Landuse Land Cover Study with Different Geospatial Indices of Korba Coalfield Region, Chhattisgarh, India

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IJEP 42(12): 1445-1455 : Vol. 42 Issue. 12 (December 2022)

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Vijayendra Pratap Dheeraj, C.S. Singh* and Ashwani Kumar Sonkar

Indian Institute of Technology (Banaras Hindu University), Department of Mining Engineering, Varanasi – 221 005, Uttar pradesh, India

Abstract

This present study has been made carried out for the analysis of landuse land cover (LULC) changes in Korba Coalfield, Chhattisgarh, from last 19 years of data (that is 2002-2021). Remote sensing and GIS datasets were adopted to analyse spatial-temporal changes. LULC classes were classified mainly into barren land, built-up area, cropland, forest area, mining area and water bodies. The maximum likelihood method of supervised classification (ArcGIS software) was adopted to classify selected images into suitable LULC classes. The changes in land cover are detected on 5–7 year time interval using satellite data of Landsat 4-5 TM, Landsat-8 OLI and TIRS with different geo-spatial indices, like normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference soil index (NDSI). The observations show that cropland and forest area indicate maximum degradation and decreased with net change by 58.57 km2 in 2002 and 82.54 km2 in 2021(-16.03 km2) and 40.69 km2 in 2002 and 32.24 km2 in 2021 (-8.45 km2) whereas built-up area and mining area have increased with net change by 29.38 km2 in 2002 and 39.64 km2 in 2021 (+10.26 km2) and 28.48 km2 in 2002 and 38.81 km2 in 2021 (+8.33 km2), respectively. Apart from this, barren land and water bodies were also increased with net change by 22.86 km2 in 2002 and 26.53 km2 in 2021 (+3.67 km2) and 6.59 km2 in 2002 and 8.81 km2 in 2021 (+2.22 km2). Net change, percentage change and rate of change in land cover of different classes were also calculated. Therefore, these used indices are very reliable for mapping as well as monitoring different land cover changes over a large extent in mining areas.

Keywords

Landuse/land cover, Change rate calculation, Geo-spatial indices, Korba coalfield region, Chhattisgarh

