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Monitoring Post-Mining Reclamation Success in Jharia Coalfield Using Geospatial Technology

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Abstract

The widespread usage of coal for power generation necessitates continuing mining. While producing a valuable resource, this method significantly degrades the natural environment, notably the local vegetation. Once mining has stopped, reclaiming the destroyed areas to restore the natural landscape is critical. Mining activities have been going on for centuries; however, monitoring reclaimed areas through field-based methods is inefficient and time-consuming. In contrast, the expanding accessibility of geospatial data over the last five decades has aided in the accurate and consistent mapping and monitoring of reclaimed mining zones. Keeping in line, the present study utilized Landsat TM/OLI data from 2005 to 2021 to track reclamation success in a part of Jharia Coalfield, India. The methods included deriving Normalized Difference Vegetation Index (NDVI) images to evaluate the spatiotemporal variation of vegetation health, density, and vigour; and visual appreciation using a variety of RGB combinations of three date NDVI images. Later, a statistical threshold method based on Z-scores was employed to quantify the NDVI change values into three categories- Decrease, Unchanged and Increase. According to these analyses, the reclamation success in the study area ranged from modest to good. In the two focus areas, there has been an increase of 72 and 76 hectares in the Increase class. The accuracy of the classified change image was calculated to be 84.4 per cent. Until recently, no such work has been reported from the study area. The present research results are critical to mining professionals, environmentalists, and society and provide a promising way to inform about the success of reclamation activities and their monitoring.

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References

  • Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120.

    Article  Google Scholar 

  • Bodlák, L., Krováková, K., Nedbal, V., & Pechar, L. (2012). Assessment of landscape functionality changes as one aspect of reclamation quality–The case of Velká podkruˇsnohorská dump Czech Republic. Ecological Engineering, 43, 19–25.

    Article  Google Scholar 

  • Brown, J. (2018). NDVI, the Foundation for remote sensing phenology, retrieved August 28, 2021, from https://www.usgs.gov/core-science-systems/eros/phenology/science/ndvi-foundation-remote-sensing-phenology?qt-science_center_objects=0#qt-science_center_objects

  • Buczyńska, A., Blachowski, J., & Bugajska-Jędraszek, N. (2023). Analysis of post-mining vegetation development using remote sensing and spatial regression approach: A case study of former babina mine (Western Poland). Remote Sensing, 15, 719.

    Article  Google Scholar 

  • Cai, B. F. (2008). Monitoring and evaluating of major forestry ecological project based on remote sensing–A case study of three north shelter forest project.

  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903.

    Article  Google Scholar 

  • CIL (2021). Coal india limited (CIL), Retrieved August 27, 2021, from https://www.coalindia.in/about-us/

  • CMPDI (2020). Land restoration / reclamation monitoring of 52 opencast coal mines projects of CIL producing more than 5 Mcm (Coal+OB) annually based on satellite data for the year 2019, Ranchi: Remote Sensing Cell, Geomatics Division, CMPDI, retrieved August 25, 2021, from http://archive.coalindia.in/DesktopModules/DocumentList/documents/Sat.%20Monitoring%20of%20Reclamation%20more%20than%205%20MCM%20for%202019-20.pdf

  • Coal Mines Regulations (2011). Retrieved August 25, 2021, from http://www.dgms.net/Notified%20Draft%20Coal%20Mines%20Regulation%202011.pdf.

  • Congalton, R. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Digavinti, J., & Manikiam, B. (2021). Satellite monitoring of forest fire impact and regeneration using NDVI and LST. Journal of Applied Remote Sensing, 15(4), 042412. https://doi.org/10.1117/1.JRS.15.042412

    Article  Google Scholar 

  • DiGirolamo, P. (2006). A comparison of change detection methods in an urban environment using LANDSAT TM and ETM satellite imagery: A multi-temporal, multi-spectral analysis of Gwinnett County, GA 1991–2000, Retrieved August 13, 2021, from https://core.ac.uk/download/pdf/71421207.pdf

  • Erener, A. (2011). Remote sensing of vegetation health for reclaimed areas of seyitömer open cast coal mine. International Journal of Coal Geology, 86(1), 20–26.

    Article  CAS  Google Scholar 

  • Fiehn, A., Kostinek, J., Eckl, M., Klausner, T., Gałkowski, M., Chen, J., Gerbig, C., Röckmann, T., Maazallahi, H., Schmidt, M., Korbeń, P., Neçki, J., Jagoda, P., Wildmann, N., Mallaun, C., Bun, R., Nickl, A.-L., Jöckel, P., Fix, A., & Roiger, A. (2020). Estimating CH4, CO2 and CO emissions from coal mining and industrial activities in the upper silesian coal basin using an aircraft-based mass balance approach. Atmospheric Chemistry and Physics, 20(21), 12675–12695.

    Article  CAS  Google Scholar 

  • Gielisch, H. & Kropp, C. (2017). Coal fires a major source of greenhouse gases- a forgotten Problem. Environmental Risk Assessment and Remediation, [e-journal] 02(01, Retrieved August 25, 2021, from http://www.alliedacademies.org/articles/coal-fires-a-major-source-of-greenhouse-gases-a-forgotten-problem-8871.html

  • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537–2562.

