Identification of land use structure in the zone of influence of the Soligorsk potash plant based on remote sensing data
Abstract
The spatiotemporal characteristics of five land use types – arable land, forests, meadows, wetlands and water bodies – over an area of 8100 km2 in the influence zone of the Soligorsk Potash Plant in Belarus using four vegetation indices (NDVI, GNDVI, SAVI and GCI) based on Sentinel-2A remote sensing data (March – September 2023) are analyzed in the article. The study was conducted on nine territorial blocks in the ArcGIS environment with the accuracy of land type interpretation using the weighted average method for 900 representative plots. The obtained results made it possible to refine the models for interpreting land use structure based on the share of land types, as well as the dynamics of vegetation indices during the growing season. The coefficients of determination (R2) of the four vegetation indices for forest, arable and meadow lands are as follows: NDVI (0.78–0.82) > GNDVI (0.75–0.80) > SAVI (0.73–0.79) > GCI (0.69–0.77). Spatial analysis of the NDVI and GNDVI indices specifies a significant influence of soil moisture within the boundaries of meadow lands, the role of agricultural intensity on arable lands and insufficient sensitivity of the SAVI index for interpreting water bodies and wetlands. The research results made it possible to assess the spatial heterogeneity of land use in the mining region for subsequent analysis of land and soil resource degradation processes.
About the Authors
Chen Jiang
Belarusian State University
Belarus
Belarus
Jiang Chen – Post Graduate Student
16, Leningradskaya Str., 220030, Minsk
A. N. Chervan
Belarusian State University
Belarus
Belarus
Chervan Alexander Nikolaevich – Ph. D. (Agriculture), Associate Professor, Head of Department of Soil Science and Geoinformatic, Faculty of Geography and Geoinformatic
16, Leningradskaya Str., 220030, Minsk
References
1. Chen Fu, Zhu Yanfeng, Luo Zhanbin, e. a. Soil-Vegetation System Resilience and Synergies – Trade-Offs in Reclaiming Surface Coal Mines on The Loess Plateau. Journal of Chian Coal Science, 2024, vol. 49, рр. 4950–4602. DOI: 10.13225/j.cnki.jccs.2024.0326. (in Chinese)
2. Yachao Guo, Yanli Huang, Junmeng Li, Shenyang Ouyang, Laiwei Wu, Wenyue Qi. Study on the influence of mining disturbance on the variation characteristics of vegetation index: A case study of Lingwu Mining Area. Environmental Development, 2023, vol. 45. DOI: 10.1016/j.envdev.2023.100811.
3. Li Haidong, Shen Weishou, Si Wantong, Yan Qingwu. Investigation of Driving Factors of Land Degradation in Mine Areas in China: Concept, Types and Approaches. Journal of Ecology and Rural Environment, 2015, vol. 3, рр. 445–451. DOI: 10.11934/j.issn.1673-4831.2015.04.001. (in Chinese)
4. Klimat i srednyaya pogoda kruglyj god v Soligorske [Climate and average weather all year round in Soligorsk]. 2025. Available at: https://ru.weatherspark.com/y/95110/%D0%9E%D0%B1%D1%8B%D1%87%D0%BD%D0%B0%D1%8F%D0%BF%D0%BE%D0%B3%D0%BE%D0%B4%D0%B0%D0%B2%D0%A1%D0%BE%D0%BB%D0%B8%D0%B3%D0%BE%D1%80%D1%81%D0%BA%D0%91%D0%B5%D0%BB%D0%B0%D1%80%D1%83%D1%81%D1%8C%D0%B2%D0%B5%D1%81%D1%8C-%D0%B3%D0%BE%D0%B4 (accessed 1 December 2025). (in Russian)
5. Oficial'nyj sajt Respubliki Belarus' [Official Website of the Republic of Belarus]. 2025. Available at: https://www.belarus.by/cn/business/brands-of-belarus (accessed 1 December 2025). (in Russian)
6. Zhang Yuxuan, Li Xutuo, Liu Mingyi, Gao Yongwei, Zhang Dandan. Analysis of Potash Resource Endowment and Investment Environment in Belarus. Northwestern Geology, vol. 5, рр. 306–317. DOI: 10.19751/j.cnki.61-1149/p.2022.03.025. (in Chinese)
7. Hui Qinghua, Quan Zhaoming, Zhang Huifang, e. a. Potassium extraction technology and mechanism by flotation in China's salt lake. Applied Chemical Industry, 2021, vol. 50, рр. 3414–3419. DOI: doi.org/10.16581/j.cnki.issn1671-3206.2021.12.007. (in Chinese)
8. Zhao Bochao, Chervan A. N. Spatial distribution of soils by salinization level in Soligorsk district of Belarus. Soil and Land Resources, 2024, vol. 1, рр. 5–12.
