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The potential of using hyperspectral airborne data in monitoring post-mining novel ecosystems

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Parent Category: 2026

Category: Content №4 2026

Created on 24 August 2026

Last Updated on 24 August 2026

Published on 30 November -0001

Written by J. Ceglarek, S. Hacia, A. K. Hutniczak, A. Błońska, G. Woźniak

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Authors:

J. Ceglarek, orcid.org/0000-0003-0444-7072, Adam Mickiewicz University, Faculty of Geographical and Geological Sciences, Poznan, Poland

S. Hacia, orcid.org/0009-0004-6298-6247, University of Silesia in Katowice, Faculty of Natural Sciences, Institute of Biology, Biotechnology and Environmental Protection, Katowice, Poland

A. K. Hutniczak*, orcid.org/0000-0002-6235-6139, University of Silesia in Katowice, Faculty of Natural Sciences, Institute of Biology, Biotechnology and Environmental Protection, Katowice, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A. Błońska, orcid.org/0000-0002-1696-0001, University of Silesia in Katowice, Faculty of Natural Sciences, Institute of Biology, Biotechnology and Environmental Protection, Katowice, Poland

G. Woźniak, orcid.org/0000-0003-1936-2880, University of Silesia in Katowice, Faculty of Natural Sciences, Institute of Biology, Biotechnology and Environmental Protection, Katowice, Poland

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (4): 112 - 119

https://doi.org/10.33271/nvngu/2026-4/112

Abstract:

Purpose. Improving the efficiency and accuracy of monitoring post-mining landscapes through the use of hyperspectral remote sensing and analysis of airborne data for vegetation management on post-mining heaps, in line with the principles of modern geoinformation technologies and remote sensing science.

Methodology. Based on remote sensing methods, particularly hyperspectral airborne data, monitoring and assessment of post-mining areas are carried out. Hyperspectral remote sensing provides an effective approach for assessing contamination levels, vegetation condition, and restoration processes, thereby supporting decision-making in land reclamation and sustainable management of post-mining landscapes.

Findings. An improved, validated model for monitoring post-mining ecosystems using hyperspectral airborne data has been developed, thereby increasing the accuracy of assessing the ecological condition of waste heaps and the dynamics of vegetation recovery. The spectral characteristics of areas with different levels of anthropogenic impact have been identified, enabling the detection of degradation zones and areas of natural regeneration. The effectiveness of integrating hyperspectral data and geoinformation technologies for reclamation control and post-mining landscape management has been proven.

Originality. The monitoring approach for post-mining ecosystems based on the use of hyperspectral airborne data and geoinformation technologies has been proposed and validated. Its main principles include:

-     the determination of spectral characteristics of vegetation cover and waste heaps with different levels of anthropogenic impact;

-     the identification of degradation, contamination, and natural regeneration zones;

-     the integration of remote sensing data with geoinformation systems;

-     the provision of reclamation process control as well as forecasting the development of new ecosystems in areas affected by mining activities.

Practical value. The proposed approach to monitoring post-mining ecosystems using hyperspectral airborne data and geoinformation technologies improves the accuracy and efficiency of assessing the ecological condition of waste heaps and adjacent areas. The obtained results can be used to identify zones of degradation and natural vegetation recovery, as well as to monitor reclamation processes in landscapes affected by mining activities. The practical implementation of this approach improves environmental monitoring systems in mining regions and enhances the effectiveness of management decisions for restoring disturbed territories.

Keywords: remote sensing, minerals, mineral heaps, hyperspectral data, environmental resilience assessment tool

References.