    Article  Google Scholar 

  • Gupta, K., & Chatterjee, S. (2021). Assessment of mining derelict land using multicriteria decision-making technique and approaches toward sustainable regeneration—a case study in Jamuria CD block, West Bengal (India). In G. S. Bhuniya, U. Chatterjee, A. Kashyap, & P. K. Shit (Eds.), Land Reclamation and restoration strategies for sustainable development geospatial technology based approach (Vol. 10, pp. 173–190). Elsevier.

    Chapter  Google Scholar 

  • Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32, 1–6.

    Article  Google Scholar 

  • Huete, A. R., Liu, H. Q., Batchily, K., & van Leeuwen, W. (1997). A Comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440–451.

    Article  Google Scholar 

  • Kariyawasam, N., Raval, S. & Shamsoddini, A. (2014). Incorporating remote sensing as a tool to assist rehabilitation monitoring in a dolomite mining operation in South Australia. 6966, Congress, Engaging the Challenges-Enhancing the Relevance, 16–21 June, Kuala Lampur, Malaysia, 1–13.

  • Kuenzer, C., Zhang, J., Li, J., Voigt, S., Mehl, H., & Wagner, W. (2007). Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction. International Journal of Remote Sensing, 28(20), 4561–4585.

    Article  Google Scholar 

  • Lévesque, J., & Staenz, K. (2008). Monitoring mine tailings revegetation using multitemporal hyperspectral image data. Canadian Journal of Remote Sensing, 34, S172–S186.

    Article  Google Scholar 

  • Li, J., Fu, P., Mao, Y., Saini, V., & Sokol, E. (2020). A parametric study on the inception and evolution of underground coal fires based on a lab-scale experimental setup. Fire Technology, 56, 1039–1057.

    Article  Google Scholar 

  • Li, J., Yang, Y., Li, J., Mao, Y., Saini, V., & Kokh, S. (2022). A TGA–DSC-based study on macroscopic behaviors of coal–oxygen reactions in context of underground coal fires. Journal of Thermal Analysis and Calorimetry, 147, 3185–3194.

    Article  CAS  Google Scholar 

  • Maiti, K. K., Bandyopadhyay, J., Chakravarty, D., Das, S., & Mondal, S. (2019). Geospatial analysis for the assessment of mine land reclamation area: A case study of Noamundi Block. Jharkhand, Spatial Information Research, 27(6), 681–693.

    Article  Google Scholar 

  • McKenna, P. B., Lechner, A. M., Phinn, S., & Erskine, P. D. (2020). Remote sensing of mine site rehabilitation for ecological outcomes: A global systematic review. Remote Sensing, 12(21), 3535.

    Article  Google Scholar 

  • Mróz, M., & Sobieraj, A. (2004). Comparison of several vegetation indices calculated on the basis of a seasonal SPOT XS time series, and their suitability for land cover and agricultural crop identification. Technical Sciences, 7, 39–66.

    Google Scholar 

  • Nelson, R. F. (1983). Detecting forest canopy change due to insect activity using landsat MSS. Photogrammetric Engineering and Remote Sensing, 49(9), 1303–1314.

    Google Scholar 

  • Otsu, N. (1979). A threshold selection method from grey-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66.

    Article  Google Scholar 

  • Payero, J. O., Neale, C. M. U., & Wright, J. L. (2004). Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass. Applied Engineering in Agriculture, 20(3), 385–393.

    Article  Google Scholar 

  • Pohl, C. (1999). Tools and methods for fusion of images of different spatial resolution. International Archives of Photogrammetry and Remote Sensing, 32, 76–82.

    Google Scholar 

  • PPCA (2017). Powering past coal alliance declaration, retrieved August 27, 2021, from https://www.gov.uk/government/publications/powering-past-coal-alliance-declaration

  • Purevdorj, T. S., Tateishi, R., Ishiyama, T., & Honda, Y. (1998). Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19(18), 3519–3535.

    Article  Google Scholar 

  • Rouse, J., Haas, R. H., Schell, J. A. & Deering, D. (1973). Monitoring vegetation systems in the Great Plains with ERTS, Vol. 1, Section A, 3d ERTS-1 symposium, 1973, NASA. Goddard space flight center, pp 309–317.

  • Sader, S. A., & Winne, J. C. (1992). RGB-NDVI colour composites for visualizing forest change dynamics. International Journal of Remote Sensing, 13(16), 3055–3067.

    Article  Google Scholar 

  • Saini, V., Gupta, R. P., & Arora, M. K. (2015). Spatio-temporal pattern of eco-environmental parameters in Jharia coal -field, India. In: Michel U, Schulz K, Ehlers M, Nikolakopoulos KG, Civco D, editors. Proceedings of SPIE 9644 earth resources and environmental remote sensing/GIS applications VI, 96441H Sep 21–24; Toulouse, France

  • Saini, V. & Tiwari, R. K. (2017). Effect of Urbanization on Land Surface Temperature and NDVI: A case study of dehradun, India, 38th Asian conference on remote sensing, 23–27 October, New Delhi, India, 1–10.

