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Quantitative assessment of landscape transformation due to coal mining activity using earth observation satellite data in Jharsuguda coal mining region, Odisha, India

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Abstract

Opencast (coal) mining activities significantly affect the society and environment in several aspects, including land-useland-cover (LULC) alteration. The present study aims to quantify the alteration in LULC patterns in every 4 year from 2006 to 2018 in the Jharsuguda coal mining region in Odisha, India. The study has used the multitemporal Landsat series satellite data for LULC classification. A support vector machine algorithm was designed for LULC classifications into five broad classes, viz. water body, mining area, forest/vegetation area, bare land, and built-up area. The key findings of the study indicated that the coverage of mining area was gradually increased from 2006 to 2018 with an annual change rate of + 0.03%. On the other hand, a significant loss in the forest cover/vegetation was observed with the annual change rate of − 0.04% from 2006 to 2018. The remarkable increment in the coverage of bare land area was also noted during the study period. The mining activity has posed a serious threat to the forest resources over the study region. Hence, a proper management policy for mine reclamation should be practised over the Jharsuguda coal mining region to protect the forest, environment, and society.

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  1. United States Geological Survey (USGS) earth explorer (http://www.earthexplorer.usgs.gov).

References

  • Areendran, G., Rao, P., Raj, K., Mazumdar, S., & Puri, K. (2013). Land use/land cover change dynamics analysis in mining areas of Singrauli district in Madhya Pradesh, India. Tropical Ecology, 54(2), 239–250.

    Google Scholar 

  • Basommi, P. L., Guan, Q., & Cheng, D. (2015). Exploring land use and land cover change in the mining areas of Wa East District, Ghana using satellite imagery. Open Geosciences, 1, 618–626. https://doi.org/10.1515/geo-2015-0058.

    Article  Google Scholar 

  • Black life: Impact of coal mining in Jharsuguda. (2014). Article by India water portal. Retrieved May 12, 2020, from https://www.indiawaterportal.org/articles/black-life-impact-coal-mining-jharsuguda.

  • Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Sciences, 18, 251–259. https://doi.org/10.1016/j.ejrs.2015.07.003.

    Article  Google Scholar 

  • Chen, L., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE, 13(7), e0200493. https://doi.org/10.1371/journal.pone.0200493.

    Article  CAS  Google Scholar 

  • Coal in India. (2019). Retrieved May 10, 2020, from https://www.industry.gov.au/sites/default/files/2019-08/coal-in-india-2019-report.pdf.

  • Coal Resources of India. (2004). Coal wing, geological survey of India, Kolkata. Retrieved May 10, 2020, from https://www.portal.gsi.gov.in/gsiDoc/pub/IndiaCoalResources2004.pdf.

  • Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data, principles and practices. Boca Raton, London, New York: Lewis Publishers.

    Google Scholar 

  • Demirel, N., Düzgün, Ş., & Emil, M. K. (2011). Landuse change detection in a surface coal mine area using multi-temporal high-resolution satellite images. International Journal of Mining, Reclamation and Environment, 25(4), 342–349. https://doi.org/10.1080/17480930.2011.608889.

    Article  Google Scholar 

  • Dhar, B. B., Jamal, A., & Ratan, S. (1991). Air pollution problems in an Indian opencast coal mining complex: A case study. International Journal of Surface Mining, Reclamation and Environment, 5(2), 83–88. https://doi.org/10.1080/09208119108944290.

    Article  Google Scholar 

  • Forkuor, G., Ullmann, T., & Griesbeck, M. (2020). Mapping and monitoring small-scale mining activities in Ghana using sentinel-1 time series (2015–2019). Remote Sensing, 12(6), 911. https://doi.org/10.3390/rs12060911.

    Article  Google Scholar 

  • Garai, D., & Narayana, A. C. (2018). Land use/land cover changes in the mining area of Godavari coal fields of southern India. The Egyptian Journal of Remote Sensing and Space Sciences, 21, 375–381. https://doi.org/10.1016/j.ejrs.2018.01.002.

