Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 23, 2026
Number 1 Number 2 Number 3 Number 4
Volume 22, 2025
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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. 4, pp. 242-257

Application of satellite forest indices and global land cover data to regional shelterbelt mapping

S.S. Shinkarenko 1 , S.A. Bartalev 1 , A.V. Kashnitskii 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 16.06.2026
DOI: 10.21046/2070-7401-2026-23-4-242-257
The relevance of assessing the condition and area of field-protective forest belts (shelterbelts) in Russia is driven by their ongoing degradation and absence of a unified inventory system. The aim of this study is to determine optimal methods for shelterbelt mapping at the regional level using Sentinel-2 satellite data. The research was conducted in Volgograd Region characterized by a wide range of soil and climatic conditions. We compared the results of mapping tree and shrub vegetation (TSV) obtained with the Bi-Seasonal Forest Index (BSFI), the Forest Index (FI), and seven global land cover products (ESA (European Space Agency) WorldCover, ESRI (Environmental Systems Research Institute) LandCover, FROM-GLC10 (Finer Resolution Observation and Monitoring of Global Land Cover), Dynamic World, GLC_FCS10 (Global 10 m Land-Cover dataset with Fine Classification System), Copernicus LCFM (Global Land Cover and Tropical Forest Mapping and Monitoring service), and JRC_GFC2020 (Joint Research Centre Global map of Forest Cover)). Validation of the indices against aerial photography showed that BSFI approximates canopy cover more accurately (R2 = 0.80) than does FI (R2 = 0.73). To identify shelterbelts specifically, 50-m buffer zones were constructed around agricultural fields, thereby minimizing confusion between tree and shrub vegetation and highly productive herbaceous vegetation. The results indicate that global land cover products systematically underestimate the protective forest cover of cropland (0.7–1.5 %), compared with official statistics (2.2 %). The BSFI demonstrated the best agreement with statistical data (cropland protective forest cover of 2.2 %), which is attributed to its ability to detect suppressed stands under arid conditions through the use of winter satellite images with snow cover. A hybrid approach combining ESA and Copernicus maps is proposed to increase completeness when generating a reference sample for classifying satellite remote sensing data by applying a TSV mask derived from forest indices; this requires further research. The findings can serve as a basis for assessing shelterbelt area and protective forest cover of cropland at both the regional and national levels.
Keywords: protective afforestation, remote sensing, mapping, Volgograd Region, tree and shrub vegetation, agroforestry
Full text

References:

  1. Antonov S. A., Spatial analysis of protective forest plantations based on geographic information technologies and remote sensing data, InterCarto. InterGIS. GI Support of Sustainable Development of Territories: Proc. Intern. Conf., Moscow: Moscow University Press, 2020, V. 26, Pt. 2, pp. 408–420 (in Russian), DOI: 10.35595/2414-9179-2020-2-26-408-420.
  2. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p. (in Russian).
  3. Vasilchenko A. A., Vypritskiy A. A., Mapping forest plantations of the Volgograd region according to remote sensing data using the BSFI and NDWI indices, Geodeziya i kartografiya, 2023, V. 84, No. 10, pp. 39–49 (in Russian), DOI: 10.22389/0016-7126-2023-1000-10-39-49.
  4. Vypritskiy A. A., Shinkarenko S. S., Analysis of soil and climatic factors influence on the protective forest condition based on Sentinel-2 data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 5, pp. 147–163 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-147-163.
  5. Dubenok N. N., Tanyukevich V. V., Baboshko O. I., Phytosaturation of field protecting forest stripes and their ameliorative influence on cropping power of agricultural crops, Vestnik rossiiskoi sel’skokhozyaistvennoi nauki, 2016, No. 1, pp. 27–30 (in Russian).
  6. Kashnitskii A. V., Loupian E. A., Archive of information products on surface type observation frequency based on Sentinel-2 data and its possible applications, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 2, pp. 335–342 (in Russian), DOI: 10.21046/2070-7401-2025-22-2-335-342.
  7. Kashnitskii A. V., Burtsev M. A., Proshin A. A., Technology to create cloud-free composites from Sentinel-2 satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 5, pp. 76–85 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-76-85.
