Geoinformation analysis of the dynamics and structure of land cover classes of the Novogrudok Upland

  • Dmitry A. Kislitsyn Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus
  • Nikolay V. Klebanovich Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

Abstract

Using the author’s method of automated interpretation of the land cover classes based on the software packages ENVI (version 5.3), ArcGIS (version 10.7) for «Landsat-5», «Landsat-7», and «Sentinel-2» satellite images, the areas of various land cover classes were calculated for three administrative districts of Novogrudok Upland (Korelichy, No­vogrudok and Dyatlov districts of the Grodno Region). The main features of the structure of land cover classes for the period from 1986 to 2019 are analysed, which differ markedly within the region under study, due to natural conditions, relief features, as well as the level of soil fertility, which affected the degree of agricultural development of administrative districts. The possibility of using information about the relief and the normalised difference vegetation index (NDVI) to increase the overall accuracy of the results of automated interpretation in vector format in the geographic information system ArcGIS (version 10.7) is shown. Based on the use of morphometric indicators of the relief (slope and vertical dis­section) and the NDVI, areas of soils susceptible to water erosion and the main areas of the gully network for the territory of the Novogrudok Upland were identified based on automated interpretation of the «Sentinel-2» satellite image for 2019. Features of the spatial location were identified arable land on eroded soils, which are noticeably more common on the eastern slopes of the Novogrudok Upland than on the western ones, which is associated with differences in the values of morphometric relief parameters, as well as with the peculiarities of the genesis of soil-forming rocks and the granulometric composition of soils. The accuracy of the final result of automated interpretation was assessed based on the error matrix, which amounted to 80.4 %, while the highest values of user accuracy (more than 90 %) are typical for water bodies, as well as forest lands and lands under trees and shrubs on automorphic and semi-hydromorphic soils.

Author Biographies

Dmitry A. Kislitsyn, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

trainee lecturer at the department of soil science and geographic information systems, faculty of geography and geoinformatics

Nikolay V. Klebanovich, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

doctor of science (agricultural sciences), full professor; professor at the department of soil science and geographic information systems, faculty of geography and geoinformatics

