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. 227-241

Relationships between tree cover of small dry valleys and vegetation indices in the south of Central Russian Upland

E.A. Terekhin 1 
1 Belgorod State National Research University, Belgorod, Russia
Accepted: 26.05.2026
DOI: 10.21046/2070-7401-2026-23-4-227-241
Assessing the relationships between natural afforestation in landscapes and indicators derived from Earth remote sensing data is a necessary condition for developing approaches to spatiotemporal assessment of forested lands. The article analyzes the relationships between tree cover of small dry valleys and 18 spectral vegetation indices measured using Sentinel-2 data. The study objects were areas of the small dry valleys network in the south of the Central Russian Upland. Spectral indices showed different correlation with the tree cover of the small dry valleys. Normalized indices that use green and shortwave infrared reflectance in addition to near-infrared reflectance values showed the strongest relationship with tree cover. The differences between tree cover gradations, with the exception of the gradation with values greater than 0.8, are statistically significant for most indices. The relationship between the overwhelming majority of vegetation indices and tree cover is positive and can be considered stable. The dependence of the studied indices on tree cover values is approximated by a logarithmic function, with the highest explanatory power for the NDI (Normalized Difference Index) and the GNDVI (Green Normalized Difference Vegetation Index).
Keywords: forest area, vegetation indices, forest-steppe, remote sensing data, Sentinel-2
Full text

References:

