Mine void identification using Object-based Image Analysis (OBIA) of satellite imagery Sentinel 2 data

Authors

  • Leta Lestari Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology
  • Ginting Jalu Kusuma Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology http://orcid.org/0000-0001-5929-6940
  • Abie Badhurahman Center of Research Excellence (CoRE) in Mine Closure and Mining Environment, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology http://orcid.org/0000-0003-3260-9817
  • Sendy Dwiki Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology http://orcid.org/0000-0002-2603-7894
  • Rudy Sayoga Gautama Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology http://orcid.org/0000-0001-9108-4200

DOI:

https://doi.org/10.15243/jdmlm.2023.102.4129

Keywords:

land cover, mine void, OBIA, shape, WIUP

Abstract

Open pit mining is an extensively-used method in Indonesian coal mining. This method is characterized by the formation of mine void at the end of life-of-mine (LOM) due to insufficient material to backfill the mine-out areas. Mine voids are legally accepted as one of mine closure options and categorized as “Reklamasi Bentuk Lain†- a miscellaneous reclamation option (Decree of Minister of Energy and Mineral Resources/KepMen ESDM No.1827, 2018), However, unmanageable voids will exert negative impacts. The identification and mapping of mine voids spatially are imperative to give stakeholders ample information to construct viable mine voids management and benefit all stakeholders. In this research, Sentinel 2 satellite image data is used for land monitoring so that void can be mapped based on land cover classification. The land cover classification was carried out based on the Object-based Image Analysis (OBIA) method. This method has a good level of accuracy, ranging from 86.1 to 96.4%. Based on the land cover classification, potential voids are analyzed based on their shape, where potential voids have elongation values of 0.2-1.0 and circularity of 0.1-0.8. In addition, potential voids are analyzed based on the location where they are found (referred to as the Mining License Area/WIUP data). In 2018 there were 40 potential voids inside WIUP and 5 potential voids outside WIUP, while in 2020, 62 potential voids inside WIUP and 8 potential voids outside WIUP were identified in the study area. The final result of potential mine void, i.e. mine void-1 could not further be distinguished between mine sumps, voids, or mine ponds without additional data and analysis. On the other hand, mine void-2 could not be further assigned as natural water bodies or mine void from illegal activities. Subsequent studies using more elaborated data, processes, and analysis are important, to enhance the accuracy of void mapping using satellite images.

Author Biographies

Leta Lestari, Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology

Graduate Student of Mining Engineering

Institut Teknologi Bandung

Ginting Jalu Kusuma, Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology

Lecture in Mining Engineering

Institut Teknologi Bandung

Abie Badhurahman, Center of Research Excellence (CoRE) in Mine Closure and Mining Environment, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology

Research Assistant in Mining Engineering

Institut Teknologi Bandung

Sendy Dwiki, Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology

Lecture in Mining Engineering

Institut Teknologi Bandung

Rudy Sayoga Gautama, Department of Mining Engineering, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology

Lecture in Mining Engineering

Institut Teknologi Bandung

References

Alberti, M. 2008. Advances in urban ecology: Integrating humans and ecological processes in urban ecosystems. In Advances in Urban Ecology: Integrating Humans and Ecological Processes in Urban Ecosystems. Springer US, doi:10.1007/978-0-387-75510-6.

Arifin, S. and Kartika, T. 2021. Monitoring model of land cover change for the indication of devegetation and revegetation using Sentinel-2. International Journal of Remote Sensing and Earth Sciences 17(2):163, doi:10.30536/j.ijreses.2020.v17.a3385.

Awaliyan, R. and Sulistyoadi, Y.B. 2018. Land cover classification on Sentinel-2A satellite imagery using tree algorithm methods. ULIN: Jurnal Hutan Tropis 2(2), doi:10.32522/ujht.v2i2.1363 (in Indonesian).

Ayuhati, K. 2018. Revealing the Potential of Metallurgical Coal, Coal with High Selling Prices. Ministry of Energy and Mineral Resources, Indonesia (in Indonesian).

Baatz, M. and Schape, A. 2000. Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T. and Griesebner, G. (Eds). Angewandte Geographische Informations Verarbeitung, Wichmann-Verlag, Heidelberg, 12-23.

