ORIGINAL ARTICLE
Remote Sensing Analysis of Environmental Changes in a Post-Mining Area: A Case Study of the Olkusz Region – Preliminary Results
1
Department of Geodesy and Geoinformatics, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Wrocław, Poland
Submission date: 2024-12-09
Final revision date: 2025-03-27
Acceptance date: 2025-04-07
Online publication date: 2025-05-20
Publication date: 2025-05-20
Corresponding author
Aleksandra Smentek
Department of Geodesy and Geoinformatics, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Wybrzeże Stanisława Wyspiańskiego 27, 50-370, Wrocław, Poland
Department of Geodesy and Geoinformatics, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Wybrzeże Stanisława Wyspiańskiego 27, 50-370, Wrocław, Poland
Civil and Environmental Engineering Reports 2025;35(3):1-18
KEYWORDS
TOPICS
ABSTRACT
Continuous environmental monitoring of post-mining areas is essential, even years after the end of mining activity. Olkusz-Pomorzany mine closure in 2020 included shutting down the water pumps (2022), which resulted in the restoration of the underground water table and subsequent water appearing on the surface. The aim of this study is to analyse spatio-temporal water and vegetation changes in the post-mining and adjacent areas in Olkusz region, Poland, using remote sensing techniques on open-access satellite imagery data. The study uses nine Sentinel-2 images (2022-2024) and spectral indices (Modified Normalized Difference Water Index, Normalized Difference Vegetation Index) to identify and calculate the area of surface water and vegetation condition changes. Indices revealed a total water area of almost 400,000 m2 and a substantial vegetation cover decrease. Using selected indices on open-access data allows the detection of surface water bodies and can provide preliminary results of spatio-temporal analysis of environmental changes in selected post-mining area.
REFERENCES (41)
1.
Jhariya, DC, Khan, R and Thakur, GS 2016. Impact of Mining Activity on Water Resource: An Overview study. Conference: National Seminar on Recent Practices & Innovations in Mining Industry (RPIMI 2016), Raipur, India, February 20216.
2.
Laurence, D 2006. Optimisation of the mine closure process. Journal of Cleaner Production 14(3), 285–98.
3.
Sanmiquel, L, Bascompta, M, Vintró, C and Yubero, T 2018. Subsidence Management System for Underground Mining. Minerals 8(6), 243.
4.
Tajduś, K, Sroka, A, Misa, R and Dudek, M 2017. Przykłady zagrożeń powierzchni terenu deformacjami nieciągłymi typu powierzchniowego ujawniające się nad zlikwidowanymi podziemnymi wyrobiskami górniczymi [Examples of threats to the land surface by discontinuous deformations of the surface type revealed over decommissioned underground mine excavations]. Prace Instytutu Mechaniki Górotworu PAN, 19, 3-10.
5.
Lupa, M, Pełka, A, Młynarczuk, M, Staszel, J and Adamek, K 2023. Why Rivers Disappear—Remote Sensing Analysis of Postmining Factors Using the Example of the Sztoła River, Poland. Remote Sensing, 16(1), 111.
6.
Mroczkowska, P and Biały, W 2015. Zwałowiska górnicze, a środowisko wodne na obszarach górniczych Republiki Czeskiej [Mining shafts, and the water environment in the mining areas of the Czech Republic]. Systemy Wspomagania w Inżynierii Produkcji (3 (12)), 132–41.
7.
Sebnem Düzgün, H, Demirel, N 2011. Remote Sensing of the Mine Environment. London: CRC Press.
8.
Gojković, Z, Kilibarda, M, Brajović, L, Marjanović, M, Milutinović, A and Ganić, A 2023. Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine. Remote Sensing 15(10), 2519.
9.
Pawłuszek-Filipiak, K, Wielgocka, N, Tondaś, D, Borkowski, A 2023. Monitoring nonlinear and fast deformation caused by underground mining exploitation using multi-temporal Sentinel-1 radar interferometry and corner reflectors: application, validation and processing obstacles. International Journal of Digital Earth 16(1), 251–271.
10.
Declercq, PY, Dusar, M, Pirard, E, Verbeurgt, J, Choopani, A and Devleeschouwer, X 2023. Post Mining Ground Deformations Transition Related to Coal Mines Closure in the Campine Coal Basin, Belgium, Evidenced by Three Decades of MT-InSAR Data. Remote Sensing 15(3), 725.
11.
Fabre, S, Elger, A, Riviere, T 2020. Exploitation of Sentinel-2 images for long-term vegetation monitoring at a former ore processing site, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1533–1537.
12.
Orlov, S, Tsygulev, K, Sekrieru, R, Smagin, S and Smagin, A 2024. Remote Monitoring Algorithm for Vegetation Recovery on Mining Industry Sites. Software Engineering Methods Design and Application. Cham: Springer Nature Switzerland, p. 677–83.
13.
Sun, X, Zhou, Y, Jia, S, Shao, H, Liu, M, Tao, S, Dai, X 2024. Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas. Journal of Environmental Management 356, 120678.
14.