  1. Kuzmenko Ya.V., Lisetskii F. N., Kirilenko Zh. A., Grigoryeva O. I., Providing the optimum water protection forests amount at basin organization of nature management, Izvestiya Samarskogo nauchnogo tsentra RAN, 2013. V. 15, No. 3–2, pp. 652–657 (in Russian).
  2. Sablina O. M., Narozhnyaya A. G., Spread of ravines in the Belgorod Region, Vestnik Voronezhskogo gosudarstvennogo universiteta. Ser.: Geografiya. Geoehkologiya, 2023, No. 3. pp. 25–34 (in Russian), DOI: 10.17308/geo/1609-0683/2023/3/25-34.
  3. Tarasova L. V., Kurbanov E. A., Vorobiev O. N. et al., Monitoring forest cover in riparian zones along the rivers of Mari El Republic using satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 2, pp. 177–195 (in Russian), DOI: 10.21046/2070-7401-2024-21-2-177-195.
  4. Terekhin E. A., Spatiotemporal spectral-response assessment of the forest cover of small dry valleys in the Central Russian forest–steppe, Izvestiya, Atmospheric and Oceanic Physics, 2021, V. 57, No. 12, pp. 1566–1575, DOI: 10.1134/S0001433821120215.
  5. Terekhin E. A., Reforestation on abandoned agricultural lands in the Central Russian forest–steppe, Izvestiya Rossiiskoi akademii nauk. Ser. geograficheskaya, 2022, V. 86, No. 4, pp. 594–604 (in Russian), DOI: 10.31857/S2587556622040112.
  6. Terekhin E. A., Recent trends in forest cover changes in the Central Russian forest-steppe, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 207–221 (in Russian), DOI: 10.21046/2070-7401-2025-22-5-207-221.
  7. Ukrainskij P. A., Terekhin E. A., Pavlyuk Ya. V., Fragmentation of forests in the upper part of the Vorskla River basin since the end of the 18th century, Vestnik Moskovskogo universiteta. Ser. 5: Geografiya, 2017, No. 1, pp. 82–91 (in Russian).
  8. Fiziko-geograficheskoe raionirovanie central’nyh chernozemnyh oblastei (Physical and geographical zoning of the Central Chernozem regions), Voronezh: Izd. Voronezhskogo universiteta, 1961, 263 p. (in Russian).
  9. Tsvetkov M. A., Izmenenie lesistosti Evropeiskoi Rossii s kontsa XVII stoletiya po 1914 god (Changes in forest cover in European Russia from the end of the 17th century to 1914), Moscow: Izd. AN SSSR, 1957, 213 p. (in Russian).
  10. Chendev Yu. G. Evolyutsiya lesostepnykh pochv Srednerusskoi vozvyshennosti v golotsene (Evolution of forest-steppe soils of the Central Russian Upland in the Holocene), Moscow: GEOS, 2008, 212 p. (in Russian).
  11. Shabanov N. V., Bartalev S. A., Eroshenko F. V., Plotnikov D. E., Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 4, pp. 166–178 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-166-178.
  12. Bartalev S. A., Egorov V. A., Loupian E. A., Khvostikov S. A., A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data, Remote Sensing Letters, 2014, V. 5, No. 1, pp. 55–64, DOI: 10.1080/2150704X.2013.870675.
  13. Bera D., Das Chatterjee N., Bera S. et al., Comparative performance of Sentinel-2 MSI and Landsat-8 OLI data in canopy cover prediction using Random Forest model: Comparing model performance and tuning parameters, Advances in Space Research, 2023, V. 71, No. 11, pp. 4691–4709, DOI: 10.1016/j.asr.2023.01.027.
  14. Chendev Yu. G., Hubbart J. A., Terekhin E. A. et al., Recent afforestation in the Iowa river and Vorskla river basins: A comparative trends analysis, Forests, 2016, V. 7, No. 11, Article 278, DOI: 10.3390/f7110278.
  15. Clevers J. G. P. W., Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture, Remote Sensing of Environment, 1989, V. 29, No. 1, pp. 25–37, DOI: 10.1016/0034-4257(89)90076-X.
  16. Crist E. P., Cicone R. C., A physically based transformation of Thematic Mapper data — The TM Tasseled Cap, IEEE Trans. Geoscience and Remote Sensing, 1984, V. GE-22, No. 3, pp. 256–263, DOI: 10.1109/TGRS.1984.350619.
  17. Gitelson A. A., Kaufman Y. J., Merzlyak M. N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sensing of Environment, 1996, V. 58, No. 3, pp. 289–298, DOI: 10.1016/S0034-4257(96)00072-7.
  18. Gitelson A. A., Kaufman Y. J., Stark R., Rundquist D., Novel algorithms for remote estimation of vegetation fraction, Remote Sensing of Environment, 2002, V. 80, No. 1, pp. 76–87, DOI: 10.1016/S0034-4257(01)00289-9.
  19. Goel N. S., Qin W., Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation, Remote Sensing Reviews, 1994, V. 10, No. 4, pp. 309–347, DOI: 10.1080/02757259409532252.
  20. Gudex-Cross D., Pontius J., Adams A., Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery, Remote Sensing of Environment, 2017, V. 196, pp. 193–204, DOI: 10.1016/j.rse.2017.05.006.
  21. 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, No. 3, pp. 337–352, DOI: 10.1016/j.rse.2003.12.013.
  22. Huete A. R., A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 1988, V. 25, No. 3, pp. 295–309, DOI: 10.1016/0034-4257(88)90106-X.
  23. Huete A., Didan K., Miura T. et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 2002, V. 83, No. 1–2, pp. 195–213, DOI: 10.1016/S0034-4257(02)00096-2.
  24. Jiang Z., Huete A. R., Didan K., Miura T., Development of a two-band enhanced vegetation index without a blue band, Remote Sensing of Environment, 2008, V. 112, No. 10, pp. 3833–3845, DOI: 10.1016/j.rse.2008.06.006.
  25. Jordan C. F., Derivation of Leaf-area index from quality of light on the forest floor, Ecology, 1969, V. 50, No. 4, pp. 663–666, DOI: 10.2307/1936256.
  26. Kaufman Y. J., Tanre D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS, IEEE Trans. Geoscience and Remote Sensing, 1992, V. 30, No. 2, pp. 261–270, DOI: 10.1109/36.134076.
  27. Liu D., Yang L., Jia K. et al., Global fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods, Remote Sensing, 2018, V. 10, No. 10, Article 1648, DOI: 10.3390/rs10101648.
  28. McNairn H., Protz R., Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper, Canadian J. Remote Sensing, 1993, V. 19, No. 2, pp. 152–159, DOI: 10.1080/07038992.1993.10874543.
  29. Pearson R. L., Miller L. D., Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Proc. 8 th Intern. Symp. on Remote Sensing of Environment, 1972, V. 2, pp. 1357–1381.
  30. Radhakrishnan S., Lakshminarayanan A. S., Chatterjee J. M., Hemanth D. J., Forest data visualization and land mapping using support vector machines and decision trees, Earth Science Informatics, 2020, V. 13, No. 4, pp. 1119–1137, DOI: DOI: 10.1007/s12145-020-00492-3.
  31. Roujean J.-L., Breon F.-M., Estimating PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing of Environment, 1995, V. 51, No. 3, pp. 375–384, DOI: 10.1016/0034-4257(94)00114-3.
  32. Rouse J. W., Jr., Haas R. H., Schell J. A., Deering D. W., Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, NASA/GSFCT Type II Report. Greenbelt, MD: Goddard Space Flight Center, April 1973, 93 p.
  33. Senf C., Laštovička J., Okujeni A. et al., A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data, Remote Sensing of Environment, 2020, V. 240, Article 111691, DOI: 10.1016/j.rse.2020.111691.
  34. 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, No. 10, pp. 4038–4048, DOI: 10.1109/JSTARS.2019.2938388.
  35. Tucker C. J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 1979, V. 8, No. 2, pp. 127–150, DOI: 10.1016/0034-4257(79)90013-0.
  36. Vincini M., Frazzi E., D’Alessio P., A broad-band leaf chlorophyll vegetation index at the canopy scale, Precision Agriculture, 2008, V. 9, No. 5, pp. 303–319, DOI: 10.1007/s11119-008-9075-z.