Baboo, D.S. and Devi, M. 2010. An analysis of different resampling methods in Coimbatore, District. Global Journal of Computer Science and Technology 10(15):61-66.

Blaschke, T. 2010. Object based image analysis for remote sensing. In ISPRS Journal of Photogrammetry and Remote Sensing 65(1):2-16, doi:org/10.1016/j.isprsjprs.2009.06.004

BP. 2020. Statistical Review of World Energy, 2020, 69th Edition.

Bürck, S. 2019. Remote sensing analyses for open-pit mine area computation. A comparative study on the implementation of multi-spectral classifications and crowdsourcing to compute the spatial extent of four open-pit mines in Indonesia, Australia, Canada and Brazil. Master Thesis, Faculty of Chemistry and Earth Sciences, Heidelberg University, Germany.

Burnett, C. and Blaschke, T. 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling 168(3):233-249, doi:10.1016/S0304-3800(03)00139-X.

Candra, D.S., Phinn, S. and Scarth, P. 2016. Cloud and cloud shadow masking using multi-temporal cloud masking algorithm in tropical environments. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2:95-100, doi:10.5194/isprs-archives-xli-b2-95-2016.

Castillejo-González, I.L., López-Granados, F., García-Ferrer, A., Peña-Barragán, J. M., Jurado-Expósito, M., de la Orden, M. S. and González-Audicana, M. 2009. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Computers and Electronics in Agriculture 68(2):207-215, doi:10.1016/j.compag.2009.06.004.

Coluzzi, R., Imbrenda, V., Lanfredi, M. and Simoniello, T. 2018. A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses. Remote Sensing of Environment 217:426–443, doi:10.1016/j.rse.2018.08.009.

Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1):35-46, doi:10.1016/0034-4257(91)90048-B.

Cote, M. and Saeedi, P. 2014. Hierarchical image segmentation using a combined geometrical and feature-based approach. Journal of Data Analysis and Information Processing 02(04):117-136, doi:10.4236/jdaip.2014.24014.

Csillik, O. and Belgiu, M. 2017. Cropland mapping from Sentinel-2 time series data using object-based image analysis. AGILE Conference on Geographic Information Science 2017, Wageningen, The Netherlands.

Defries, R.S. and Townshend, J.R. 1994. NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing 15(17):3567-3586, doi:10.1080/01431169408954345.

Directorate General of Mineral and Coal. 2017. Trend projection of coal reserves in Indonesia. Directorate General of Mineral and Coal. Jakarta.

Drusch, M., del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. and Bargellini, P. 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 120:25-36, doi:10.1016/j.rse.2011.11.026.

Espindola, G.M., Camara, G., Reis, I.A., Bins, L.S. and Monteiro, A.M. 2006. Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing 27(14):3035-3040, doi:10.1080/01431160600617194.

European Space Agency. 2015. Sentinel-2 user handbook. Standard Document, Issue 1 Rev 2.

Foody, G.M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80(1):185-201, doi:10.1016/S0034-4257(01)00295-4.

Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S.,… Fernandez, V. 2017. Copernicus Sentinel-2A calibration and products validation status. Remote Sensing 9(6):584, doi:10.3390/rs9060584.

Gašparović, M. and Jogun, T. 2018. The effect of fusing Sentinel-2 bands on land-cover classification. International Journal of Remote Sensing 39(3):822-841, doi:10.1080/01431161.2017.1392640.

Gómez-Chova, L., Amorós-López, J., Mateo-García, G., Muñoz-Marí, J. and Camps-Valls, G. 2017. Cloud masking and removal in remote sensing image time series. Journal of Applied Remote Sensing 11(1):15005, doi:10.1117/1.JRS.11.015005.

Hagolle, O., Huc, M., Pascual, D.V. and Dedieu, G. 2010. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment 114(8):1747-1755, doi:10.1016/j.rse.2010.03.002.

Heilman, G.E., Strittholt, J.R., Slosser, N.C. and Dellasala, D.A. 2002. Forest fragmentation of the conterminous united states: Assessing forest intactness through road density and spatial characteristics. BioScience 52(5):411-422, doi:10.1641/0006-3568(2002)052 [0411:FFOTCU]2.0.CO;2.

