The study highlights the use of remote sensing (RS) for geoinformation modelling of the Kakhovka dam destruction impact on the Lower Dnipro National Nature Park. The modelling was performed with data from the Landsat satellite and geographic information systems (GIS) methods. Authors describe the algorithm for processing of geospatial data from Landsat, evaluate the flooding scale with water index and judge the condition of plants by vegetation index. The study concludes that ongoing monitoring and the development of comprehensive approaches for the rehabilitation of the affected areas are necessary. The use of modelling results is proposed for planning of protection and restoration activities to overcome the consequences of man-made disasters.
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Isupova, S., & Chernov, A. (2023). Witnessing Changes of the Ukrainian Landscape: Drawing the Segments of Ukrainian Terrain Using a Map-Making Machine. 17th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, Nov 2023, Volume 2023, p.1 – 5. https://doi.org/10.3997/2214-4609.2023520067
Kastridis, A., Theodosiou, G., & Fotiadis, G. (2021). Investigation of flood management and mitigation measures in ungauged NATURA protected watersheds. Hydrology, 8(4), 170. URL: https://doi.org/10.3390/hydrology8040170
Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrological Sciences Journal, 56(2), 212–225. URL: https://doi.org/10.1080/02626667.2011.555836
Ministry of Environmental Protection and Natural Resources of Ukraine. (2020, 2023). Environmental passports. Retrieved from mepr.gov.ua: https://mepr.gov.ua/diyalnist/napryamky/ekologichnyj-monitoryng/ekologichni-pasporty/ [In Ukrainian].
Szabo, S., Gácsi, Z., & Balazs, B. (2016). Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. https://doi.org/10.21120/LE/10/3-4/13
Tsiupa, I., & Plichko, L. (2023). Study of Dynamics of Changes in the Kakhovka Reservoir Based on Remote Sensing Data. 2023(1), 1–5. URL: https://doi.org/10.3997/2214-4609.2023520170
Valle Júnior, R. F. do, Siqueira, H. E., Valera, C. A., Oliveira, C. F., Sanches Fernandes, L. F., Moura, J. P., & Pacheco, F. A. L. (2019). Diagnosis of degraded pastures using an improved NDVI-based remote sensing approach: An application to the Environmental Protection Area of Uberaba River Basin (Minas Gerais, Brazil). Remote Sensing Applications: Society and Environment, 14, 20–33. https://doi.org/10.1016/j.rsase.2019.02.001