Journal of Mining Engineering

Journal of Mining Engineering

A fuzzy-statistical segmentation of Sentinel 2 satellite images to determine the mineral pollution of coal in Damghan

Document Type : research - paper

Authors
Amir mahmood Razaviyan 1
Ali Reza Arab_Amiri 2
Abolghasem Kamkar Rouhani 1
Meysam Davoodabadi 3
1 Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
2 Lecturer at Shahrood University
3 Faculty Memeber, Remote Sensing Educational Group, Hekmat Institute of Higher Education
10.22034/ijme.2025.2024810.2005
Abstract
 
[1] Corresponding Author





Human activities, such as mining, constructing highways, and building infrastructures like dams and industrial facilities, have caused significant damage to many natural ecosystems. In addition to disrupting the balance of these ecosystems, some of these activities have led to considerable pollution. Traditional methods, such as geostatistics and geophysics, while helpful info helpful in identifying contaminated areas and measuring the extent of pollution, are often costly, time-consuming, and come with helpful in identifying areas and measuring the extent of pollution, are often expensive, time-consuming and come with various limitations. To address these limitations, remote sensing data can provide an effective solution. The foundation of remote sensing lies in the spectral frequencies or images captured through satellite or drone equipment. Remote sensing data processing methods for identifying the type and estimating the extent of soil contamination fall into three main categories: physical models, mathematical models, and physics-based mathematical models. Given the various challenges associated with remote sensing of pollution in mining areas and their surroundings and the limited research conducted—particularly in Iran—this study addresses a critical important important important necessary conducted undertaken—particularly in Iran—this study addresses the essential gap. Iran hosts numerous mining sites in or near critical environmental ecosystems, making pollution management in these areas a pressing issue. This research proposes a method combining fuzzy clustering and edge-based features to identify surface and physical soil contamination caused by mining activities and coal-washing waste from the eastern Alborz coal preparation plant. This approach uses Sentinel-2 satellite imagery at a 1:100,000 scale for the studied mining area. The study focuses on the eastern Alborz coal mining region in Semnan Province, Iran. This area lies 140 kilometers from the provincial capital, 25 kilometers northeast of Damghan, and 75 kilometers from Shahroud.
Keywords
Fuzzy clustering
Sentinel 2
Pollution segmentation
Coal
Satellite image processing
Subjects
Ceballos, G., Ehrlich, P. R., & Dirzo, R. (2017). Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proceedings of the national academy of sciences, 114(30), E6089-E6096.  ##
Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P., & Moomaw, W. R. (2020). World scientists’ warning of a climate emergency. BioScience, 70(1), 8-100. ##
Choe, E., van der Meer, F., van Ruitenbeek, F., van der Werff, H., de Smeth, B., & Kim, K. W. (2008). Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain. Remote Sensing of Environment, 112(7), 3222-3233. ##
Ingram, J.C., Dawson, T.P., Whittaker, R.J., 2005. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens. Environ. 94, 491– 507. ##
Watson, J.E., Joseph, L.N., Fuller, R.A., 2010. Mining and conservation: implications for Madagascar’s littoral forests. Conservation Letters 3, 286–287. ##
Rogan, J., Agrawal, S., Gamboa, C., et al,.2018 . Bebbington, A.J., Bebbington, D.H., Sauls, L.A.,Resource extraction and infrastructure threaten forest cover and community rights. Proc. National Academy Sci., 201812505. ##
Hodgson, D.A., Vyverman, W., Chepstow-Lusty, A., Tyler, P.A., 2000. From rainforest to wasteland in 100 years: the limnological legacy of the Queenstown mines, Western Tasmania. Archiv für Hydrobiologie 149, 153–176 ##
Kim, S.-M., Choi, Y., Suh, J., Oh, S., Park, H.-D., Yoon, S.-H., 2012a. Estimation of soil erosion and sediment yield from mine tailing dumps using GIS: a case study at the samgwang mine, Korea. Geosystem Eng. 15, 2–9. ##
Mazabanda, C., Kemper, R., Thieme, A., Hettler, B., Finer, M., 2018. Impacts of mining project (mirador) in the Ecuadorian amazon. 2018. Monitoring Andean Amazon Project (MAAP). ##
Werner, T. T., Bebbington, A., & Gregory, G. (2019). Assessing impacts of mining: Recent contributions from GIS and remote sensing. The Extractive Industries and Society, 6(3), 993-1012. ##
Agboola, O., Babatunde, D. E., Fayomi, O. S. I., Sadiku, E. R., Popoola, P., Moropeng, L., ... & Mamudu, O. A. (2020). A review on the impact of mining operation: Monitoring, assessment and management. Results in Engineering, 8, 100181. ##
Zhu, D., Chen, T., Zhen, N., & Niu, R. (2020). Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environmental Science and Pollution Research, 27, 15716-15728. ##
Gallwey, J., Robiati, C., Coggan, J., Vogt, D., & Eyre, M. (2020). A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining. Remote Sensing of Environment, 248, 111970. ##
Qian, X., Li, C., Wang, W., Yao, X., & Cheng, G. (2023). Semantic segmentation guided pseudo label mining and instance re-detection for weakly supervised object detection in remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 119, 103301. ##