Heryadi, Y. and Miranda, E. 2020. Land cover classification based on sentinel-2 satellite imagery using convolutional neural network model: A case study in Semarang Area, Indonesia. Studies in Computational Intelligence 830:191-206, doi:10.1007/978-3-030-14132-5_15.

Hilker, T. 2017. Surface reflectance/bidirectional reflectance distribution function. Comprehensive Remote Sensing 1-9:2-8, doi:10.1016/B978-0-12-409548-9.10347-1.

Hollstein, A., Segl, K., Guanter, L., Brell, M. and Enesco, M. 2016. Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images. Remote Sensing 8(8), doi:10.3390/rs8080666.

Hudaya, G.K. and Madiutomo, N. 2019. The availability of Indonesian coal to meet the 2050 demand. Indonesian Mining Journal 22(2):107-128, doi:10.30556/imj.vol22.no2.2019.689.

Indarto, I., Mandala, M., Febrian Arifin, F. and Hakim, L.F. 2020. Sentinel-2 imagery application for mapping land cover and land use at village levels. Jurnal Geografi 12(02):189, doi:10.24114/jg.v12i02.16970 (in Indonesian).

Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G. and Zhang, X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 101(3):366-378, doi:10.1016/j.rse.2006.01.003.

Jiao, L., Liu, Y. and Li, H. 2012. Characterizing land-use classes in remote sensing imagery by shape metrics. ISPRS Journal of Photogrammetry and Remote Sensing 72: 46-55, doi:10.1016/j.isprsjprs.2012.05.012.

Kaplan, G. and Avdan, U. 2017. Object-based water body extraction model using Sentinel-2 satellite imagery. European Journal of Remote Sensing 50(1):137-143, doi:10.1080/22797254.2017.1297540.

Kerr, G.H.G., Fischer, C. and Reulke, R. 2015a. A data-driven approach to quality assessment for hyperspectral systems. Computers and Geosciences 83:100-109, doi:10.1016/j.cageo.2015.07.004.

Kerr, G.H.G., Fischer, C. and Reulke, R. 2015b. Reliability assessment for remote sensing data: Beyond Cohen’s kappa. International Geoscience and Remote Sensing Symposium (IGARSS), 2015-November, 4995-4998, doi:10.1109/IGARSS.2015.7326954.

Kitada, K. and Fukuyama, K. 2012. Land-use and land-cover mapping using a gradable classification method. Remote Sensing 4(6):1544-1558, doi:10.3390/RS4061544.

Kolokoussis, P. and Karathanassi, V. 2018. Oil Spill Detection and Mapping Using Sentinel 2 Imagery. Journal of Marine Science and Engineering 6(1):4, doi:10.3390/jmse6010004.

Kotaridis, I. and Lazaridou, M. 2021. Delineation of open-pit mining boundaries on multi-spectral imagery. Remote Sensing, IntechOpen, doi:10.5772/intechopen.94120.

Kvålseth, T.O. 2015. Measurement of interobserver disagreement: correction of Cohen’s Kappa for negative values. Journal of Probability and Statistics 2015, doi:10.1155/2015/751803.

Lamquin, N., Woolliams, E., Bruniquel, V., Gascon, F., Gorroño, J., Govaerts, Y., Leroy, V., Lonjou, V., Alhammoud, B., Barsi, J.A., Czapla-Myers, J.S., McCorkel, J., Helder, D., Lafrance, B., Clerc, S. and Holben, B.N. 2019. An inter-comparison exercise of Sentinel-2 radiometric validations assessed by independent expert groups. Remote Sensing of Environment 233: 111369, doi:10.1016/j.rse.2019.111369.

Lantzanakis, G., Mitraka, Z. and Chrysoulakis, N. 2016. Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. In K. Themistocleous, D. G. Hadjimitsis, S. Michaelides, & G. Papadavid (Eds.), Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016) (Vol. 9688, p. 96880A). SPIE, doi:10.1117/12.2242889.