References

  1. Mwitwa, J., et al. 2012. Governance and sustai-nability challenges in landscapes shaped by mining: mining-forestry linkages and impacts in the copper belt of Zambia and the DR Congo. For. Pol. Econ., 25:19–30.
  2. Abd El-Hamid, H.T., W. Caiyong and Z. Yongting. 2019. Geospatial analysis of landuse driving force in coal mining area: case study in Ningdong, China. Geo. J., 86(1):1-16.
  3. Samal, D.R. and S.S. Gedam. 2015. Monitoring land-use changes associated with urbanization: An object-based image analysis approach. European J. Rem. Sens., 48 (1):85–99.
  4. Orimoloye, I.R. and O.O. Ololade. 2020a. Potential implications of gold-mining activities on some environmental components: a global assessment (1990 to 2018). J. King Saud Univ. Sci., 32 (4): 2432–2438.
  5. Orimoloye, I.R. and O.O. Ololade. 2020b. Spatial evaluation of landuse dynamics in gold mining area using remote sensing and GIS technology. Int. J. Env. Sci. Tech., 17:4465–4480. doi.:10.1007/s13762-020-02789-8.
  6. Rudke, A.P., et al. 2020. Impact of mining activities on areas of environmental protection in the southwest of the Amazon: a GIS-and remote sensing-based assessment. J. Env. Manage., 263: 110392.
  7. Hemba, S., et al. 2017. Analysis of the physical growth and expansion of Makurdi town using remote sensing and GIS techniques. Imperial J. Interdiscip. Res., 3: 821–827.
  8. Prakash, A. and R.P. Gupta. 1998. Landuse mapping and change detection in a coal mining area – A case study in the Jharia coalfield, India. Int. J. Remote Sens., 3:391–410.
  9. Turner, M.G. and C.L. Ruscher. 2004. Change in landscape patterns in Georgia USA. Landscape Ecol. 1 (4):251–421.
  10. Singh, A. 2007. Revegetation of coal-mine spoils using Prosopis julifora in Singrauli coalfield is a harmful practice from an ecological viewpoint. Curr. Sci., 93 (9):1204.
  11. Manna, A. and R. Maiti. 2014. Opencast coal mining induced defaced topography of Raniganj coalfield in india- Remote sensing and GIS based analysis. J. Indian Soc. Remote Sens., 42 (4):755–764.
  12. Akiwumi, F.A. and D.R. Butler. 2008. Mining and environmental change in Sierra Leone, West Africa: a remote sensing and hydrogeomorphological study. Env. Monitor. Assess., 142:309–318.
  13. Khan, I. and A. Javed. 2012. Spatio-temporal land cover dynamics in opencast coal mine area of Sing-rauli, M.P., India. J. Geogr. Inform. Syst., 4: 521-529.
  14. Bajocco, S., et al. 2012. The impact of landuse/land cover changes on land degradation dynamics: a mediterranean case study. Env. Manage., 49 (5): 980-989.
  15. Tadesse, L., et al. 2017. Landuse and land cover changes and soil erosion in Yezat watershed, Northwestern Ethiopia. Int. Soil Water Conserv. Res., 5:85–94.
  16. Singh, N.P., T.K. Mukherjee and B.B.P. Shrivastava. 1997. Monitoring the impact of coal mining and thermal power industry on landuse pattern in and around Singrauli coalfield using remote sensing data and GIS. J. Indian Soc. Remote Sens., 25 (1):61–72.
  17. Dhar, B.B., A. Jamal and S. Ratan. 1991. Air pollution problems in an Indian opencast coal mining complex: A case study. Int. J. Surf. Min. Reclam. Env., 5 (2):83–88.
  18. Mal´eki, H., et al. 2017. Earth Obs geoinformation mapping and estimating land change between 2001 and 2013 in a heterogeneous landscape in West Africa: loss of forestlands and capacity building opportunities. Int. J. Appl., 63:15–23.
  19. Garai, D., and A. C. Narayana. 2018. Landuse/land cover changes in the mining area of Godavari coal fields of Southern India. Egyptian J. Remote Sens. Space Sci., 21(3):375-381.
  20. Leprieur, C., et al. 2000. Monitoring vegetation cover across semi-arid regions: Comparison of remote observations from various scales. Int. J. Rem. Sens., 21 (2):281–300.
  21. Jensen, R.R. and P.J. Hardin. 2005. Estimating urban leaf area using field measurements and satellite remote sensing data. J. Arboric., 31 (1):21–27.
  22. Chuvieco, E., M.P. Martin and A. Palacios. 2010. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Rem. Sens., 23:5103–5110.
  23. Panda, S.S., D.P. Ames and S. Panigrahi. 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Rem. Sens., 2 (3):673–696.
  24. Kolios, S. and C.D. Stylios. 2013. Identification of land cover/landuse changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data. Appl. Geogr., 40:150–160.
  25. Klemas, V.V. 2016. Remote sensing of submerged aquatic vegetation. Coast. Res. Library. 13:125–140.
  26. Ololade, O.O. and H.J. Annegarn. 2015. Dynamics of landuse/cover changes and landscape fragmentation analysis in Rustenburg area, South Africa. African J. Econ. Sustain. Develop., 4 (3):234.
  27. Li, P., L. Jiang and Z. Feng. 2013. Cross-comparison of vegetation indices derived from Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) sensors. Rem. Sens., 6 (1): 310–329.
  28. Zaitunah, A., A.A. Samsuri and R.A. Safitri. 2018. Normalized difference vegetation index (ndvi) analysis for land cover types using Landsat-8 OLI in Besitang watershed, Indonesia. IOP Conference Series: Earth Env. Sci., 126: 1-9.
