Features of spatio-temporal variability of photosynthetic activity of vegetation in valley-river landscapes

DOI: 10.35595/2414-9179-2024-1-30-588-603

View or download the article (Rus)

About the Author

Arseniy O. Poletaev

Belgorod National Research University, Institute of Earth Sciences,
85, Pobedy str., Belgorod, 308015, Russia,
E-mail: poletaev@bsu.edu.ru

Abstract

The article describes the possibilities of using the Chlorophyll vegetation index (CVI) to detect vegetation in floodplain areas of rivers, which have significant potential for sequestration and long-term sequestration of carbon (carbon dioxide). CVI index rasters are calculated based on Landsat-8–9 OLI/TIRS C2 L2 satellite images obtained during the vegetative season in 2020–2023 (April–August). Based on a set of raster data of the CVI index, rasters of the difference between rasters of the CVI index were calculated and areas of increase and decrease in the CVI index were identified in the key area located in the valleys of the Seversky Donets and the Nezhegol rivers (Don River basin). Combining the landscape map and the raster of the difference between the CVI rasters made it possible to identify areas of marsh-grass-sedge meadows, where the chlorophyll content decreased from the beginning of May to the beginning of July 2022. An analysis of the ratio of the areas of the difference raster values between the CVI rasters showed (according to data for the growing seasons 2020–2023) that a value equal to 1 is in the range of 1.6–53.6 % of the area of the entire key territory and falls on the marsh-grass-sedge meadows, grass-forb meadows, sedge-reed marshy meadows and swamps, floodplain forests with domination of willow and black alder. A value equal to 0 is in the range of 46.4–98.3 % of the total area and falls on marsh-grass-sedge meadows, grass-forb meadows, pine plantations, sedge-reed marshy meadows and swamps, floodplain forests with domination of willow and black alder. The value equal to -1 is in the range of 0–17.6 % of the entire area and falls on marsh-grass-sedge meadows, grass-forb meadows, sedge-reed marshy meadows and swamps. For territories where the average value of the CVI index is less than or equal to 1, the predominant type of vegetation is pine plantations. For territories where the average value of the CVI index is within 1.1–1.5, meadow vegetation predominates. In areas where the average value of the CVI index is more than 1.5, the largest area is occupied by floodplain forests with domination of willow and black alder. The joint use of data obtained during landscape research and the results of analysis of changes in vegetation indices allows us to obtain new information for making decisions on the rational organization of environmental management within valley-river landscapes.

Keywords

vegetation index, chlorophyll vegetation index, river valley, floodplain landscapes, Don River basin