Li, J. and Sheng, Y. 2008. Evaluation of NDWI as a universal index to map global surface water. AGUFM, 2008:H41B-0876.

Lillesand, T., Kiefer, R. and Chipman, J. 2015. Remote Sensing and Image Interpretation, 7th edition, Wiley, ISBN: 978-1-118-34328-9 February 2015 736 Pages.

Lopes, M., Frison, P., Crowson, M., Warrenâ€Thomas, E., Hariyadi, B., Kartika, W.D., Agus, F., Hamer, K.C., Stringer, L., Hill, J.K. and Pettorelli, N. 2020. Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series. Methods in Ecology and Evolution 11(4):532-541, doi:10.1111/2041-210X.13359.

Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U. and Gascon, F. 2017. Sen2Cor for Sentinel-2. In: Bruzzone, L., Bovolo, F. and Benediktsson, J.A. (Eds.), Image and Signal Processing for Remote Sensing XXIII, Vol. 10427, SPIE, doi:10.1117/12.2278218.

Marangoz, A.M., Sekertekin, A. and Akçin, H. 2017. Analysis of land use land cover classification results derived from sentinel-2 image. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 17(23): 25-32, doi:10.5593/sgem2017/23/S10.004.

Mather, P.M. and Koch, M. 2010. Computer Processing of Remotely-Sensed Images: An Introduction. 4th Edition. John Wiley and Sons, doi:10.1002/9780470666517.

Ministry of Energy and Mineral Resources. (2018). Decree of the Minister of Energy and Mineral Resources of the Republic of Indonesia Number 1827 K/30/MEM/2018 concerning Guidelines for implementing Good Mining Engineering Rules (in Indonesian).

Ouattara, S., Loum, G.L., Clément, A. and Vigouroux, B. 2011. Analysis of the relevance of evaluation criteria for multicomponent image segmentation. Journal of Software Engineering and Applications 4(6):371-378, doi:10.4236/jsea.2011.46042.

Pancorbo, J.L., Lamb, B.T., Quemada, M., Hively, W.D., Gonzalez-Fernandez, I. and Molina, I. 2021. Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements. ISPRS Journal of Photogrammetry and Remote Sensing 173:166-180, doi:10.1016/j.isprsjprs.2021.01.009.

Pei, W., Yao, S., Knight, J. F., Dong, S., Pelletier, K., Rampi, L. P., Wang, Y. and Klassen, J. 2017. Mapping and detection of land use change in a coal mining area using object-based image analysis. Environmental Earth Sciences 76(3), doi:10.1007/s12665-017-6444-9.

Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., Quang, C., Gascon, F. and Boccia, V. 2020. Next updates of atmospheric correction processor Sen2Cor. In: Notarnicola, C., Bovenga, F., Bruzzone, L., Bovolo, F., Benediktsson, J.A., Santi, E. and Pierdicca, N. (eds.), Image and Signal Processing for Remote Sensing XXVI, Vol. 11533, SPIE, doi:10.1117/12.2574035.

Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y. and Ranagalage, M. 2020. Sentinel-2 Data for land cover/use mapping: a review. Remote Sensing 12(14):2291, doi:10.3390/rs12142291.

Porwal, S. and Katiyar, S.K. 2014. Performance evaluation of various resampling techniques on IRS imagery. 2014 7th International Conference on Contemporary Computing, IC3 2014, 489-494, doi:10.1109/IC3.2014.6897222.

Roberts, D.A., Numata, I., Holmes, K., Batista, G., Krug, T., Monteiro, A., Powell, B. and Chadwick, O.A. 2002. Large area mapping of land-cover change in Rondônia using multitemporal spectral mixture analysis and decision tree classifiers. Journal of Geophysical Research: Atmospheres 107(20):LBA 40-1-LBA 40-18, doi:10.1029/2001JD000374.

Rwanga, S.S., Ndambuki, J.M., Rwanga, S.S. and Ndambuki, J.M. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences 8(4):611-622, doi:10.4236/IJG.2017.84033.