  29. Orimoloye, I.R., et al. 2019. Wetland shift monitoring using remote sensing and GIS techniques: landscape dynamics and its implications on Isimangaliso wetland park, South Africa. Earth Sci. India. 12 (4):553–563.
  30. Rasul, A., et al. 2018. Applying built-up and bare-soil indices from Landsat-8 to cities in dry climates. Land, 7 (3):81.
  31. Snapir, B., D.M. Simms and T.W. Waine. 2017. Mapping the expansion of Galamsey Gold mines in the cocoa growing area of Ghana using optical remote sensing. Int. J. Appl. Earth Obs. Geoinf., 58: 225–233. doi: 10.1016/j.jag.2017.02.009.
  32. Provisional Coal Statistics. 2015-16. Available at: http://www.coalcontroller.gov.in/writereaddata/files/Provisional%20Coal%20Statistics%202015-16.pdf.
  33. Sachdev, R.K. 2007. Clean coal technologies – present scenario in India. Clean coal day : advanced clean coal technology International Symposium. Japan.
  34. Bocco, G., M. Mendoza and A. Velazquez. 2001. Remote sensing and GIS-based regional geomorphological mapping- a tool for landuse planning in developing countries. Geomorphol., 39: 211–219.
  35. Laskar, A. 2003. Integrating GIS and multicriteria decision making techniques for land resource planning. Master’s Thesis. International Institute for Geo-information Science and Earth Observation, Enschede, Netherlands.
  36. Turner II, B.L., E.F. Lambin and A. Reenberg. 2007. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. U.S.A. 104 (52):20666–207671.
  37. Yuan, F., et al. 2005. Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sens. Env., 98 (2-3):317-328.
  38. El Gammal, E.A., S.M. Salem and A.E.A. El Gammal. 2010. Change detection studies on the world’s biggest artificial lake (Lake Nasser, Egypt). Egypt. J. Remote Sens. Space Sci., 13, 89–99.
  39. Meshesha, T.W., S.K. Tripathi and D. Khare. 2016. Analyses of landuse and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa watershed northern-central Highland of Ethiopia. Model. Earth Syst. Env., 2: 168.
  40. Rawat, J.S. and M. Kumar. 2015. Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt. J. Remote Sens. Space Sci., 18:77–84.
  41. Ma, L., et al. 2017. A review of supervised object based land cover image classification. ISPRS J. Photogramm. Remote Sens., 130:277–293.
  42. Toren, T. and E. Unal. 2001. Assessment of open pit coal mining impacts using remote sensing: A case study from Turkey. 17th international Mining Congress and Exhibition of Turkey, 2001.
  43. Yan, G., et al. 2006. Comparison of pixel-based and object-oriented image classification approaches : a case study in a coal fire area, Wuda, Inner Mongolia China. Int. J. Remote Sens., 27(18): 4039-4055.
  44. Boakey, E., et al. 2008. Landsat images for assessment of the impact of landuse and land cover changes on the barekese catchment in Ghana. European J. Sci. Res., 21 (4):617–626.
  45. Chitade, Z.A. and S.K. Katyar. 2010. Impact analysis of opencast coal mines on landuse/land cover using remote sensing and GIS technique: a case study. Int. J. Eng. Tech., 2 (12):7171–7176.
  46. Omo-Irabor, O.O. 2016. A comparative study of image classification algorithms for landscape assessment of the Niger Delta region. J. Geogr. Inform. Syst., 8:163–170.
  47. Rao, K.N. and K. Narendra. 2006. Mapping and evaluation of urban sprawling in the Mehadrigedda watershed in Visakhapatnam metropolitan region using remote sensing and GIS. Curr. Sci., 91 (11): 1552–1557.
  48. Madasa, A., I. R. Orimoloye and O.O. Ololade. 2021. Application of geospatial indices for mapping land cover/use change detection in a mining area. J. African Earth Sci., 175:104108.
  49. Rao, C.S. 1983. Coal resources of Madhya Pradesh, Jammu and Kashmir. Geological Survey of India. Bulletin series A, no.45, vol III.
  50. Singh, R., et al. 2017. Hydrogeochemical assessment of surface and groundwater resources of Korba coalfield, Central India: environmental implications. Arabian J. Geosci., 10(14):1-16.
  51. Singh, A. K., N. P. Varma and G.C. Mondal. 2016. Hydrogeochemical investigation and quality assessment of mine water resources in the Korba coalfield, India. Arabian J. Geosci., 9(4):278.
  52. Erbek, F.S., C. Ozkan and M. Taberner. 2004. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int. J. Rem. Sens., 25 (9):1733–1748.
  53. Ozbakir, A. and A. Bannari. 2008. Performance of TDVI in urban landuse/cover classification for quality of place measurement. The International Archives of the Photogrammetry, remote sensing and spatial information sciences. Proceedings, pp 691–694.
  54. Rouse, J.W., et al. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 symposium (vol I: Technical presentations). NASA SP-351. National Aeronautics and Space Administration, Washington, District of Columbia. pp 309–317.
  55. McFeeters, S.K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens., 17 (7):1425-1432.
  56. Rogers, A.S. and M.S. Kearney. 2004. Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices. Int. J. Remote Sens., 25(12):2317–2335.
  57. Joshi, P.K., et al. 2006. Assessing areas deforested by coal mining activities through satellite remote sensing images and GIS in parts of Korba, Chattisgarh. J. Indian Soc. Remote Sens., 34 (4): 415–421.
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