ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 207-222

Method for protective forest plantations mapping based on multi-temporal high spatial resolution satellite images and Bi-Season Forest Index

S.S. Shinkarenko 1, 2 , S.А. Bartalev 1 , A.A. Vasilchenko 2 
1 Space Research Institute RAS, Moscow, Russia
2 Federal Scientific Center of Agroecology, Complex Meliorations and Agroforestry RAS , Volgograd, Russia
Accepted: 16.08.2022
DOI: 10.21046/2070-7401-2022-19-4-207-222
Protective forest plantations (PFPs) are a very important component of the sustainable functioning of agricultural landscapes. They prevent the development of water and wind erosion. Over the recent decades, the rates of PFPs development in Russia have decreased many times over as compared to the middle of the last century. At the same time, due to natural and anthropogenic factors, the existing PFPs are subject to large-scale degradation processes, while there is a lack of information about their current state. Land cover type information products and forest monitoring systems cover only a part of the PFPs. Protective forest belts with a width of 10–20 m are reflected very fragmentarily on the existing global and national satellite maps of forests. The well-known approaches to the mapping of PFPs are based on expert interpretation of Earth remote sensing (ERS) data of ultra-high spatial resolution or ground-based surveys using GPS receivers. Such methods are time consuming and cannot be applied over large areas. The paper proposes a new approach for mapping PFPs based on multi-season satellite ERS data using the BSFI (Bi-Season Forest Index). The index is calculated as the normalized difference between the minimum NDVI value for the vegetation period and the maximum albedo determined in winter when there is snow cover on the earth’s surface. Pixels with positive values of this index can be attributed to the areas covered with tree vegetation. We have compared the PFP area with the results of expert interpretation of detailed satellite images and widely used information products of satellite land cover mapping. The PFP area was identified on the basis of the BSFI using satellite imagery data obtained by the Sentinel 2 toolbox. The comparative analysis showed a close connection between the results obtained using the Sentinel 2 data and the estimates of the PFP area based on expert interpretation of detailed satellite images. The connection was characterized by a correlation coefficient R = 0.99. The analysis also allowed establishing the value of accuracy of forest identification based on this index at the level of 91 %. The relative error in identifying field-protective forest belts in the 50 m zone around the field boundaries was 9 %. The proposed approach is recommended for mapping PFPs in sparsely forested regions, where it is possible to obtain satellite images of the earth’s surface with snow cover.
Keywords: shelterbelt, tree and shrub vegetation, agrolandscapes, agroforestry, remote sensing, Sentinel 2, NDVI
Full text


  1. Agrolesomelioratsiya (Agroforestry), Volgograd: VNIALMI, 2006, 746 p. (in Russian).
  2. Anikeev E. A., Muntean A. N., Zakharov D. S., Assessment of provision of Pridnestrovian territory by field-protective forest strips, Vestnik Pridnestrovskogo universiteta. Ser.: Mediko-biologicheskie i khimicheskie nauki, 2018, No. 2(59), pp. 107–113 (in Russian).
  3. Antonov S. A., Spatial analysis of protective forest plantations based on geographic information technologies and remote sensing data, InterKarto. InterGIS. Geoinformatsionnoe obespechenie ustoichivogo razvitiya territorii (InterCarto. InterGIS. GI support of sustainable development of territories), Proc. Intern. Conf., Moscow: Moscow University Press, 2020, Vol. 26, Part 2, pp. 408–420 (in Russian), DOI: 10.35595/2414-9179-2020-2-26-408-420.
  4. Bartalev S. A., Isaev A. S., Loupian E. A., Modern Priorities of Development of Monitoring of Boreal Ecosystems by the Data of Spacecraft Observations, Sibirskii ekologicheskii zhurnal, 2005, Vol. 12, No. 6, pp. 1039–1054 (in Russian).
  5. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Land cover mapping over Russia using Earth observation data, Moscow: IKI RAN, 2016, 208 p. (in Russian).
  6. Bartalev S. A., Bogodukhov M. A., Zharko V. O., Sidorenkov V. M. (2022a), Investigation of the possibilities of using ICESat-2 data to estimate the height of forests in Russia, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 195–206 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-195-206.
  7. Bartalev S. A., Vorushilov I. I., Egorov V. A. (2022b), Creation and radiometric normalisation of cloud-free composite satellite images of snow-covered terrestrial surface for forest monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 57–69 (in Russian), DOI: 10.21046/2070-7401-2022-19-2-57-69.