    Article  Google Scholar 

  • Goparaju, L., Prasad, P. R. C., & Ahmad, F. (2017). Geospatial technology perspectives for mining vis-a-vis sustainable forest ecosystems. Present Environment and Sustainable Development, 11, 219–238. https://doi.org/10.1515/pesd-2017-0020.

    Article  Google Scholar 

  • Isidro, C. M., McIntyre, N., Lechner, A. M., & Callow, I. (2017). Applicability of earth observation for identifying small-scale mining footprints in a wet tropical region. Remote Sensing, 9(9), 945. https://doi.org/10.3390/rs9090945.

    Article  Google Scholar 

  • Ivanciuc, O. (2007). Applications of support vector machines in chemistry. In K. B. Lipkowitz & T. R. Cundari (Eds.), Reviews in computational chemistry (Vol. 23, pp. 291–400). Weinheim: Wiley.

    Chapter  Google Scholar 

  • Kumar, P., Gupta, D. K., Mishra, V. N., & Prasad, R. (2015). Comparison of support vector machine, artificial neural network and spectral angle mapper algorithms for crop classification using LISS IV data. International Journal of Remote Sensing, 36(6), 1604–1617.

    Article  Google Scholar 

  • Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and image interpretation (7th ed.). Hoboken: Wiley.

    Google Scholar 

  • MCL (Mahanadi Coalfield Limited). (2020). A miniratna subsidiary company of coal India limited. Retrieved January 7, 2020, from https://www.mahanadicoal.in/About/eproduction.php.

  • Mishra, N. (2015). A report on Coal mining, displacement and rural livelihoods: A study in Mahanadi Coalfield, Odisha. Retrieved January 7, 2020, from https://niti.gov.in/sites/default/files/2019-01/Report%20on%20Coal%20Mining%2C%20Displacement%20and%20Rural%20Livelihoods%20A%20Study%20in%20Mahanadi%20Coalfield%20Odisha.pdf.

  • Mishra, P. K., Rai, A., & Rai, S. C. (2019). Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. The Egyptian Journal of Remote Sensing and Space Sciences. https://doi.org/10.1016/j.ejrs.2019.02.001.

    Article  Google Scholar 

  • Patel, A. K., Chatterjee, S., & Gorai, A. K. (2017). Development of machine vision-based ore classification model using support vector machine (SVM) algorithm. Arabian Journal of Geosciences, 10(5), 107. https://doi.org/10.1007/s12517-017-2909-0.

    Article  Google Scholar 

  • Patel, A. K., Chatterjee, S., & Gorai, A. K. (2018). Development of an expert system for iron ore classification. Arabian Journal of Geosciences, 11(15), 401.

    Article  Google Scholar 

  • Prakash, A., & Gupta, R. P. (1998). Land-use mapping and change detection in a coal mining area—A case study in the Jharia coalfield, India. International Journal of Remote Sensing, 19(3), 391–410. https://doi.org/10.1080/014311698216053.

    Article  Google Scholar 

  • Puyravaud, J. P. (2003). Standardizing the calculation of the annual rate of deforestation. Forest Ecology and Management, 177, 593–596. https://doi.org/10.1016/S0378-1127(02)00335-3.

    Article  Google Scholar 

  • Qian, T., Bagan, H., Kinoshita, T., & Yamagata, Y. (2014). Spatial-temporal analyses of surface coal mining dominated land degradation in Holingol, Inner Mongolia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5), 1675–1687. https://doi.org/10.1109/JSTARS.2014.2301152.

    Article  Google Scholar 

  • Ranjan, A. K., Anand, A., Vallisree, S., & Singh, K. R. (2016). LU/LC change detection and forest degradation analysis in Dalma Wildlife Sanctuary using 3S technology: A case study in Jamshedpur-India. AIMS Geosciences, 2(4), 273–285. https://doi.org/10.3934/geosci.2016.4.273.

    Article  Google Scholar 

  • Ranjan, A. K., & Kanga, S. (2018). Dynamic changes in mangrove forest and Lu/Lc variation analysis over Indian Sundarban Delta in West Bengal (India) using multi-temporal satellite data. i-manager’s Journal on Future Engineering and Technology, 13(3), 9–23. https://doi.org/10.26634/jfet.13.3.14226.

