Abstract
Studying the dynamics of groundwater in mining areas is important from the perspective of impact on ecosystems. This study determines water stress for subarctic mining areas using modern methods—remote sensing, machine learning, GIS. The study is carried out using the example of the Kostomuksha mining cluster, located in the northwestern part of the Republic of Karelia, Russia. Water stress was studied based on changes in NDMI in different vegetation land cover classes. To bring different values of remote sensing data to a single scale, the method of normalization by pseudo-invariant features (PIF) was applied. Landscape, climatic, topographic, and anthropogenic factors that control changes in humidity were studied, for a total of 15 variables. The importance of predictors was assessed using the recursive feature elimination method. The most important predictors are vegetation LCC, valley depth, distance to road, elevation, and KdistQ. The topographical features of the area have a large influence on the natural dynamics of NDMI. The greatest negative changes occur on steep slopes, as well as hills and ridges. The accumulation of water in the valleys forms the positive dynamics of NDMI. The interaction of the influence of mining with natural patterns of changes in vegetation humidity has been studied. The greatest negative changes are manifested for all vegetation classes within the newly formed groundwater depression cones. In areas of previously formed depression cones, the adaptation of vegetation to water stress is noted. Deciduous vegetation in wetlands has been determined to be most sensitive to anthropogenic stress.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All relevant raw data used in this study are available from the primary author.
References
Al-Abadi AM, Fryar AE, Rasheed AA, Pradhan B (2021) Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq. Environ Earth Sci 80:1–22. https://doi.org/10.1007/S12665-021-09725-0
Bartold M, Kluczek M (2023) A machine learning approach for mapping chlorophyll fluorescence at inland wetlands. Remote Sensing 15:2392. https://doi.org/10.3390/rs15092392
Bhattacharya A, Routh J, Jacks G et al (2006) Environmental assessment of abandoned mine tailings in Adak, Västerbotten district (Northern Sweden). Appl Geochem 21:1760–1780. https://doi.org/10.1016/J.APGEOCHEM.2006.06.011
Boori MS, Choudhary K, Kupriyanov A (2019) Mapping of groundwater potential zone based on remote sensing and GIS Techniques: a case study of Kalmykia Russia. Opt Mem Neural Netw 28:36–49. https://doi.org/10.3103/S1060992X1901003X
Bradshaw CJA, Warkentin IG (2015) Global estimates of boreal forest carbon stocks and flux. Glob Planet Change 128:24–30. https://doi.org/10.1016/J.GLOPLACHA.2015.02.004
Brumbaugh WG, Morman SA, May TW (2011) Concentrations and bioaccessibility of metals in vegetation and dust near a mining haul road, Cape Krusenstern National Monument, Alaska. Environ Monit Assess 182:325–340. https://doi.org/10.1007/s10661-011-1879-z
Cai Z, Fan C, Chen F, Li X (2021) Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): an effective normalization method to evaluate urbanization impacts on land surface temperature changes. Atmosphere (Basel) 12:1540. https://doi.org/10.3390/atmos12111540
Carabassa V, Ortiz O, Alcañiz JM (2019) RESTOQUARRY: Indicators for self-evaluation of ecological restoration in open-pit mines. Ecol Indic 102:437–445. https://doi.org/10.1016/J.ECOLIND.2019.03.001
Chambers ME, Fornwalt PJ, Malone SL, Battaglia MA (2016) Patterns of conifer regeneration following high severity wildfire in ponderosa pine—dominated forests of the Colorado Front Range. For Ecol Manage 378:57–67. https://doi.org/10.1016/J.FORECO.2016.07.001
Chen X, Vierling L, Deering D (2005) A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sens Environ 98:63–79. https://doi.org/10.1016/j.rse.2005.05.021
Construction of an ore-stripping complex of cyclic-flow technology equipment for the central section of the Kostomuksha quarry of ferruginous quartzite deposits. Environmental impact assessment (2019). Project. SPb-Giproshakht, St. Petersburg.