  8. Zharko V. O., Bartalev S. A., Egorov V. A., Investigation of forest growing stock volume estimation possibilities over Russian Primorsky Krai region using Proba-V satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 1, pp. 157–168 (in Russian), DOI: 10.21046/2070-7401-2018-15-1-157-168.
  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, Vol. 12, No. 5, pp. 263–284 (in Russian).
  10. Loupian E. A., Bartalev S. A., Balashov I. V., Egorov V. A., Ershov D. V., Kobets D. A., Senko K. S., Stytsenko F. V., Sychugov I. G., Satellite monitoring of forest fires in the 21st century in the territory of the Russian Federation (facts and figures based on active fires detection), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 158–175 (in Russian), DOI: 10.21046/2070-7401-2017-14-6-158-175.
  11. Narozhnyaya A. G., Chendev Yu. G., The study of the modern ecological state of shelterbelts using GIS and remote sensing data, InterKarto. InterGIS. Geoinformatsionnoe obespechenie ustoichivogo razvitiya territorii (InterCarto. InterGIS. GI support of sustainable development of territories), Proc. Intern. Conf., Moscow: Moscow University Press, 2020, Vol. 26, Part 2, pp. 54–65 (in Russian), DOI: 10.35595/2414-9179-2020-2-26-54-65.
  12. National report “Global Climate and Soil Cover of Russia: Desertification and Land Degradation, Institutional, Infrastructure, Technological Adaptation Measures (Agriculture and Forestry)”, Vol. 2, Moscow: OOO “Izdatel’stvo MBA”, 2019, 476 p. (in Russian).
  13. Proezdov P. N., Mashtakov D. A., Popov V. G., Kuznetsova L. V., Karpushkin A. V., Samsonov E. V., Panfilov A. V., Rozanov A. V., Udalova O. G., Vishnyakova V. V., Berlin N. G., Pugovkina I. A., Khazova A. G., Panfilova E. G., Irgiskin I. Yu., Agroforestry, Saratov: OOO “Amirit”, 2016, 472 p. (in Russian).
  14. Rulev A. S., Yuferev V. G., Anopin V. N., Rulev G. A., Geoinformation analysis of the state of roadside forest plantations, Izvestiya Orenburgskogo gosudarstvennogo agrarnogo universiteta, 2014, No. 3(47), pp. 42–45 (in Russian).
  15. 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 obrazovanie, 2016, No. 4(44), pp. 24–32 (in Russian).
  16. Strategy for the development of protective afforestation in the Volgograd region for the period up to 2025, Volgograd: FSC of agroecology RAS, 2017, 39 p. (in Russian).
  17. Terekhin E. A., Recognition of abandoned agricultural lands using seasonal NDVI values, Computer Optics, 2017, Vol. 41, No. 5, pp. 719–725 (in Russian), DOI: 10.18287/2412-6179-2017-41-5-719-725.
  18. Terekhin E. A., Spatial analysis of tree vegetation of abandoned arable lands using their spectral response in forest-steppe zone of Central Chernozem Region, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 142–156 (in Russian), DOI: 10.21046/2070-7401-2020-17-5-142-156.
  19. Timer’yanov A., Influence of agroforest stands on cost agricultural grounds, Vestnik Bashkirskogo gosudarstvennogo agrarnogo universiteta, 2010, No. 3, pp. 43–48 (in Russian).
  20. Tkachenko N. A., Koshelev A. V., Mapping of aprotective woodiness of agrolandscapes of Volgograd Zavolzhye, Vestnik APK Stavropol’ya, 2017, No. 2(26), pp. 137–143 (in Russian).
  21. Khovratovich T. S., Bartalev S. A., Kashnitskii A. V., Forest change detection based on sub-pixel estimation of crown cover density using bitemporal satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 102–110 (in Russian), DOI: 10.21046/2070-7401-2019-16-4-102-110.
  22. Chimitdorzhiev T. N., Dmitriev A. V., Kirbizhekova I. I., Sherkhoeva A. A., Baltukhaev A. K., Dagurov P. N., Remote optical-microwave measurements of forest parameters: modern state of research and experimental assessment of potentials, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 9–24 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-9-24.
  23. Shinkarenko S. S., Bartalev S. A., NDVI seasonal dynamics of the North Caspian pasture landscapes according to MODIS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4. pp. 179–194 (in Russian), DOI: 10.21046/2070-7401-2020-17-4-179-194.