9. Zhang Xiaoman, Yong Qianxi, Qi Mengyao, Sun Zhifu, Cao Peiyi, Peng Weijun. Status and Prospect of Potassium Extracting from Potassium Feldspar. Conservation and Utilization of Mineral Resources, 2020, vol. 40, no. 4, рр. 172–178. DOI: 10.13779/j.cnki.issn1001-0076.2020.04.021. (in Chinese)
10. Vzglyady Evropy na Zemlyu [Copernicus Eurpes eyes on Earth]. 2025. Available at: https://www.copernicus.eu/en (accessed 1 December 2025). (in Russian)
11. Anatoly A. Gitelson, Yuri Gritz. Mark N. Merzlyak. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 2003, vol. 16, рр. 271–282. DOI: doi.org/10.1078/0176-1617-0088.
12. Li Fenling, Wang Li, Liu Jing, Chang Qingrui. Remote Sensing Estimation of SPAD Value for Wheat Leaf Based on GF-1 Data. Transactions of the Chinese Society for Agricultural Machinery, 2015, vol. 4, рр. 273–281. DOI: 10.6041/j.issn.1000-1298.2015.09.040. (in Chinese)
13. Wang Zhengxing, Liu Chuang, Huete Alfredo. Progress in Vegetation Index Research: From AVHRR-NDVI to MODIS-EVI. Acta Ecologica Sinica, 2003, vol. 2, рр. 979–987. (in Chinese)
14. Guo Yuchuan, He Ying, Li Xia. Vegetation coverage inversion and vegetation index optimization in arid areas based on MODIS. Remote Sensing of Land and Resources, 2011, vol. 2, рр. 115–118. (in Chinese)
15. Akhona Madasa, Israel R. Orimoloye, Olusola O. Ololade. Application of geospatial indices for mapping land cover/use change detection in a mining area. J. of African Earth Sciences, 2021, vol. 175, рр. 104–108. DOI: doi.org/10.1016/j.jafrearsci.2021.104108.
16. Bascietto Marco, Enrico Santangelo, Claudio Beni. Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status. Land, 2021, vol. 1, рр. 80. DOI: doi.org/10.3390/land10010080.
17. Orimoloye R., Ololade O. O. Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology. International J. of Environmental Science and Technology, 2020, vol. 1, рр. 4465–4480. DOI: 10.1007/s13762-020-02789-8.
18. Zhao Yihan, Hou Mengjing, Feng Qisheng, e. a. Remote sensing estimation of aboveground biomass of natural grassland in Menyuan, Qinghai based on Landsat 8 and random forests. J. of Grassland Science, 2022, vol. 3, pp. 1–14. (in Chinese)
19. Tang Xiying, Li Huazhe, Cui Lijuan, Zhao Xinsheng, Zhai Xiajie, Lei Yinru, Li Jing, Wang Jinzhi, Li Wei. Inversion of Wetland Plant Species Diversity Using UAV Hyperspectral Data. J. of Earth Information Science, 2024, vol. 2, pp. 1954–1974. DOI: 10.12082/dqxxkx.2024.240055. (in Chinese)
20. Liao Chenghao, Liu Xuehua. Comparative analysis of the identification effectiveness of water body identification indices from MODIS data. Remote sensing of land resources, 2008, vol. 4, pp. 22–27. (in Chinese)
21. Geomorfologicheskaya karta [Geomorphological map]. Natsional'nyi atlas Belarusi = National Atlas of Belarus. Minsk, Belkatografiya Publ., 2002, pp. 67. (in Russian)
Review
For citations:
Jiang Ch., Chervan A.N. Identification of land use structure in the zone of influence of the Soligorsk potash plant based on remote sensing data. Nature Management. 2025;(1):51-63. (In Russ.)
Views:
53
JATS XML
Email this article 