Saghafi, M, Ahmadi, A and Bigdeli, B 2021. Sentinel-1 and Sentinel-2 data fusion system for surface water extraction. Journal of Applied Remote Sensing, Vol. 15, Issue 1, 014521.
15.
Bioresita, F, Puissant, A, Stumpf, A, Malet, JP 2019. Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping, International Journal of Remote Sensing 40:23, 9026-9049.
16.
Jiang, H, Wang, M, Hu, H, Xu, J 2021. Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions. Water, 13(7), 945.
17.
Düzgün, H.S., & Demirel, N. 2011. Remote Sensing of the Mine Environment (1st ed.). London: CRC Press.
18.
Nursaputra, M, et al, 2021. The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area, IOP Conf. Series: Earth and Environmental Science 886 (2021) 012100, 2nd Biennial Conference of Tropical Biodiversity, Makassar, Indonesia, 4 -5 August 2021.
19.
Yang, Z, Shen, Y, Li, J, et al, 2022. Unsupervised monitoring of vegetation in a surface coal mining region based on NDVI time series. Environmental Science and Pollution Research 29, 26539–26548.
20.
Li, H, Xie, M, Wang, H, Li, S, Xu M 2020. Spatial Heterogeneity of Vegetation Response to Mining Activities in Resource Regions of Northwestern China. Remote Sensing 12, no. 19, 3247.
21.
Padmanaban, R, Bhowmik, AK, Cabral, P 2017. A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area. ISPRS International Journal of Geo-Information 6(12), 401.
22.
Erner, A 2011. Remote sensing of vegetation health for reclaimed areas of Seyitömer open cast coal mine. International Journal of Coal Geology V-86(1), 20-26.
23.
Karan, SK, Samadder, SR, Maiti, SK 2016. Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of Environmental Management V182, 272-283.
24.
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.
25.
Kareem, H H, Attaee, M H, Omran, Z A 2024. Estimation the Water Ratio Index (WRI) and Automated Water Extraction Index (AWEI) of Bath in The United Kingdom Using Remote Sensing Technology of The Multispectral Data of Landsat 8-Oli. Water Conservation & Management 8(1), 171-178.
26.
Jiang, W, Ni, Y, Pang, Z, He, G, Fu, J, Lu, J, Yang, K, Long, T, Lei, T 2020. A new index for identifying water body from Sentinel-2 satellite remote sensing imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3–2020, 33-8.
27.
Gao, B 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58(3), 257–66.
28.
Xu, H 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14), 3025–33.
29.
ZGH Bolesław S.A. Likwidacja Kopalni. ZGH Bolesław, https://zghboleslaw.pl/pl/aktu.... Accessed 20-11-2024.
30.
Niewdana, J, Świć, E 2011. Żywioły w świecie podziemnych skarbów Olkuskich kopalń rud [Elements in the world of underground treasures of Olkusz ore mines]. Bukowno: Zakłady Górniczo-Hutnicze BOLESŁAW S.A. w Bukownie.
31.
Motyka, J, Adamczyk, Z, Juśko, K 2016. Dopływy wody do olkuskich kopalń rud cynku i ołowiu w ujęciu historycznym [Water inflows to Olkusz zinc and lead ore mines in historical perspective]. Przegląd Górniczy Volume. 72, 6, 49–58.
32.
Woch, MW, Stefanowicz, AM, Stanek, M 2017. Waste heaps left by historical Zn-Pb ore mining are hotspots of species diversity of beech forest understory vegetation. Science of The Total Environment 599–600, 32–41.
33.
Photo by Rozmus, F, published by Olkusz.tv. Facebook, 17-08-2024. https://www.facebook.com/Olkus.... Accessed 05-12-2024.
34.
Bureau for Forest Management and Geodesy - Forest Data Bank, https://www.bdl.lasy.gov.pl/po..., Accessed on 05-12-2024.
35.
European Space Agency. Sentinel-2. Copernicus Data Space Ecosystem, https://dataspace.copernicus.e.... Accessed 05-12-2024.
36.
AGH University of Science and Technology. Archival Weather Charts. AGH University, http://meteo.ftj.agh.edu.pl/ar.... Accessed 05-12-2024.
37.
Kriegler, F J, Malila, W A, Nalepka, R F, Richardson, W 1969. Preprocessing transformations and their effects on multspectral recognition. Proceedings of the Sixth International Symposium on Remote Sesning of Environment, 97–131.
38.
Huang, S, Tang, L, Hupy, JP, Wang, Y, Shao, G 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res, 32(1), 1–6.
39.
Mehta, A, Shukla, S, Rakholia, S 2021. Vegetation Change Analysis using Normalized Difference Vegetation Index and Land Surface Temperature in Greater Gir Landscape. Journal of Scientific Research 65, 01–6.
40.
Sentinel Hub. Normalized Difference Snow Index (NDSI). Sentinel Hub Custom Scripts, https://custom-scripts.sentine.... Accessed 05-12-2024.
41.
EOS Data Analytics. Color Infrared (CIR) Imagery: Uses & Benefits. EOS, https://eos.com/make-an-analys.... Accessed 05-12-2024.
| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