Dabrowska-Zielinska K, Budzynska M, Tomaszewska M, Bartold M, Gatkowska M (2015) The study of multifrequency microwave satellite images for vegetation biomass and humidity of the area under Ramsar convention. In Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26–31 July 2015. pp. 5198–5200. https://doi.org/10.1109/IGARSS.2015.7327005
Dąbrowska-Zielińska K, Misiura K, Malińska A, Gurdak R, Grzybowski P, Bartold M, Kluczek M (2022) Spatiotemporal estimation of gross primary production for terrestrial wetlands using satellite and field data. Remote Sens Appl Soc Environ 27:100786. https://doi.org/10.1016/j.rsase.2022.100786
Demidov IN, Shelekhova TS (2006) Diatomites of Karelia (features of formation, distribution, prospects of use). Petrozavodsk, Karelian Scientific Center of the Russian Academy of Sciences.
Dong S, Feng H, Xia M et al (2020) Spatial–temporal evolutions of groundwater environment in prairie opencast coal mine area: a case study of Yimin Coal Mine, China. Environ Geochem Health 42:3101–3118. https://doi.org/10.1007/s10653-020-00544-z
Du Y, Teillet PM, Cihlar J (2002) Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sens Environ 82:123–134. https://doi.org/10.1016/S0034-4257(02)00029-9
Development of tailings facilities of JSC Karelsky Okatysh for the period 2018–2043., (2018) Project documentation. JSC “Mekhanobr Engineering” St. Petersburg.
De Ferranti J (2020) Digital elevation data. http://viewfinderpanoramas.org/dem3.html Accessed 15 December 2020
Elberling B, Asmund G, Kunzendorf H, Krogstad EJ (2002) Geochemical trends in metal-contaminated fiord sediments near a former lead–zinc mine in West Greenland. Appl Geochem 17:493–502. https://doi.org/10.1016/S0883-2927(01)00119-6
Galhardi JA, Bonotto DM (2016) Hydrogeochemical features of surface water and groundwater contaminated with acid mine drainage (AMD) in coal mining areas: a case study in southern Brazil. Environ Sci Pollut Res 23:18911–18927. https://doi.org/10.1007/s11356-016-7077-3
Gao BC (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Goswami S, Rai AK (2024) Understanding the vulnerability of coastal groundwater aquifers in Odisha. India Environ Earth Sci 83:44. https://doi.org/10.1007/s12665-023-11358-4
Hamdani N, Baali A (2019) Height Above Nearest Drainage (HAND) model coupled with lineament mapping for delineating groundwater potential areas (GPA). Groundw Sustain Dev 9:100256. https://doi.org/10.1016/j.gsd.2019.100256
Heikkinen P, Korkka-Niemi K, Lahti M et al (2002) Groundwater and surface water contamination in the area of the Hitura nickel mine, Western Finland. Env Geol 42:313–329. https://doi.org/10.1007/s00254-002-0525-z
Henselowsky F, Rölkens J, Kelterbaum D, Bubenzer O (2021) Anthropogenic relief changes in a long-lasting lignite mining area (‘Ville’, Germany) derived from historic maps and digital elevation models. Earth Surf Proc Land 46:1725–1738. https://doi.org/10.1002/esp.5103
Hladky R, Lastovicka J, Holman L, Stych P (2020) Evaluation of the influence of disturbances on forest vegetation using landsat time series; a case study of the low Tatras National Park. Eur J Remote Sens 53:40–66. https://doi.org/10.1080/22797254.2020.1713704
Hofgaard A, Rees G, Tømmervik H et al (2010) Role of disturbed vegetation in mapping the boreal zone in northern Eurasia. Appl Veg Sci 13:460–472. https://doi.org/10.1111/J.1654-109X.2010.01086.X
Horler DNH, Ahern FJ (1986) Forestry information content of thematic mapper data. Int J Remote Sens 7(3):405–428
Hydrogeology of the USSR (1971) Murmansk region and Karelian ASSR, vol XXVII. Bosom, Moscow
Ivanov NM, Korsakova MA, Dudareva GA (2021) State geological map of the Russian Federation, scale 1: 200,000. Karelian series. Sheet Q-36-XXVII,XXVIII (Yuma). Explanatory letter. Moscow, VSEGEI.