  • Saini, V., Gupta, R. P., & Arora, M. K. (2016). Environmental impact studies in Coalfields in India: A case study from Jharia Coal-Field. Renewable and Sustainable Energy Reviews, 53, 1222–1239.

    Article  Google Scholar 

  • Saini, V., Gupta, R. P., & Arora, M. K. (2019). Environmental monitoring in the Jharia Coalfield, India: Vegetation indices and surface temperature measurements. In G. B. Stracher (Ed.), Coal and peat fires: A global perspective volume 5: case studies–advances in field and laboratory research (Vol. 5, pp. 359–385). Elsevier.

    Chapter  Google Scholar 

  • Saini, V., Li, J., Yang, Y., & Li, J. (2023). Evaluating environmental quality in Rujigou coalfield, China, using analytic hierarchy process. Environmental Science and Pollution Research, 30, 1841–1853.

    Article  CAS  Google Scholar 

  • Saini, V., Li, J., Yang, Y., Li, J., Wang, B., & Tan, J. (2022). Investigating the environmental impacts of coal mining using remote sensing and in situ measurements in Ruqigou Coalfield, China. Environmental Monitoring and Assessment, 194, 780.

    Article  Google Scholar 

  • Singh, N., Gupta, V. & Singh, A. (2011). Geospatial technology for land reclamation monitoring of opencast coal mines in India. In Proceedings of the 34th International Symposium on Remote Sensing of Environment. Sydney, Australia, pp. 1–4, 10−15 April

  • Singh, A. (1986). Change detection in the tropical forest environment of northeastern India using Landsat. In M. J. Eden & J. T. Parry (Eds.), Remote sensing and tropical land management (pp. 237–254). John Wiley and Sons.

    Google Scholar 

  • Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.

    Article  Google Scholar 

  • Straker, J., Blazecka, M., Sharman, K., Woelk, S., Boorman, S., & Kuschminder, J. (2004). Use of remote sensing in reclamation assessment at Teck Cominco’s Bullmoose mine site. British Columbia Mine Reclamation Symposium, 10, 0042453.

    Google Scholar 

  • Thakur, T. K., Dutta, J., Bijalwan, A., & Swamy, S. L. (2022b). Evaluation of decadal land degradation dynamics in old coal mine areas of central India. Land Degradation & Development, 33, 3209–3230.

    Article  Google Scholar 

  • Thakur, T. K., Dutta, J., Upadhyay, P., Patel, D. K., Thakur, A., Kumar, M., & Kumar, A. (2022a). Assessment of land degradation and restoration in coal mines of central India: A time series analysis. Ecological Engineering, 175, 106493.

    Article  Google Scholar 

  • Townshend, J. R. G., & Justice, C. O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 11, 1435–1445.

    Article  Google Scholar 

  • USGS (2021). Using the USGS landsat level-1 data product. Retrieved August 13, 2021, from: https://www.usgs.gov/core-science-systems/nli/landsat/using-usgs-landsat-level-1-data-product

  • van Dijk, P., Zhang, J., Jun, W., Kuenzer, C., & Wolf, K.-H. (2011). Assessment of the contribution of in-situ combustion of coal to greenhouse gas emission based on a comparison of Chinese mining information to previous remote sensing estimates. International Journal of Coal Geology, 86(1), 108–119.

    Article  Google Scholar 

  • Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115(12), 3468–3478.

    Article  Google Scholar 

  • Wang, S., Guo, J., Yu, Y., Shi, P., & Zhang, H. (2023). Quality evaluation of land reclamation in mining area based on remote sensing. International Journal of Coal Science & Technology, 10, 43.

    Article  Google Scholar 

  • Wężyk, P., Szostak, M., Krzaklewski, W., Pająk, M., Pierzchalski, M., Szwed, P., Hawryło, P., & Ratajczak, M. (2015). Landscape monitoring of post-industrial areas using LiDAR and GIS technology. Geodesy and Cartography, 64(1), 125–137.

    Article  Google Scholar 

  • Zhang, G., Dong, J., Xiao, X., Hu, Z., & Sheldon, S. (2012). Effectiveness of ecological restoration projects in Horqin sandy Land China based on SPOT-VGT NDVI data. Ecological Engineering, 38(1), 20–29.

    Article  Google Scholar 

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Acknowledgements

The author thanks the USGS (http://earthexplorer.usgs.gov/) for providing the open-source Landsat data used in this work. Due acknowledgement is provided to the anonymous reviewers and the associate editor whose comments significantly improved the manuscript.

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  1. Department of Civil Engineering, Indian Institute of Technology Ropar, Nangal Road, Rupnagar, Punjab, 140001, India

    Varinder Saini

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  1. Varinder Saini
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Saini, V. Monitoring Post-Mining Reclamation Success in Jharia Coalfield Using Geospatial Technology. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01877-3

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