwater quality in and around Peenya industrial area of Bangalore, South India using GIS techniques. Environ Monit Assess , 184 (7): 4067–4077 doi: 10.1007/s10661-011-2244
21	Prasad R K, Mondal N C, Banerjee P, Nandakumar M V, Singh V S (2008). Deciphering potential groundwater zone in hard rock through the application of GIS. Environ Geol , 55(3): 467–475 doi: 10.1007/s00254-007-0992-3
22	Rai B, Tiwari A, Dubey V S (2005). Identification of groundwater prospective zones by using remote sensing and geoelectrical methods in Jarla and Raniganj coalfields, Dhanbad district, Jharkhand state. J of Earth System Sciences , 114(5): 515–522 doi: 10.1007/BF02702027
23	Ramkumar T, Venkatramanan S, Anithamary I, Syed Ibrahim S M (2011). Evaluation of hydrogeochemical parameters and quality assessment of the groundwater in Kottur blocks, Tiruvarur district, Tamilnadu, India. ArabJ Geosciences , doi: 10.1007/s12517-011-0327-2
24	Rekha V B, Thomas A P, Suma M, Vijith H (2011). An integration of spatial information technology for groundwater potential and quality investigations in Koduvan ár, sub-watershed of Meenachil River basin, Kerala, India. J Indian Soc Remote Sens , 39(1): 63–71 doi: 10.1007/s12524-010-0050-6
25	Rokade V M, Kundal P, Joshi A K (2007). Groundwater potential modeling through remote sensing and GIS: a case study from Rajura Taluka, Chandrapur district, Maharashtra. J Geol Soc London , 69: 943–948
26	Saha D, Dhar Y R, Vittal S S, (2010). Delineation of groundwater development potential zones in parts of marginal Ganga alluvial plain in south Bihar, eastern India. EnvironMonitor , 165(1–4): 179–191 doi: 10.1007/s10661- 009–0937–2
27	Sankar K (2002). Evaluation of groundwater potential zones using remote sensing data in upper Vaigai River basin, Tamil Nadu, India. J Indian Soc Remote Sens , 30(3): 119–129 doi: 10.1007/BF02990644
28	Skidmore A K, Bijer W, Schmidt K, Kumar L (1997). Use of remote sensing and GIS for sustainable land management. ITC Journal , 3: 302–315
29	Skubon B A (2005). Groundwater quality and GIS investigation of a shallow sand aquifer, Oak opening region, Northwest Ohio. Geol Soc Am Abstr Programs , 7: 37–94
30	Solomon S, Quiel F (2006). Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeol J, 14(6): 1029–1041 doi: 10.1007/s10040-006-0096-2
31	Srinivasa R Y, Jugran K D (2003). Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensing and GIS. Hydrol Sci J , 48(5): 821–833 doi: 10.1623/hysj.48.5.821.51452
32	Subramani T, Elango L, Damodarasamy S R (2005). Groundwater quality and its suitability for drinking and agricultural use in Chithar River basin, Tamil Nadu, IndiaEnviron Geol , 47(8): 1099–1110 doi: 10.1007/s00254-005-1243-0
33	Sujatha D, Rajeswara Reddy B (2003). Quality characterization of groundwater in the south-eastern part of the Ranga Reddy district, Andhra Pradesh, India. J Environ Geology , 44(5): 579–586 doi: 10.1007/s00254-003-0794-1
34	Tjandra F L, Kondhoh A, Mohammed M A (2003). A conceptual database design for hydrology using GIS. In: Proceedings of Asia Pacific Association of Hydrology and Water Resources, Kyoto
35	Vasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Gopinath S, Sarma S (2011). Groundwater potential zoning in Thirumani- muttar sub-basin Tamilnadu, India—a GIS and remote sensing approach, geo-spatial. Inf Sci , 14(1): 17–26
36	Venkatramanan S, Ramkumar T, Anithamary I (2012). A statistical approach on hydrogeochemistry of groundwater in Muthupet coastal region, Tamilnadu, India, Carpathian. Journal of Earth and Environmental Sciences , 7(1): 47–54
37	Vijith H (2007). Groundwater potential in the hard rock terrain of western Ghats: a case study from Kottayam district, Kerala using resourcesat (Irs-P6) data and GIS techniques. J Indian Soc Remote Sens , 35(2): 163–171 doi: 10.1007/BF02990780
38	Vittala S, Govindaiah S, Gowda H H (2005). Evaluation of groundwater potential zones in the sub-watersheds of North Pennar River basin around Pavagada, Karnataka, India, using remote sensing and GIS techniques. J Indian Soc Remote Sens , 33(4): 483–493 doi: 10.1007/BF02990733
39	Yammani S (2007). Groundwater quality suitable zones identification: application of GIS, Chittoor area, Andhra Pradesh, India. Env Geol , 53(1): 201–210 doi: 10.1007/s00254-006-0634-1