  8. Kulik K. N., Ivanov A. L., Rulev A. S., Svintsov I. P., Pavlovskiy E. S., Petrov V. I., Barabanov A. T., Manaenkov A. S., Vasilev Yu.I., Jdanov Yu.N., Zikov I. G., Kulik N. F., Kryuckov S. N., Malanina Z. I., Semenyutina A. V., Suchorukich Yu.I., Schulga V. D., Yuferev V. G., Strategiya razvitiya zashchitnogo lesorazvedeniya v Rossiiskoi Federatsii na period do 2025 goda, pererabotannaya i dopolnennaya (The strategy of protective forestation development in Russian Federation for a period till 2025 year, remade and supplemented), Volgograd: FSC of agroecology RAS, 2018, 36 p. (in Russian).
  9. Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitskiy A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, V. 12, No. 5, pp. 263–284 (in Russian).
  10. Narozhnyaya A. G., Chendev Yu. G., The study of the modern ecological state of shelterbelts using GIS and remote sensing data, InterCarto. InterGIS. GI Support of Sustainable Development of Territories: Proc. Intern. Conf., Moscow: Moscow University Press, 2020, V. 26, Pt. 2, pp. 54–65 (in Russian), DOI: 10.35595/2414-9179-2020-2-26-54-65.
  11. Natsional’nyi atlas pochv Rossiiskoi Federatsii (National soil atlas of the Russian Federation), S. A. Shoba (ed.), Moscow: Astrel Publishing House, 2011, 632 p. (in Russian).
  12. Natsional’nyi doklad “Global’nyi klimat i pochvennyi pokrov Rossii: opustynivanie i degradatsiya zemel’, institutsional’nye, infrastrukturnye, tekhnologicheskie mery adaptatsii (sel’skoe i lesnoe khozyaistvo)” (Global climate and soil cover of Russia: Desertification and land degradation, institutional, infrastructural, technological adaptation measures (agriculture and forestry). National report), R. S.-Kh. Edel’geriev (ed.), V. 2, Moscow: OOO “Izd. MBA”, 2019, 476 p. (in Russian).
  13. Rulev A. S., Kosheleva O. Yu., Shinkarenko S. S., Assessment of woodiness in agrolandscapes of the Southern Volga Upland according to NDVI, Izvestiya Nizhnevolzhskogo agrouniversitetskogo kompleksa: nauka i vysshee professional’noe obrazovanie, 2016, No. 4 (44), pp. 32–39 (in Russian).
  14. Silova V. A., Influence of forest reclamation improvement on the production area yield in the dry steppe zone, Nauchnyi zhurnal Rossiiskogo NII problem melioratsii, 2021, V. 11, No. 2, pp. 68–81 (in Russian), DOI: 10.31774/2222-1816-2021-11-2-68-81.
  15. Sinelnikova K. P., Berdengalieva A. N., Matveev Sh. et al., Mapping arable lands in agricultural landscapes of Volgograd Region according to remote sensing data, Izvestiya, Atmospheric and Oceanic Physics, 2023, V. 59, No. 10, pp. 1494–1502, DOI: 10.1134/S0001433823120228.
  16. Terekhin E. A., Effect of abandoned agricultural lands forest cover on Sentinel-2 spectral response in forest-steppe natural zone, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 4, pp. 223–235 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-223-235.
  17. Terekhov A. G., Makarenko N. G., Pak I. T., Automatic classification algorithm of quick bird images in the problem of evaluating of forest completeness, Komp’yuternaya optika, 2014, V. 38, No. 3, pp. 580–583 (in Russian), DOI: 10.18287/0134-2452-2014-38-3-580-583.
  18. Cheplyanskii I. Ya., Turchin T. Ya., Ermolova A. S., Remote monitoring of state forest shelterbelts in the steppe zone of European Russia, Izvestiya vuzov. Lesnoi zhurnal, 2022, No. 3, pp. 44–59 (in Russian), DOI: 10.37482/0536-1036-2022-3-44-59.