Setyowati, H.A., Dwinugroho, M.P., Sigit Heru Murti, B.S., Yulianto, A., Ajiwihanto, N.E., Hadinata, J. and Sanjiwana, A.K. 2018. ESDM one map Indonesia: opportunities and challenges to support one map policy based on applied Web-GIS. IOP Conference Series: Earth and Environmental Science 165(1), doi:10.1088/1755-1315/165/1/012021.

Sim, J. and Wright, C.C. 2005. The Kappa statistic in reliability studies: use, interpretation, and sample size requirements. Physical Therapy 85(3):257-268, doi:10.1093/ptj/85.3.257.

Sinabutar, J.J., Sasmito, B. and Sukmono, A. 2020. Cloud masking study using band quality assessment, function of mask and multi-temporal cloud masking on Landsat 8 imagery. Jurnal Geodesi UNDIP 9(3):51-60 (in Indonesian).

Smith, R.B. 2012. Remote Sensing of Environment (RSE) with TNTmips ® TNTview ®.

Soulard, C.E., Acevedo, W., Stehman, S.V. and Parker, O.P. 2016. Mapping extent and change in surface mines within the United States from 2001 to 2006. Land Degradation & Development 27(2):248-257, doi:10.1002/ldr.2412.

Szabó, S., Gácsi, Z. and Balázs, B. 2016. Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Landscape & Environment 10(3-4):194-202, doi:10.21120/le/10/3-4/13.

van der Meer, F., Hecker, C., van Ruitenbeek, F., van der Werff, H., de Wijkerslooth, C. and Wechsler, C. 2014. Geologic remote sensing for geothermal exploration: A review. International Journal of Applied Earth Observation and Geoinformation 33(1):255-269, doi:10.1016/j.jag.2014.05.007.

Verhoeven, V.B. and Dedoussi, I.C. 2022. Annual satellite-based NDVI-derived land cover of Europe for 2001–2019. Journal of Environmental Management 302:113917, doi:10.1016/j.jenvman.2021.113917.

Wentz, E.A. 2010. A shape definition for geographic applications based on edge, elongation, and perforation. Geographical Analysis 32(2):95-112, doi:10.1111/j.1538-4632.2000.tb00419.x.

Werner, T.T., Mudd, G.M., Schipper, A.M., Huijbregts, M.A.J., Taneja, L. and Northey, S.A. 2020. Global-scale remote sensing of mine areas and analysis of factors explaining their extent. Global Environmental Change, 60:102007, doi:10.1016/j.gloenvcha.2019.102007.

Whiteside, T.G., Boggs, G.S. and Maier, S.W. 2011. Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation 13(6):884-893, doi:10.1016/j.jag.2011.06.008.

Widayani, P. 2018. Application of object-based image analysis for early identification of slums using satellite imagery worldview-2. Majalah Geografi Indonesia 32(2):162, doi:10.22146/mgi.32306 (in Indonesian).

Xie, Y., Sha, Z. and Yu, M. 2008. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1(1):9-23, doi:10.1093/jpe/rtm005.

Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14):3025-3033, doi:10.1080/01431160600589179.

Zhou, W., Troy, A. and Grove, M. 2008. Object-based Land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high-resolution remote sensing data. Sensors 8(3):1613-1636, doi:10.3390/s8031613.

Zhu, Z. and Woodcock, C.E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment 152:217-234, doi:10.1016/j.rse.2014.06.012.

Zygmunt, M., Szewczyk, R., Gniadek, J. and Janus, J. 2022. New approach to shape elongation estimation based on the Delaunay triangulation. Survey Review, doi:10.1080/00396265.2022.2091876.

Downloads

Submitted

25-08-2022

Accepted

21-10-2022

Published

01-01-2023

How to Cite

Lestari, L., Kusuma, G. J., Badhurahman, A., Dwiki, S., & Gautama, R. S. (2023). Mine void identification using Object-based Image Analysis (OBIA) of satellite imagery Sentinel 2 data. Journal of Degraded and Mining Lands Management, 10(2), 4129–4142. https://doi.org/10.15243/jdmlm.2023.102.4129

Issue

Vol. 10 No. 2 (2023)

Section

Research Article

License

Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Scientific Journal by Eko Handayanto is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://ub.ac.id.
Permissions beyond the scope of this license may be available at https://ircmedmind.ub.ac.id/.