Jin S, Sader SA (2005) Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens Environ 94:364–372. https://doi.org/10.1016/J.RSE.2004.10.012
Kayet N, Pathak K, Chakrabarty A et al (2019) Assessment of foliar dust using hyperion and landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas. J Clean Prod 218:993–1006. https://doi.org/10.1016/j.jclepro.2019.01.305
Kulikov VS, Svetov SA, Slabunov AI, Kulikova VV, Polin AK, Golubev AI, Gorkovets VY, Ivashchenko VI, Gogolev MA (2017) Geological map of Southeastern Fennoscandia (scale 1:750,000): a new approach to map compilation. Trans. KarRC RAS. 2:3–41. https://doi.org/10.17076/geo444
Kuuluvainen T, Gauthier S (2018) Young and old forest in the boreal: critical stages of ecosystem dynamics and management under global change. For Ecosyst 5:1–15. https://doi.org/10.1186/S40663-018-0142-2/FIGURES/4
Landsat Collections https://www.usgs.gov/landsat-missions/landsat-collections. Accessed January 10, 2024
Li J, Liang J, Wu Y et al (2021) Quantitative evaluation of ecological cumulative effect in mining area using a pixel-based time series model of ecosystem service value. Ecol Indic 120:106873. https://doi.org/10.1016/j.ecolind.2020.106873
Lian H, Yi H, Yang Y et al (2021) Impact of coal mining on the moisture movement in a vadose zone in open-pit mine areas. Sustainability 13:4125. https://doi.org/10.3390/su13084125
Liu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, Lacasse S (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front 12:385–393. https://doi.org/10.1016/j.gsf.2020.04.014
Machowski R, Rzetala MA, Rzetala M, Solarski M (2016) Geomorphological and hydrological effects of subsidence and land use change in industrial and Urban areas. Land Degrad Dev 27:1740–1752. https://doi.org/10.1002/ldr.2475
Madasa A, Orimoloye IR, Ololade OO (2021) Application of geospatial indices for mapping land cover/use change detection in a mining area. J Afr Earth Sc 175:104108. https://doi.org/10.1016/j.jafrearsci.2021.104108
Mallick J, Talukdar S, Ben KN et al (2021) A novel hybrid model for developing groundwater potentiality model using high resolution digital elevation model (DEM) derived factors. Water (Basel) 13:2632. https://doi.org/10.3390/w13192632
Mallick J, Talukdar S, Ahmed M (2022) Combining high resolution input and stacking ensemble machine learning algorithms for developing robust groundwater potentiality models in Bisha watershed, Saudi Arabia. Appl Water Sci. https://doi.org/10.1007/S13201-022-01599-2
Masroor M, Rehman S, Sajjad H et al (2021) Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm. Groundw Sustain Dev 13:100554. https://doi.org/10.1016/j.gsd.2021.100554
Menard S (2002) Applied logistic regression analysis. Sage Publications, Thousand Oaks
Mosavi A, Sajedi Hosseini F, Choubin B et al (2021) Ensemble boosting and bagging based machine learning models for groundwater potential prediction. Water Resour Manage 35:23–37. https://doi.org/10.1007/s11269-020-02704-3
Närhi P, Räisänen ML, Sutinen ML, Sutinen R (2012) Effect of tailings on wetland vegetation in Rautuvaara, a former iron–copper mining area in northern Finland. J Geochem Explor 116–117:60–65. https://doi.org/10.1016/J.GEXPLO.2012.03.005
Neff BP, Rosenberry DO, Leibowitz SG et al (2019) A Hydrologic landscapes perspective on groundwater connectivity of depressional wetlands. Water (Basel) 12:50. https://doi.org/10.3390/w12010050
Neitlich PN, Ver Hoef JM, Berryman SD, Mines A, Geiser LH, Hasselbach LM et al (2017) Trends in spatial patterns of heavy metal deposition on national park service lands along the Red Dog Mine haul road, Alaska, 2001–2006. PLoS ONE 12(5):e0177936. https://doi.org/10.1371/journal.pone.0177936
Niemela J, Ekman I, Lukashov A (1993) Quaternary deposits of Finland and Nɨrthwestern part of Russian Federation and their resources. Scale 1:1000 000. Helsinki: Geological Survey of Finland, Kar. Scientific Center RAS.
