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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 5, pp. 273-281

Automatic mapping of vegetation types using remote sensing data for mining areas

S.A. Orlov 1 , K.S. Tsygulev 1 , R.A. Sekrieru 1 , A.A. Avershin 1 , T.V. Kozhevnikova 1 , S.I. Smagin 1 
1 Computing Center FEB RAS, Khabarovsk, Russia
Accepted: 17.06.2026
DOI: 10.21046/2070-7401-2026-23-5-273-281
This paper presents the results of research aimed at automating the process of vegetation type mapping for environmental monitoring of mining areas in the Russian Far East using multispectral satellite imagery. The following vegetation types are identified: shrubs and grasses; coniferous, deciduous, and mixed forests. The input data include Sentinel-2 satellite images acquired during peak vegetation and during the period from the end of leaf fall to the onset of snow cover, as well as Global Forest Canopy Height 2019 canopy height maps. The methodology employs a fully connected neural network trained on specially prepared datasets generated using a developed vegetation type recognition algorithm. In total, 12 dataset variants were created containing different combinations of input data, including satellite imagery, calculated vegetation indices, and forest presence masks derived from canopy height maps. The vegetation type map produced by the neural network enables a preliminary assessment of biocenosis conditions based on satellite imagery, which is highly relevant for environmental monitoring of mining territories.
Keywords: satellite, data, neural network, machine learning, mapping, type of vegetation
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References:

  1. Belova E. I., Ershov D. V., Using Landsat time series for assessing reforestation on clear cuts in Bryansk region, Forest Science Issues, 2019, V. 2, No. 4, 20 p. (in Russian), DOI: 10.31509/2658-607x-2019-2-4-1-20.
  2. Zhuchkov D. V., Fetisov D. M., Assesment of the vegetation cover modern state at Obluchye by using multispectral data, Regional Problems, 2024, V. 27, No. 1, pp. 112–124 (in Russian), DOI: 10.31433/2618-9593-2024-27-1-112-124.
  3. Kashnitskii A. V., Balashov I. V., Tolpin V. A., Bartalev S. A., Vega-Science tools for refining vegetation cover maps using satellite data of varying spatial resolution, Materialy 14-i Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 14th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2016, p. 83 (in Russian).
  4. Kovalev A. V., The use of satellite data to assess vegetation conditions in the oil-producing regions of Western Siberia, Problemy geologii i osvoeniya nedr: trudy 20-go Mezhdunarodnogo simpoziuma imeni akademika M. A. Usova studentov i molodykh uchenykh, posvyashchennogo 120-letiyu so dnya osnovaniya Tomskogo politekhnicheskogo universiteta: sbornik statei (Issues in Geology and Subsurface Development: Proc. 20th Intern. Symp. named after Academician M. A. Usov for Students and Young Scientists, dedicated to the 120th anniversary of the founding of Tomsk Polytechnic University), 2016, V. 2, pp. 155–157 (in Russian).
  5. Sultanova R. R., Baiturina R. R., Diarova S. V., Assessment of forest changes in the Southern Urals based on satellite imagery from different time periods, Russian Forestry J., 2025, No. 2, pp. 51–62 (in Russian), DOI: 10.37482/0536-1036-2025-2-51-62.
  6. Agarap A. F., Deep learning using Rectified Linear Units (ReLU), https://arxiv.org/, arXiv:1803.08375, 2018, 7 p., DOI: 10.48550/arXiv.1803.08375.
  7. Dedeoğlu M., Başayiğit L., Yüksel M., Kaya F., Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey, Environmental Monitoring and Assessment, 2020, V. 192, Iss. 1, Article 16, DOI: 10.1007/s10661-019-7989-8.
  8. Elhag M., Boteva S., Al-Amri N., Forest cover assessment using remote-sensing techniques in Crete Island, Greece, Open Geosciences, 2021, V. 13, Iss. 1, pp. 345–358, DOI: 10.1515/geo-2020-0235.
  9. Haboudane D., Miller J. R., Pattey E. et al., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, Remote Sensing of Environment, 2004, V. 90, Iss. 3, pp. 337–352, DOI: 10.1016/j.rse.2003.12.013.
  10. Jiang R., Liang J., Zhao Y. et al., Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China, Scientific Reports, 2021, V. 11, Article 13775, DOI: 10.1038/s41598-021-93328-z.
  11. Kayet N., Pathak K., Singh C. P. et al., Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for-environmental impact assessment in coal mining sites, Ecotoxicology and Environmental Safety, 2022, V. 239, Article 113650, DOI: 10.1016/j.ecoenv.2022.113650.
  12. Maxwell A. E., Warner T. A., Guillén L. A., Accuracy assessment in convolutional neural network-based deep learning remote sensing studies — Part 1: Literature review, Remote Sensing, 2021, V. 13, Iss. 13, Article 2450, DOI: 10.3390/rs13132450.
  13. Potapov P., Li X., Hernandez-Serna A. et al., Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensing of Environment, 2021, V. 253, Article 112165, DOI: 10.1016/j.rse.2020.112165.
  14. Shi T., Xu H., Derivation of tasseled cap transformation coefficients for Sentinel-2 MSI at-sensor reflectance data, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2019, V. 12, Iss. 10, pp. 4038–4048, DOI: 10.1109/JSTARS.2019.2938388.
  15. Suárez-Fernández G. E., Martínez-Sánchez J., Arias P., Assessment of vegetation indices for mapping burned areas using a deep learning method and a comprehensive forest fire dataset from Landsat collection, Advances in Space Research, 2025, V. 75, Iss. 2, pp. 1665–1685, DOI: 10.1016/j.asr.2024.12.001.
  16. Varouchakis E. A., Komnitsas K., Galetakis M., Spatiotemporal analysis of vegetation health and moisture dynamics in rehabilitated mining quarries using satellite imagery, Environmental Processes, 2025, V. 12, Iss. 3, Article 45, DOI: 10.1007/s40710-025-00781-3.