1. Fojcik, Z., Hojka, M., Kaczmarzewski, S., & Woźniak, G. (2024). Examples of laser scanning applications in JSW SA mines. E3S Web of Conferences, 567, 01007. https://doi.org/10.1051/e3sconf/202456701007

2. Flores, H., Lorenz, S., Jackisch, R., Tusa, L., Contreras, I. C., Zimmermann, R., & Gloaguen, R. (2021). UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters. Minerals, 11(2), 182. https://doi.org/10.3390/min11020182

3. Woźniak, G., Dyderski, M. K., Kompała-Bąba, A., Jagodziński, A. M., Pasierbiński, A., Błońska, A., …, & Sierka, E. (2021). Use of remote sensing to track postindustrial vegetation development. Land Degradation and Development, 32(3), 1426-1439. https://doi.org/10.1002/ldr.3789

4. Hobbs, R. J., Higgs, E. S., & Hall, C. M. (2013). Defining novel ecosystems, In R. J. Hobbs, E. S. Higgs, & C. M. Hall (Eds.) Novel Ecosystems. Intervening in the New Ecological World Order, (pp. 58-60). John Wiley & Sons, Chichester. https://doi.org/10.1002/9781118354186.ch6

5. Gwenzi, W. (2021). Rethinking Restoration Indicators and End-Points for Post-Mining Landscapes in Light of Novel Ecosystems. Geoderma, 387, 114944. https://doi.org/10.1016/j.geoderma.2021.114944

6. Błońska, A., Kompała-Bąba, A., Sierka, E., Benesyei, L., Magurno, F., Bierza, W., Frydecka, K., & Woźniak, G. (2019). Impact of selected plant species on enzymatic activity of soil substratum on post-mining heaps. Journal of Ecological Engineering, 20(1), 138-144. https://doi.org/10.12911/22998993/93867

7. Bakr, J., Kompała-Bąba, A., Bierza, W., Chmura, D., Hutniczak, A., Błońska, A., Nowak, T., …, & Woźniak, G. (2024). Taxonomic and functional diversity along successional stages on post-coalmine spoil heaps. Frontiers in Environmental Science, Front. Environ. Sci., 12, 1412631. https://doi.org/10.3389/fenvs.2024.1412631

8. Konovalov, V. E., Semyachkov, A. I., & Pochechun, V. A. (2018). Concept of Mining Landscape Rehabilitation. IOP Conference Series: Materials Science and Engineering, 451, 012208. https://doi.org/10.1088/1757-899X/451/1/012208

9. Ramani, R. V. (2012). Surface Mining Technology: Progress and Prospects. Procedia Engineering, 46, 9-21. https://doi.org/10.1016/j.proeng.2012.09.440

10.      Davies, G. E., & Calvin, W. M. (2017). Mapping Acidic Mine Waste with Seasonal Airborne Hyperspectral Imagery at Varying Spatial Scales. Environmental Earth Sciences, 76(12), 432. https://doi.org/10.1007/s12665-017-6763-x

11.      Song, W., Song, W., Gu, H., & Li, F. (2020). Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas. International Journal of Environmental Research and Public Health, 17(6), 1846. https://doi.org/10.3390/ijerph17061846

12.      Tolentino, V., Lucero, A. O., Koerting, F., Savinova, E., Hildebrand, J. C., & Mickelthwaite, S. (2025). Drone-Based VNIR–SWIR Hyperspectral Imaging for Environmental Monitoring of a Uranium Legacy Mine Site. Drones, 9(4), 313. https://doi.org/10.3390/drones9040313

13.      Yang, L., & Jiuyun, S. (2011). Study of the Integrated Environmental Monitoring in Mining Area Based on Image Analysis. Procedia Engineering, 21, 267-272. https://doi.org/10.48550/arXiv.2404.00272

14.      Padró, J.-C., Carabassa, V., Balagué, J., Brotons, L., Alcañiz, J. M., & Pons, X. (2019). Monitoring Opencast Mine Restorations Using Unmanned Aerial System (UAS) Imagery. Science of The Total Environment, 657, 1602, 14. https://doi.org/10.1016/j.scitotenv.2018.12.156

15.      Ryś, K., Dyczko, A., Chmura, D., & Woźniak, G. (2025). Multi-aspect analysis of biomass production concerning taxonomic and functional trait composition of vegetation on heaps. Journal of Water and Land Development, (65), 74-89. https://doi.org/10.24425/jwld.2025.154252