  24. Shikhov A. N., Dremin D. A., Patterns of wind-induced forest damage in the European Russia and Ural: analysis with satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 3, pp. 153–168 (in Russian), DOI: 10.21046/2070-7401-2021-18-3-153-168.
  25. Begimova M., Climate indicators for forest landing and evaluation of forest shelterbelts, E3S Web Conf., 2021, Vol. 227, p. 02004, DOI: 10.1051/e3sconf/202122702004.
  26. Chen J., Ban Y., Li S., China: Open access to Earth land-cover map, Nature, 2014, Vol. 514(7523), p. 434, DOI: 10.1038/514434c.
  27. Hall D., Riggs G., Salomonson V., Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data, Remote Sensing of Environment, 1995, Vol. 54, Issue 2, pp. 127–140.
  28. Hansen M. C., Potapov P. V., Moore R., Hancher M., Turubanova S. A., Tyukavina A., Thau D., Stehman S. V., Goetz S. J., Loveland T. R., Kommareddy A., Egorov A., Chini L., Justice C. O., Townshend J. R. G., High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 2013, Vol. 342, pp. 850–853, DOI: 10.1126/science.1244693.
  29. Isaev A. S., Bartalev S. A., Lupyan E. A., Lukina N. V., Earth observations from satellites as a unique instrument to monitor Russia’s forests, Herald of the Russian Academy of Sciences, 2014, Vol. 84, No. 6, pp. 413–419, DOI: 10.1134/S1019331614060094.
  30. Karra K., Kontgis C., Statman-Weil Z., MazzarielloJ. C., Mathis M., Brumby S. P., 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.
  31. Klein A. G., Hall D. K., Riggs G. A., Improving snowcover mapping in forests through the use of a canopy reflectance model, Hydrological Processes, 1998, No. 12, pp. 1723–1744.
  32. Koshelev A. V., Tkachenko N. A., Shatrovskaya M. O., Decoding of forest belts using satellite images, IOP Conf. Series: Earth and Environmental Science, 2021, Vol. 875, Art. No. 012065, 9 p., DOI: 10.1088/1755-1315/875/1/012065.
  33. Kotel’nikov R. V., Loupian E. A., Bartalev S. A., Ershov D. V., Space Monitoring of Forest Fires: History of the Creation and Development of ISDM-Rosleskhoz, Contemporary Problems of Ecology, 2020, Vol. 13, No. 7, pp. 795–802, DOI: 10.1134/S1995425520070045.
  34. Loupian E. A., Bourtsev M. A., Proshin A. A., Kashnitskii A. V., Balashov I. V., Bartalev S. A., Konstantinova A. M., Kobets D. A., Radchenko M. V., Tolpin V. A., Uvarov I. A., Usage Experience and Capabilities of the VEGA-Science System, Remote Sensing, 2022, Vol. 14, No. 1, Art. No. 77, 19 p.,
  35. Rulev A. S., Pugacheva A. M., Formation of a New Agroforestry Paradigm, Herald of the Russian Academy of Sciences, 2019, Vol. 89, No. 5, pp. 495–501, DOI: 10.1134/S1019331619050071.
  36. Smirnov V. O., Zelentsova M. G., Krainyuk E. S., Practical Application Peculiarities of Geo-Information Technologies While Planning the Protective Forest Belts, IOP Conf. Series: Earth and Environmental Science, 2021, Vol. 666, Art. No. 042005, 8 p., DOI: 10.1088/1755-1315/666/4/042005.
  37. Vassilev K. V., Assenov A. I., Velev N. I., Grigorov B. G., Borissova B. B., Distribution, Characteristics and Ecological Role of Protective Forest Belts in Silistra Municipality, Northeastern Bulgaria, Ecologia Balcanica, 2019, Vol. 11, Issue 1, pp. 191–204.
  38. Yu T., Liu P., Zhang Q., Ren Y., Yao J., Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images, Remote Sensing, 2021, Vol. 13, No. 6, Art. No. 1131, 16 p., DOI: 10.3390/rs13061131.
  39. Zanaga D., Van De Kerchove R., De Keersmaecker W., Souverijns N., Brockmann C., Quast R., Wevers J., Grosu A., Paccini A., Vergnaud S., Cartus O., Santoro M., Fritz S., Georgieva I., Lesiv M., Carter S., Herold M., Li L., Tsendbazar N. E., Ramoino F., Arino O., ESA WorldCover 10 m 2020 v100, 2021, DOI: 10.5281/zenodo.5571936.