  19. Shinkarenko S. S., Bartalev S. A., Possibilities of assessing forest belts canopy closure using Sentinel-2 based Bi-Seasonal Forest Index and UAV data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, V. 20, No. 1, pp. 189–202 (in Russian), DOI: 10.21046/2070-7401-2023-20-1-189-202.
  20. Shinkarenko S. S., Bartalev S. A., Vasilchenko A. A., Method for protective forest plantations mapping based on multi-temporal high spatial resolution satellite images and Bi-Season Forest Index, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 4, pp. 207–222 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-207-222.
  21. Begimova M., Climate indicators for forest landing and evaluation of forest shelterbelts, E3S Web of Conf., 2021, V. 227, Article 02004, DOI: 10.1051/e3sconf/202122702004.
  22. Bourgoin C., Verhegghen A., Carboni S. et al., GFC2020: a global map of forest land use for year 2020 to support the EU Deforestation Regulation, Earth System Science Data, 2026, V. 18, No. 2, pp. 1331–1365, DOI: 10.5194/essd-18-1331-2026.
  23. Brown C. F., Brumby S. P., Guzder-Williams B. et al., Dynamic world, near real-time global 10 m land use land cover mapping, Scientific Data, 2022, V. 9, Article 251, DOI: 10.1038/s41597-022-01307-4.
  24. Gong P., Liu H., Zhang M. et al., Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017, Science Bull., 2019, V. 64, pp. 370–373, DOI: 10.1016/j.scib.2019.03.002.
  25. Hansen M. C., Potapov P. V., Moore R. et al., High-resolution global maps of 21st-century forest cover change, Science, 2013, V. 342, pp. 850–853, DOI: 10.1126/science.1244693.
  26. Karra K., Kontgis C, Statman-Weil Z. et al., Global land use/land cover with Sentinel 2 and deep learning, 2021 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), 2021, pp. 4704–4707, DOI: 10.1109/IGARSS47720.2021.9553499.
  27. Koshelev A. V., Tkachenko N. A., Shatrovskaya M. O., Decoding of forest belts using satellite images, IOP Conf. Series: Earth and Environmental Science, 2021, V. 875, Article 012065, DOI: 10.1088/1755-1315/875/1/012065.
  28. Kulik K. N., Barabanov A. T., Manaenkov A. S., Forecasting the development of protective afforestation in Russia until 2020, Studies on Russian Economic Development, 2015, V. 26, No. 4, pp. 351–358, DOI: 10.1134/S1075700715040073.
  29. Land Cover and Forest Monitoring (LCFM). Copernicus Land Monitoring Service Land Cover 2020, version 1, NERC EDS Centre for Environmental Data Analysis, 2025, DOI: 10.2909/602507b2-96c7-47bb-b79d-7ba25e97d0a9.
  30. Loupian E., Bourtsev M., Proshin A. et al., Usage experience and capabilities of the VEGA-Science system, Remote Sensing, 2022, V. 14, No. 1, Article 77, DOI: 10.3390/rs14010077.
  31. Pugacheva A. M., Functionality of zonal agroforestry systems on agricultural land of dry territories, Forests, 2023, V. 14, Article 2364, DOI: 10.3390/f14122364.
  32. Yang X., Li F., Fan W. et al., Evaluating the efficiency of wind protection by windbreaks based on remote sensing and geographic information systems, Agroforestry Systems, 2021, V. 95, pp. 353–365, DOI: 10.1007/s10457-021-00594-x.
  33. Ye W., Li X., Chen X., Zhang G., A spectral index for highlighting forest cover from remotely sensed imagery, Proc. SPIE. Land Surface Remote Sensing II, 2014. V. 9260, Article 92601L, DOI: 10.1117/12.2068775.
  34. Zanaga D., Van De Kerchove R., De Keersmaecker W. et al., ESA WorldCover 10 m 2020 v100, https://zenodo.org, 20.10.2021, DOI: 10.5281/zenodo.5571936.
  35. Zhang X., Liu L., Zhao T. et al., GLC_FCS10: global 10 m land-cover dataset with fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine, Earth System Science Data, 2025, V. 17, No. 8, pp. 4039–4062, DOI: 10.5194/essd-17-4039-2025.