16.      Fajfer, J., Dyczko, A., & Woźniak, G. (2025). Sustainable development of post-mining areas in the light of natural challenges. Zrównoważony rozwój terenów pogórniczych w świetle wyzwań przyrodniczych. Przegląd Geologiczny, 73(10), 932-934. https://doi.org/10.7306/2025.101

17.      Buzzi, J., Riaza, A., García-Meléndez, E., Weide, S., & Bachmann, M. (2014). Mapping Changes in a Recovering Mine Site with Hyperspectral Airborne HyMap Imagery (Sotiel, SW Spain). Minerals, 4(2), 313-329. https://doi.org/10.3390/min4020313

18.      Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017). Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art. IEEE Geoscience and Remote Sensing Magazine, 5(4), 37-78. https://doi.org/10.1109/MGRS.2017.2762087

19.      Obi Reddy, G.P., & Singh, S.K. (2018). Satellite Remote Sensing Sensors: Principles and Applications, (pp. 21-43). Springer International Publishing. https://doi.org/10.1007/978-3-319-78711-4_2

20.      Ali, I., Mashtaq, Z., Arif, S., Algarni, A.D., Soliman, N.F., & El-Shafai, W. (2023). Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing. Computer Systems Science and Engineering, 46(1), 303-319. https://doi.org/10.32604/csse.2023.034374

21.      Wang, X., Borsoi, R.A., Richard, C., & Chen, J. (2023). Deep Hyperspectral and Multispectral Image Fusion with Inter-Image Variability. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15. https://doi.org/10.1109/TGRS.2023.3273118

22.      Cozzolino, D., Williams, P.J., & Hoffman, L.C. (2023). An Overview of Pre-Processing Methods Available for Hyperspectral Imaging Applications. Microchemical Journal, 193, 109129. https://doi.org/10.1016/j.microc.2023.109129

23.      Lowe, A., Harrison, N., & French, A.P. (2017). Hyperspectral Image Analysis Techniques for the Detection and Classification of the Early Onset of Plant Disease and Stress. Plant Methods, 13(1), 80. https://doi.org/10.1186/s13007-017-0233-z

24.      Wang, X., Xu, H., Zhou, J., Fang, X., Shuai, S., & Yang, X. (2024). Analysis of Vegetation Canopy Spectral Features and Species Discrimination in Reclamation Mining Area Using in Situ Hyperspectral Data. Remote Sensing, 16(13), 2372. https://doi.org/10.3390/rs16132372

25.      Liu, Y., Liu, Y., & Pan, X. (2025). Hyperspectral Classification of Grasslands for Sustainable Management Using Feature Fusion GRCNet. Sustainability, 17(5), 1804. https://doi.org/10.3390/su17051804

26.      Wang, W., Liu, R., Gan, F., Zhou, P., Shang, X., & Ding, L. (2021). Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China. Remote Sensing, 13(7), 1350. https://doi.org/10.3390/rs13071350

27.      Cole, B., McMorrow, J., & Evans, M. (2014). Empirical Modeling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring. Remote Sensing, 6(1), 716-739. https://doi.org/10.3390/rs6010716

28.      Lévesque, J., & Staenz, K. (2008). Monitoring Mine Tailings Revegetation Using Multitemporal Hyperspectral Image Data. Canadian Journal of Remote Sensing, 34(sup1), S172-S186. https://doi.org/10.5589/m07-068

29.      Jiménez, M., & Díaz-Delgado, R. (2015). Towards a Standard Plant Species Spectral Library Protocol for Vegetation Mapping: A Case Study in the Shrubland of Doñana National Park. ISPRS International Journal of Geo-Information, 4(4), 2472-2495. https://doi.org/10.3390/ijgi4042472

30.      Ogen, Y., Denk, M., Glaesser, C., & Eichstaedt, H. (2022). A Novel Method for Predicting the Geochemical Composition of Tailings with Laboratory Field and Hyperspectral Airborne Data Using a Regression and Classification-Based Approach. European Journal of Remote Sensing, 55(1), 453-470. https://doi.org/10.1080/22797254.2022.2104173

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