References

  1. Feranec J, Jaffrain G, Soukup T, Hazeu G. Determining changes and flows in European landscapes 1990–2000 using CORINE Land Cover data. Applied Geography. 2010;30(1):19–35. DOI: 10.1016/j.apgeog.2009.07.003.
  2. Homer C, Dewitz JA, Limin Yang L, Danielson P, Xian G, Coulston JW, et al. Completion of the 2011 National Land Cover Database for the conterminous United States – representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing. 2015;81(5):345–354. DOI: 10.14358/PERS.81.5.345.
  3. Polykretis C, Alexakis DD, Grillakis MG, Manoudakis S. Assessment of intra-annual and inter-annual variabilities of soil erosion in Crete Island (Greece) by incorporating the dynamic «Nature» of R- and C-factors in RUSLE modeling. Remote Sensing. 2020;12(15):2439. DOI: 10.3390/rs12152439.
  4. Sujatha ER, Sridhar V. Spatial prediction of erosion risk of a small mountainous watershed using RUSLE: a case-study of the Palar sub-watershed in Kodaikanal, South India. Water. 2018;10(11):1608. DOI: 10.3390/w10111608.
  5. Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. Sentinel-2 data for land cover/use mapping: a review. Remote Sensing. 2020;12(14):2291. DOI: 10.3390/rs12142291.
  6. Rujoiu-Mare M-R, Olariu B, Mihai B-A, Nistor C, Savulescu I. Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery. European Journal of Remote Sensing. 2017;50(1):496–508. DOI: 10.1080/22797254.2017.1365570.
  7. Hongrui Zheng, Peijun Du, Jike Chen, Junshi Xia, Erzhu Li, Zhigang Xu, et al. Performance evaluation of downscaling Sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sensing. 2017;9(12):1274. DOI: 10.3390/rs9121274.
  8. Phan Thanh Noi, Kappas M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors. 2018;18(1):18. DOI: 10.3390/s18010018.
  9. Phiri D, Morgenroth J. Developments in Landsat land cover classification methods: a review. Remote Sensing. 2017;9(9):967. DOI: 10.3390/rs9090967.
  10. Hansen MC, Loveland TR. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment. 2012;122:66–74. DOI: 10.1016/j.rse.2011.08.024.
  11. Yanovskiy AA. The remote separation of drained peat and the degraded peat soils of Polesia by spectral reflectance thresholding. Prirodopol’zovanie. 2017;31:105–112. Russian.
  12. Nichiporovich ZA, Rakovich VA, Kazhdan EN. Remote diagnostics of a condition and dynamics of rebogged cutover peat deposits. Prirodopol’zovanie. 2013;23:94–99. Russian.
  13. Myshliakov SG. Mapping of crops using satellite images for land management and designer’s supervision. Zemlya Belarusi. 2012;1:52–56. Belarusian. EDN: WCBUDV.
  14. Skachkova AS, Kurlovich DM. Natural-anthropogenic landscapes of Belarusian elevated province: classification, spatial structure, zoning. Journal of Belarusian State University. Geography. Geology. 2017;1:3–13. Russian. EDN: XYPAGD.
  15. Olshevsky AV. GIS model of the automated classification of multispectral satellite images. Zemlya Belarusi. 2013;1:36–39. Russian. EDN: TRKVHP.
  16. Gorbacheva EN. RUSLE model implementation for estimation intensity of water erosion processes on Belarusian soil cover. Soil Science and Agrochemistry. 2011;2:42–51. Russian. EDN: XYMPBB.
  17. Phillips LB, Hansen AJ, Flather CH. Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production. Remote Sensing of Environment. 2008;112(12):4381–4392. DOI: 10.1016/j.rse.2008.08.001.
  18. Yengoh GT, Dent D, Olsson L, Tengberg AE, Tucker III CJ. Use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales: current status, future trends, and practical considerations. Cham: Springer; 2015. 80 p. (Springer briefs in environmental science series). DOI: 10.1007/978-3-319-24112-8.
  19. Maroz VA. Valuation of the effectiveness of land use based on the analysis of vegetation indices (on the example of Brest Polesye’s model polygons). Natural Resources. 2018;2:5–13. Russian.
  20. Gusev AP. NDVI changes as an indicator of the dynamics of the ecological state of landscapes (on the example of the Eastern Part of the Polessie Province). Proceedings of Voronezh State University. Series: Geography. Geoecology. 2020;1:101–107. Russian. DOI: 10.17308/geo.2020.1/2667.
  21. Kislitsyn DA. [Features of generalization of results of automated interpretation in ArcGIS and ENVI ]. In: Domas’ AS, Shkuratova NV, Levkovskaya MV, editors. Monitoring i okhrana okruzhayushchei sredy. Materialy Respublikanskoi nauchno-prakticheskoi konferentsii studentov, magistrantov, aspirantov; 22 marta 2023 g.; Brest, Belarus’ [Monitoring and environmental protection. Proceedings of the Republican scientific and practical conference of students, undergraduates, postgraduate students; 2023 March 22; Brest, Belarus]. Brest: Brest State A. S. Pushkin University; 2023. p. 78–80. Russian.
  22. Kurlovich DM. Morphometric GIS-analysis of landforms of Belarus. Zemlya Belarusi. 2013;4:42–48. Russian. EDN: WCBUNV.
  23. Klebanovich NV, Kislitsyn DA. Analysis of soil and vegetation cover features on the remote sensing data (by the example of the Brest Polesye’s key districts). Vesnik of Brest University. Series 5, Biology. Sciences about Earth. 2022;1:59–66. Russian.
Published
2024-06-24
Keywords: automated interpretation, relief, normalised difference vegetation index, NDVI, land cover, soil erosion, Novogrudok Upland
How to Cite
Kislitsyn, D. A., & Klebanovich, N. V. (2024). Geoinformation analysis of the dynamics and structure of land cover classes of the Novogrudok Upland. Journal of the Belarusian State University. Geography and Geology, 1, 126-140. Retrieved from https://journals.bsu.by/index.php/geography/article/view/5764
Issue
No 1 (2024): Journal of the Belarusian State University. Geography and Geology
Section
Geography

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