Ochtyra A (2020) Forest disturbances in Polish Tatra mountains for 1985–2016 in relation to topography, stand features, and protection zone. Forests 11:579. https://doi.org/10.3390/f11050579
Olthof I, Latifovic R (2007) Short-term response of arctic vegetation NDVI to temperature anomalies. Int J Remote Sens. 28(21):4823. https://doi.org/10.1080/01431160701268996
Pepliński B, Czubak W (2021) The influence of opencast lignite mining dehydration on plant production—a methodological study. Energies. 14:1917. https://doi.org/10.3390/EN14071917
Pugh RE, Dick DG, Fredeen AL (2002) Heavy metal (Pb, Zn, Cd, Fe, and Cu) Contents of Plant Foliage near the Anvil Range Lead/Zinc Mine, Faro, Yukon Territory. Ecotoxicol Environ Saf 52:273–279. https://doi.org/10.1006/EESA.2002.2201
Raevsky BV, Tarasenko VV, Petrov NV (2022) Inventory of the Kostomukshskiy strict nature reserve vegetation using landsat images. Sovr Probl Distantsionnogo Zondirovaniya Zemliiz Kosm. 19(3):47. https://doi.org/10.21046/2070-7401-2022-19-3-47-61
Rees WG, Golubeva EI, Tutubalina OV et al (2020) Relation between leaf area index and NDVI for subarctic deciduous vegetation. Int J Remote Sen. 41:8573–8589. https://doi.org/10.1080/01431161.2020.1782505
Sahour S, Khanbeyki M, Gholami V et al (2023) Evaluation of machine learning algorithms for groundwater quality modeling. Environ Sci Pollut Res 30:46004–46021. https://doi.org/10.1007/s11356-023-25596-3
Schott JR, Salvaggio C, Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sens Environ 26:1–16. https://doi.org/10.1016/0034-4257(88)90116-2
Schueler CF, Salomonson VV (1985) Landsat image data quality studies. Adv Space Res 5:1–11. https://doi.org/10.1016/0273-1177(85)90251-0
Seeyan S, Merkel B, Abo R (2014) Investigation of the relationship between groundwater level fluctuation and vegetation cover by using NDVI for Shaqlawa basin, Kurdistan region—Iraq. J Geogr Geol. https://doi.org/10.5539/jgg.v6n3p187
Sepehrara A, Javadi S, Hosseini A, Karimi N (2023) Prediction of vulnerability map regarding to the dynamic parameters and land use changes. Environ Earth Sci 82:503. https://doi.org/10.1007/s12665-023-11120-w
Šimanauskienė R, Linkevičienė R, Bartold M, Dąbrowska-Zielińska K, Slavinskienė G, Veteikis D, Taminskas J (2019) Peatland degradation: the relationship between raised bog hydrology and normalized difference vegetation index. Ecohydrology 12:2159. https://doi.org/10.1002/eco.2159
Simms ÉL, Ward H (2013) Multisensor NDVI-based monitoring of the Tundra-Taiga Interface (Mealy Mountains, Labrador, Canada). Remote Sens. 5:1066–1090. https://doi.org/10.3390/RS5031066
Singha S, Pasupuleti S, Singha SS, Kumar S (2020) Effectiveness of groundwater heavy metal pollution indices studies by deep-learning. J Contam Hydrol 235:103718. https://doi.org/10.1016/j.jconhyd.2020.103718
Stoyanov A (2022) Application of tasseled cap transformation of sentinel-2—MSI data for forest monitoring and change detection on territory of natural park “BLUE STONES.” Environ Sci Proc 22(1):42. https://doi.org/10.3390/IECF2022-13073
Syariz MA, Lin B-Y, Denaro LG et al (2019) Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery. ISPRS J Photogramm Remote Sens 147:56–64. https://doi.org/10.1016/j.isprsjprs.2018.11.007
Talukdar S, Mallick J, Sarkar SK et al (2022) Novel hybrid models to enhance the efficiency of groundwater potentiality model. Appl Water Sci 12:1–22. https://doi.org/10.1007/S13201-022-01571-0/TABLES/1
Taylor AR, Gao B, Chen HYH (2020) The effect of species diversity on tree growth varies during forest succession in the boreal forest of central Canada. For Ecol Manage 455:117641. https://doi.org/10.1016/j.foreco.2019.117641
Thanh NN, Chotpantarat S, Ha NT, Nguyen HT (2023) Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy. Environ Geochem Health 45:4583–4602. https://doi.org/10.1007/s10653-023-01512-z
