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Spectral reflectance analysis of abandoned agricultural lands in the Central Russian forest-steppe using Sentinel-2 satellite data
E.A. Terekhin 1

1 Belgorod State University, 308015, Russia, Belgorod, Pobedy str., 85

 PDF, 915 kB

DOI: 10.18287/2412-6179-CO-1160

Pages: 306-313.

Full text of article: Russian language.

Abstract:
The article considers the spectral response of post-agrogenic landscapes in the forest-steppe zone based on Sentinel-2 data. The study was carried out on the territory of the Central Chernozem region. The type of forest that forms on abandoned agricultural land has a statistically significant effect on the spectral response in most Sentinel-2 bands. The reflectance of abandoned lands with deciduous and coniferous species is statistically significantly different in most bands. The reflectance of abandoned lands with mixed forests does not differ statistically significantly from other types of post-agrogenic landscapes. The reflectance of abandoned lands is inversely related to their forest cover in most Sentinel-2 bands. The strongest correlation with forest cover is typical for red (Band 4) and SWIR (Band 11, 12) ranges for all post-agrogenic landscape types. In the same bands, there are statistically significant differences between most of forest cover gradations of post-agrogenic landscapes. The established patterns make it possible to use the reflectance in the red (Band 4) and SWIR MSI bands (11, 12) to assess the forest cover of post-agrogenic landscapes.

Keywords:
post-agrogenic landscapes, spectral responce, image processing, forest-steppe, Sentinel-2.

Citation:
Terekhin EA. Spectral reflectance analysis of abandoned agricultural lands in the Central Russian forest-steppe using Sentinel-2 satellite data. Computer Optics 2023; 47(2): 306-313. DOI: 10.18287/2412-6179-CO-1160.

Acknowledgements:
The work was supported by the Russian Science Foundation under grant # 22-27-00291.

References:
  1. Goleusov PV, Lisetskii FN. Soil reproduction in anthropogenic landscapes of forest-steppe [In Russian]. Moscow: GEOS Publisher; 2009.
  2. Kurganova IN, Telesnina VM, Lopes de Gerenyu VO, Lichko VI, Karavanova EI. The dynamics of carbon pools and biological activity of retic albic podzols in southern taiga during the postagrogenic evolution. Eurasian Soil Sci 2021; 54(3): 337-351. DOI: 10.1134/S1064229321030108.
  3. Sorokina OA. Diagnostic parameters of soil formation in gray forest soils of abandoned fields overgrowing with pine forests in the middle reaches of the Angara River. Eurasian Soil Sci 2010; 43(8): 867-875. DOI: 10.1134/S1064229310080041.
  4. Ershov DV, Gavrilyuk EA, Koroleva NV, et al. Natural afforestation on abandoned agricultural lands during post-soviet period: A comparative landsat data analysis of bordering regions in Russia and Belarus. Remote Sens 2022; 14(2): 322. DOI: 10.3390/rs14020322.
  5. Ivanov AI, Ivanova ZhA, Sokolov IV. Secondary development of unused land. Russ Agric Sci 2020; 46(3): 274-278. DOI: 10.3103/S1068367420030076.
  6. Terekhin EA. Satellite-based estimation of successional processes on abandoned farmland of south Central Russian Upland [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2019; 16(6): 180-193. DOI: 10.21046/2070-7401-2019-16-6-180-193.
  7. Sajb EA, Bezborodova AN, Solov'ev SV, Miller GF, Filimonova DA. Identification of different age fallows on erosion-hazardous territories of the south of Western Siberia using geo-information technologies [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa  2020; 17(4): 129-136. DOI: 10.21046/2070-7401-2020-17-4-129-136.
  8. Kumar S, Arya S, Jain K. A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. Int J Inf Technol 2022; 14(4): 2035-2048. DOI: 10.1007/s41870-021-00797-6.
  9. Yin H, Brandão A, Buchner J, et al. Monitoring cropland abandonment with Landsat time series. Remote Sens Environ 2020; 246: 111873. DOI: 10.1016/j.scitotenv.2020.142651.
  10. Zhu X, Xiao G, Zhang D, Guo L. Mapping abandoned farmland in China using time series MODIS NDVI. Sci Total Environ 2021; 10(755): 142651. DOI: 10.1016/j.scitotenv.2020.142651.
  11. He S, Shao H, Xian W, Zhang S, Zhong J, Qi J. Extraction of abandoned land in hilly areas based on the spatio-temporal fusion of multi-source remote sensing images. Remote Sens 2021; 13(19): 3956. DOI: 10.3390/rs13193956.
  12. Estel S, Kuemmerle T, Levers C, Baumann M, Hostert P. Mapping cropland-use intensity across Europe using MODIS NDVI time series. Environ Res Lett 2016; 11(2): 024015. DOI: 10.1088/1748-9326/11/2/024015.
  13. Grădinaru SR, Kienast F, Psomas A. Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. Ecol Indic 2019; 96: 79-86. DOI: 10.1016/j.ecolind.2017.06.022.
  14. Zhao L, Yang Q, Zhao Q, Wu J. Assessing the long-term evolution of abandoned salinized farmland via temporal remote sensing data. Remote Sens 2021; 13(20): 4057. DOI: 10.3390/rs13204057.
  15. Denisova AYu, Egorova AA, Sergeev VV, Kavelenova LM. Requirements for multispectral remote sensing data used for the detection of arable land colonization by tree and shrubbery vegetation. Computer Optics 2019; 43(5): 846-856. DOI: 10.18287/2412-6179-2019-43-5-846-856.
  16. Koley S, Chockalingam J. Sentinel 1 and Sentinel 2 for cropland mapping with special emphasis on the usability of textural and vegetation indices. Adv Space Res 2022; 69(4): 1768-1785. DOI: 10.1016/j.asr.2021.10.020.
  17. Goga T, Feranec J, Bucha T, Rusnák M, Sačkov I, Barka I, et al. A review of the application of remote sensing data for abandoned agricultural land identification with focus on Central and Eastern Europe. Remote Sens 2019; 11(23): 2759. DOI: 10.3390/rs11232759.
  18. Terekhin EA. Indication of long-term changes in the vegetation of abandoned agricultural lands for the forest-steppe zone using NDVI time series. Computer Optics 2021; 45(2): 245-252. DOI: 10.18287/2412-6179-CO-797.
  19. Shang R, Zhu Z, Zhang J, et al. Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data. Remote Sens Environ 2022; 278: 113073. DOI: 10.1016/j.rse.2022.113073.
  20. Schwieder M, Wesemeyer M, Frantz D, et al. Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sens Environ 2022; 269: 112795. DOI: 10.1016/j.rse.2021.112795.
  21. Pahlevan N, Sarkar S, Franz BA, Balasubramanian SV, He J. Sentinel-2 multispectral instrument (MSI) data processing for aquatic science applications: Demonstrations and validations. Remote Sens Environ 2017; 201: 47-56. DOI: 10.1016/j.rse.2021.112795.

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