[1]	Chao WANG, Zhiyuan LI, Xiong XU, Xiangsui ZENG, Jia LI, Huan XIE, Yanmin JIN, Xiaohua TONG. Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval[J]. Front. Earth Sci., 2023, 17(2): 378-390.
[2]	Boling YIN, Dongjie GUAN, Yuxiang ZHANG, He XIAO, Lidan CHENG, Jiameng CAO, Xiangyuan SU. How to accurately extract large-scale urban land? Establishment of an improved fully convolutional neural network model[J]. Front. Earth Sci., 2022, 16(4): 1061-1076.
[3]	Herrieth MACHIWA, Bo TIAN, Dhritiraj SENGUPTA, Qian CHEN, Michael MEADOWS, Yunxuan ZHOU. Vegetation dynamics in response to human and climatic factors in the Tanzanian Coast[J]. Front. Earth Sci., 2021, 15(3): 595-605.
[4]	Sijun ZHENG, Chen MENG, Jianhui XUE, Yongbo WU, Jing LIANG, Liang XIN, Lang ZHANG. UAV-based spatial pattern of three-dimensional green volume and its influencing factors in Lingang New City in Shanghai, China[J]. Front. Earth Sci., 2021, 15(3): 543-552.
[5]	Ziyang DAI, Fayuan LI, Mingwei ZHAO, Lanhua LUO, Haoyang JIAO. Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China[J]. Front. Earth Sci., 2021, 15(1): 94-105.
[6]	Conghui LI, Lili LIN, Zhenbang HAO, Christopher J. POST, Zhanghao CHEN, Jian LIU, Kunyong YU. Developing a USLE cover and management factor (C) for forested regions of southern China[J]. Front. Earth Sci., 2020, 14(3): 660-672.
[7]	Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[8]	Evangelin Ramani SUJATHA. A spatial model for the assessment of debris flow susceptibility along the Kodaikkanal-Palani traffic corridor[J]. Front. Earth Sci., 2020, 14(2): 326-343.
[9]	Xia LEI, Jiayi PAN, Adam DEVLIN. An ultraviolet to visible scheme to estimate chromophoric dissolved organic matter absorption in a Case-2 water from remote sensing reflectance[J]. Front. Earth Sci., 2020, 14(2): 384-400.
[10]	Jianhong LIU, Clement ATZBERGER, Xin HUANG, Kejian SHEN, Yongmei LIU, Lei WANG. *Modeling grass yields in Qinghai Province, China, based on MODIS NDVI* data—an empirical comparison**[J]. Front. Earth Sci., 2020, 14(2): 413-429.
[11]	Shifa MA, Feng LIU, Chunlei MA, Xuemin OUYANG. Integrating logistic regression with ant colony optimization for smart urban growth modelling[J]. Front. Earth Sci., 2020, 14(1): 77-89.
[12]	Tong LI, Huadong GUO, Li ZHANG, Chenwei NIE, Jingjuan LIAO, Guang LIU. Simulation of Moon-based Earth observation optical image processing methods for global change study[J]. Front. Earth Sci., 2020, 14(1): 236-250.
[13]	Mohammad Saeid MIRAKHORLO, Majid RAHIMZADEGAN. Evaluating estimated sediment delivery by Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Distributed (SEDD) in the Talar Watershed, Iran[J]. Front. Earth Sci., 2020, 14(1): 50-62.
[14]	Nan CHEN. Scale problem: Influence of grid spacing of digital elevation model on computed slope and shielded extra-terrestrial solar radiation[J]. Front. Earth Sci., 2020, 14(1): 171-187.
[15]	Pingzhi FANG, Deqian ZHENG, Liang LI, Wenyong MA, Shengming TANG. Numerical and experimental study of the aerodynamic characteristics around two-dimensional terrain with different slope angles[J]. Front. Earth Sci., 2019, 13(4): 705-720.

Viewed

Full text

Abstract

Cited

Shared

Discussed