Tianyu Z, Longcang S, Chengpeng L, Bo L (2023) Attenuation of the contribution of groundwater to a wetland caused by groundwater overexploitation. Authorea. https://doi.org/10.22541/au.168020464.43869946/v1
Tolvanen A, Eilu P, Juutinen A et al (2019) Mining in the Arctic environment—a review from ecological, socioeconomic and legal perspectives. J Environ Manage 233:832–844. https://doi.org/10.1016/j.jenvman.2018.11.124
Tutubalina OV, Rees WG (2001) Vegetation degradation in a permafrost region as seen from space: Noril’sk (1961–1999). Cold Reg Sci Technol 32:191–203. https://doi.org/10.1016/S0165-232X(01)00049-0
Vasuki Y, Yu L, Holden E-J et al (2019) The spatial-temporal patterns of land cover changes due to mining activities in the Darling Range, Western Australia: a visual analytics approach. Ore Geol Rev 108:23–32. https://doi.org/10.1016/j.oregeorev.2018.07.001
Virtanen T, Mikkola K, Patova E, Nikula A (2002) Satellite image analysis of human caused changes in the tundra vegetation around the city of Vorkuta, north-European Russia. Environ Pollut 120:647–658. https://doi.org/10.1016/S0269-7491(02)00186-0
Vorobiev ON, Kurbanov EA, Demisheva EN et al (2019) Remote monitoring of forest ecosystems sustainability. Volga State University of Technology, Yoshkar-Ola
Wang H, Zhang L, Yin K, Luo H, Li J (2021) Landslide identification using machine learning. Geosci Front. 12:351–364. https://doi.org/10.1016/j.gsf.2020.02.012
Wei X, Giles-Hansen K, Spencer SA et al (2022) Forest harvesting and hydrology in boreal forests: under an increased and cumulative disturbance context. For Ecol Manage 522:120468. https://doi.org/10.1016/j.foreco.2022.120468
Weiss AD (2001) Topographic position and landforms analysis. Paper presented at the meeting of the poster presentation. ESRI user conference, San Diego
Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80:385–396. https://doi.org/10.1016/S0034-4257(01)00318-2
Xi-jun M, Zhao-hua L, Jian-long C (2008) Ecological risk assessment of open coal mine area. Environ Monit Assess 147:471–481. https://doi.org/10.1007/s10661-008-0215-8
Xu X, Gu X, Wang Q et al (2018) Production scheduling optimization considering ecological costs for open pit metal mines. J Clean Prod 180:210–221. https://doi.org/10.1016/J.JCLEPRO.2018.01.135
Yihdego Y, Drury L (2016) Mine dewatering and impact assessment in an arid area: case of Gulf region. Environ Monit Assess 188:634. https://doi.org/10.1007/s10661-016-5542-6
Zenkov IV, Yuronen YP, Nefedov BN, Zayats VV (2017) Remote monitoring of ecological state of disturbed lands in the area of Trojanovo open pit coal mine in Bulgaria. Eurasian Min. https://doi.org/10.17580/em.2017.01.10
Zhang M, Wang J, Li S et al (2020) Dynamic changes in landscape pattern in a large-scale opencast coal mine area from 1986 to 2015: a complex network approach. Catena (Amst) 194:104738. https://doi.org/10.1016/j.catena.2020.104738
Zhang J, Zhang J, Deng Y et al (2023) Quantitative evaluation of ecological and environmental impacts caused by future mining. Ore Geol Rev 162:105672. https://doi.org/10.1016/J.OREGEOREV.2023.105672
Zhou X, Wen H, Zhang Y, Xu J, Zhang W (2021) Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci Front. 12:101211. https://doi.org/10.1016/j.gsf.2021.101211
Zhou X, Yang W, Luo K, Tang X (2022) Estimation of aboveground vegetation water storage in natural forests in Jiuzhaigou National Nature Reserve of China using machine learning and the combination of landsat 8 and sentinel-2 data. Forests 13:507. https://doi.org/10.3390/f13040507
Acknowledgements
The study was carried out with the support of the Institute of Geology of the Karelian Research Center.
Author information
Authors and Affiliations
Contributions
NK: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Krutskikh, N. Detection of water stress due to the mining of ferruginous quartzite in a subarctic region. Environ Earth Sci 83, 324 (2024). https://doi.org/10.1007/s12665-024-11636-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-024-11636-9