    Article  Google Scholar 

  • Ranjan, A. K., & Parida, B. R. (2019). Paddy acreage mapping and yield estimation using sentinel-based optical and SAR sensors data in Sahibganj district, Jharkhand (India). Spatial Information Research, 27, 399. https://doi.org/10.1007/s41324-019-00246-4.

    Article  Google Scholar 

  • Ranjan, A. K., Vallisree, S., Verma, S. K., Murmu, L., & Kumar, P. B. S. (2017). Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990–2016 using geospatial technology. International Journal of Geomatics and Geoscience, 7(3), 275–292.

    Google Scholar 

  • Sekhar, P. H., & Mohan, S. K. (2014). Assessment of impact of opencast mine on surrounding forest: A case study from Keonjhar district of Odisha, India. Journal of Environmental Research and Development, 9, 249–254.

    Google Scholar 

  • Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Singh, N. P., Mukherjee, T. K., & Shrivastava, B. B. P. (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. Journal of the Indian Society of Remote Sensing, 25(1), 61–72.

    Article  Google Scholar 

  • Telmer, K., & Stapper, D. (2007). Evaluating and monitoring small scale gold mining and mercury use: building a knowledge-base with satellite imagery and field work. Final Report, UNIDO Project EG/GLO/01/G34, University of Victoria: Victoria, BC, Canada, 2007. Retrieved May 12, 2020, from https://iwlearn.net/resolveuid/5efd7f292f962736362ee5b8cd798bf7.

  • Thakkar, A. K., Desai, V. R., Patel, A., & Potdar, M. B. (2017). Post-classification corrections in improving the classification of land use/land cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India. The Egyptian Journal of Remote Sensing and Space Science. https://doi.org/10.1016/j.ejrs.2016.11.006.

    Article  Google Scholar 

  • Turner, B. L., II, Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences USA, 104(52), 20666–207671.

    Article  CAS  Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  • Xia, N., Cheng, L., & Li, M. C. (2019). Mapping urban areas using a combination of remote sensing and geolocation data. Remote Sensing, 11, 1470. https://doi.org/10.3390/rs11121470.

    Article  Google Scholar 

  • Yuan, F., Sawaya, K. E., Loeffelhoz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2–3), 317–328.

    Article  Google Scholar 

  • Zeng, T., & Wang, C. (2016). SVM-based land use/cover classification in Shihezi area. Progress in Electromagnetic Research Symposium (PIERS). https://doi.org/10.1109/PIERS.2016.7734875.

    Article  Google Scholar 

  • Zhang, Q. L., Schaaf, C., & Seto, K. C. (2013). The vegetation adjusted NTL urban index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment, 129, 32–41.

    Article  Google Scholar 

  • Zhang, X. Y., & Li, P. J. (2018). A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis. Journal of Photogrammetry and Remote Sensing, 135, 93–111.

    Article  Google Scholar 

  • Zhou, L., & Yang, X. (2008). Use of neural networks for land cover classification from remotely sensed imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 575–578.

    Google Scholar 

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Acknowledgements

We thank the anonymous reviewer and the editor for giving constructive comments. The authors sincerely acknowledge the United State Geological Survey (USGS) earth explorer for providing Landsat series satellite data free of cost.

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  1. Department of Mining Engineering, National Institute of Technology, Rourkela, OD, 769008, India

    Avinash Kumar Ranjan, Debasmita Sahoo & A. K. Gorai

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  1. Avinash Kumar Ranjan
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  2. Debasmita Sahoo
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  3. A. K. Gorai
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Correspondence to A. K. Gorai.

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Ranjan, A.K., Sahoo, D. & Gorai, A.K. Quantitative assessment of landscape transformation due to coal mining activity using earth observation satellite data in Jharsuguda coal mining region, Odisha, India. Environ Dev Sustain 23, 4484–4499 (2021). https://doi.org/10.1007/s10668-020-00784-0

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