References

  1. Avetisyan D., Stankova N., Dimitrov Z. Assessment of spectral vegetation indices performance for post-fire monitoring of different forest environments. Fire, 2023. V. 6. No. 8. P. 290. DOI: 10.3390/fire6080290.
  2. Chandrasekar K., Sesha Sai M.V.R., Roy P.S., Dwevedi R.S. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product. International Journal of Remote Sensing, 2010. V. 31. No. 15. P. 3987–4005. DOI: 10.1080/01431160802575653.
  3. Domnina E.A., Timonov A.S., Kantor G.Y., Kislitsyna A.P., Savinykh V.P. Experience of detailed mapping of floodplain meadow vegetation. Theoretical and Applied Ecology, 2017. No. 1. P. 42–49 (in Russian).
  4. Geerling G.W., Labrador-Garcia M., Clevers J.G.P.W., Ragas A.M.J., Smits A.J.M. Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. International Journal of Remote Sensing, 2007. V. 28. No. 19. P. 4263–4284. DOI: 10.1080/01431160701241720.
  5. Hunt E.R., Daughtry C.S.T., Eitel J.U.H., Long D.S. Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 2011. V. 103. No. 4. P. 1090–1099. DOI: 10.2134/agronj2010.0395.
  6. Kooistra L., Salas E.A.L., Clevers J.G.P.W., Wehrens R., Leuven R.S.E.W., Nienhuis P.H., Buydens L.M.C. Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environmental Pollution, 2004. V. 127. No. 2. P. 281–290. DOI: 10.1016/S0269-7491(03)00266-5.
  7. Kurganovich K.A., Golyatina M.A. Spatiotemporal response of NDVI to changes in climatic characteristics in the Trans-Baikal Territory for the period 2000–2014. Transbaikal State University Journal, 2015. No. 9. P. 10–20 (in Russian).
  8. Kutuzov A.V. Monitoring of the large flat water reservoirs using remote sensing data. Issledovanie Zemli iz kosmosa (Earth Observation and Remote Sensing), 2011. No. 6. P. 64–72 (in Russian).
  9. Lisetskii F.N., Poletaev A.O., Buryak Z.A. Geoinformation support for studies of the boundaries of flood zones in urban areas. Journal of Physics: Conference Series. IOP Publishing, 2022. V. 2388. No. 1. P. 012134. DOI: 10.1088/1742-6596/2388/1/012134.
  10. Marinina O.A., Yermolaev O.P., Maltsev K.A., Lisetskii F.N., Pavlyuk Y.V. Evaluation of siltation of rivers with intensive economic development of watersheds. Journal of Engineering and Applied Sciences, 2016. V. 11. No. 14. P. 3004–3013. DOI: 10.3923/jeasci.2016.3004.3013.
  11. Poletaev A.O. Predictive capabilities of GIS technologies for determining the boundaries of flood zones in floodplain landscapes (using the example of the Seversky Donets and Nezhegol rivers). Proceedings of the XIV International Landscape Conference “Theoretical and Applied Problems of Landscape Geography. VII Milkov Readings”. Voronezh, 2023. V. 2. P. 277–280 (in Russian). DOI: 10.17308/978-5-9273-3693-7-2023-277-280.
  12. Poletaev A., Lisetskii F. Using vegetation indices to identify high chlorophyll tree cover in floodplains for carbon sequestration. E3S Web of Conferences, 2024. V. 486. 07013. DOI: 10.1051/e3sconf/202448607013.
  13. Rygalova N.V., Plutalova T.G. Estimation of the relation of NDVI steppe vegetation and radial growth of pine belt forests in arid conditions of the south of Western Siberia. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2021. V. 27. Part 2. P. 355–367 (in Russian). DOI: 10.35595/2414-9179-2021-2-27-355-367.
  14. Sims N.C., Colloff M.J. Remote sensing of vegetation responses to flooding of a semi-arid floodplain: Implications for monitoring ecological effects of environmental flows. Ecological Indicators, 2012. V. 18. P. 387–391. DOI: 10.1016/j.ecolind.2011.12.007.
  15. Terekhin E.A. Long-term dynamics of the vegetation index for abandoned farmlands in the Central Chernozem Region of Russia. Regional Geosystems, 2021. V. 45. No. 4. P. 505–515 (in Russian). DOI: 10.52575/2712-7443-2021-45-4-505-515.
  16. Van Iersel W., Straatsma M., Middelkoop H., Addink E. Multitemporal classification of river floodplain vegetation using time series of UAV images. Remote Sensing, 2018. V. 10. No. 7. P. 1144. DOI: 10.3390/rs10071144.
  17. Vincini M., Calegari F., Casa R. Sensitivity of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution for different canopy structures. Precision Agriculture, 2016. V. 17. No. 3. P. 313–331. DOI: 10.1007/s11119-015-9424-7.
  18. Vysotskaya A.A., Medvedkov A.A. Climate-driven “greening” of the kurum landscape in the valley of the lower reaches of the Podkamennaya Tunguska river. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2022. V. 28. Part 1. P. 305–313 (in Russian). DOI: 10.35595/2414-9179-2022-1-28-305-313.
  19. Yudina Yu.V. Mapping geosystems of the Belgorod region: regional features. Modern Problems of Science and Education, 2013. No. 6. P. 933 (in Russian).
  20. Zhu X., Li Q., Guo C. Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought. Ecological Informatics, 2024. 102717. DOI: 10.1016/j.ecoinf.2024.102717.

For citation: Poletaev A.O. Features of spatio-temporal variability of photosynthetic activity of vegetation in valley-river landscapes. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2024. V. 30. Part 1. P. 588–603. DOI: 10.35595/2414-9179-2024-1-30-588-603 (in Russian)