Zhang, Z., He, G., Wang, M., Wang, Z., Long, T., & Peng, Y. (2015). Detecting decadal land cover changes in mining regions based on satellite remotely sensed imagery: A case study of the stone mining area in Luoyuan county, SE China. Photogrammetric Engineering & Remote Sensing, 81(9), 745-751. ##
Xiao, D., Yin, L., & Fu, Y. (2021). Open-pit mine road extraction from high-resolution remote
sensing images using RATT-UNet. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. ##
Shamsolmoali, P., Zareapoor, M., Wang, R., Zhou, H., & Yang, J. (2019). A novel deep structure UNet for sea-land segmentation in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3219-3232. ##
Qin, P., Cai, Y., & Wang, X. (2021). Small waterbody extraction with improved U-Net using Zhuhai-1 hyperspectral remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. ##
Fan, X., Yan, C., Fan, J., & Wang, N. (2022). Improved U-net remote sensing classification algorithm fusing attention and multiscale features. Remote Sensing, 14(15), 3591. ##
Wang, C., Chang, L., Zhao, L., & Niu, R. (2020). Automatic identification and dynamic monitoring of open-pit mines based on improved mask RCNN and transfer learning. Remote Sensing, 12(21), 3474. ##
Chen, T., Hu, N., Niu, R., Zhen, N., & Plaza, A. (2020). Object-oriented open-pit mine mapping using Gaofen-2 satellite image and convolutional neural network, for the Yuzhou City, China. Remote Sensing, 12(23), 3895. ##
Maxwell, A. E., Bester, M. S., Guillen, L. A., Ramezan, C. A., Carpinello, D. J., Fan, Y., ... & Pyron, J. L. (2020). Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps. Remote Sensing, 12(24), 4145. ##
Xu, D., Zhao, Y., Jiang, Y., Zhang, C., Sun, B., & He, X. (2021). Using improved edge detection method to detect mining-induced ground fissures identified by unmanned aerial vehicle remote sensing. Remote Sensing, 13(18), 3652. ##
Xie, H., Pan, Y., Luan, J., Yang, X., & Xi, Y. (2021). Open-pit mining area segmentation of remote sensing images based on DUSegNet. Journal of the Indian Society of Remote Sensing, 49, 1257-1270. ##
Xie, H., Pan, Y., Luan, J., Yang, X., & Xi, Y. (2021). Semantic segmentation of open pit mining area based on remote sensing shallow features and deep learning. In Big Data Analytics for CyberPhysical System in Smart City: BDCPS 2020, 28-29 December 2020, Shanghai, China (pp. 52-59). Springer Singapore. ##
Liang, C., Xiao, B., & Cheng, B. (2021, July). GCN-Based Semantic Segmentation Method for Mine Information Extraction in GAOFEN-1 Imagery. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 3432-3435). IEEE. ##
Wu, K. Y., Wang, X., Zhou, J. J., Wang, X. F., Fan, Y. P., & Yao, M. (2021, October). An improved D-LinkNet method for road extraction from high resolution remote sensing images. In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) (pp. 175-180). IEEE. ##
He, H., Xu, H., Zhang, Y., Gao, K., Li, H., Ma, L., & Li, J. (2022). Mask R-CNN based automated identification and extraction of oil well sites. International Journal of Applied Earth Observation and Geoinformation, 112, 102875. ##
Wu, M., Zhang, C., Liu, J., Zhou, L., & Li, X. (2019). Towards accurate high resolution satellite image semantic segmentation. Ieee Access, 7, 55609-55619. ##
Xing, Z., Zhao, S., Guo, W., Meng, F., Guo, X., Wang, S., & He, H. (2023). Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model. Energy, 285, 128771. ##
Selva, M., Aiazzi, B., Butera, F., Chiarantini, L., & Baronti, S. (2015). Hyper-sharpening: A first approach on SIM-GA data. IEEE Journal of selected topics in applied earth observations and remote sensing, 8(6), 3008-3024. ##
Wang, Q., Shi, W., Li, Z., & Atkinson, P. M. (2016). Fusion of Sentinel-2 images. Remote sensing of environment, 187, 241-252. ##
Kaplan, G., & Avdan, U. (2018, March). Sentinel-2 pan sharpening—comparative analysis. In Proceedings (Vol. 2, No. 7, p. 345). MDPI. ##
Bouslihim, Y., Kharrou, M. H., Miftah, A., Attou, T., Bouchaou, L., & Chehbouni, A. (2022). Comparing pan-sharpened landsat-9 and sentinel-2 for land-use classification using machine learning classifiers. Journal of Geovisualization and Spatial Analysis, 6(2), 35. ##
Siok, K., Ewiak, I., & Jenerowicz, A. (2020).Multi-sensor fusion: A simulation approach to pansharpening aerial and satellite images. Sensors, 20(24), 7100. ##
Sun, Z., Xuan, P., Song, Z., Li, H., & Jia, R. (2022). A texture fused superpixel algorithm for coal mine waste rock image segmentation. International Journal of Coal Preparation and Utilization, 42(4), 1222-1233. ##
Saranathan, A. M., & Parente, M. (2015). Uniformity-based superpixel segmentation of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1419- 1430. ##
Wu, X., Zhang, X., & Lin, H. (2018). Superpixel Based Factor Analysis and Target Transformation Method for Martian Minerals Detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1901-1906. ##
Gay, R., Lecoutre, J., Menouret, N., Morillon, A., & Monasse, P. (2022). Bilateral K-Means for Superpixel Computation (the SLIC Method). Image Processing On Line, 12, 72-91. ##
Song, J., Cong, W., & Li, J. (2017). A Fuzzy Cmeans Clustering Algorithm for Image Segmentation Using Nonlinear Weighted Local Information. J. Inf. Hiding Multim. Signal Process., 8(3), 578-588. ##
Song, Q., Wu, C., Tian, X., Song, Y., & Guo, X. (2022). A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation. Applied Intelligence, 1-22. ##
Journal of Mining Engineering
Volume 19, Issue 64
Autumn 2024
Pages 100-121

  • Receive Date 14 March 2024
  • Revise Date 19 January 2025
  • Accept Date 29 January 2025