Assessing aquatic enviromnemt quality of the urban water bodies by system analysis methods based on integrating remote sensing data

1Fedorovskyi, OD, 1Khyzhniak, AV, 1Tomchenko, OV
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
Space Sci. & Technol. 2021, 27 ;(5):011-018
Publication Language: English
The work presents the comprehensive methodology for assessment of the state of the urban aquatic environment such as Lakes Opechen, Verbne, and Redkyne in Kyiv using the methods of system analysis. The methodology includes structural-textural analysis of the satellite images and the method based on statistical criteria. The spectral-texture analysis of the satellite images was used to get input information for remote assessment of reservoirs as index images: Normalized Difference Pond Index (NDPI), Normalized Difference Turbidity Index (NDTI), and Normalized Difference Algae Index (NDAI) computed from the Sentinel-2. The surface temperature distribution was estimated from the Landsat 8.
            The method based on statistical criteria is used for a detailed assessment of the aquatic environment using the obtained indexed images and the corresponding cartographic representation of the water quality. The probabilistic and statistical approaches were used to present the statistical criterion for recognizing classes of objects based on the results of measuring their informative features. These approaches are used to solve optimization problems in statistical theories of identification and recognition. This method allowed the cartographic representing of the change in the water quality and aquatic ecosystem in accordance with the reference areas of the state of the reservoir in 2017.
Keywords: aquatic environment, methods of system analysis, NDAI, NDPI, NDTI, remote sensing, spectral indices, temperature map, water quality
1. Arkhipov A. I., Glazunov N. M., Khyzhniak A. V. (2018). Heuristic Criterion for Class Recognition by Spectral Brightness. Cybern. and Syst. Anal., 54, No. 1, 94—98.
2. Batoh S. V. (2017). The ecohydrological characteristics of Kyiv water bodies. Qualifying scientific work on the rights of manuscript: Doctoral dissertation. Kyiv [in Ukrainian].
3. Khyzhniak A. V., Tomchenko O. V., Porushkevych A. Yu. (2017). Application of the remote sensing in environmental management as the interdisciplinary approach. Cent. Eur. Res. J., 3, No. 1, 34—42.
4. Lacaux J-P., Tourre Y. M., Vignolle C., Ndione J-A., Lafaye M. (2001). Classification of Ponds from High-Spatial Resolution Remote Sensing: Application to Rift Valley Fever Epidemics in Senegal. Remote Sens. Environ., 66—74.
5. Method for remote assessment of ecological condition and water quality of inland waters. Reference publication “Prospective scientific and technical developments of the National Academy of Sciences of Ukraine”. Issue “Ecology and Environmental Protection”. Kyiv: Akademperiodika, 32, 2017 [in Ukrainian].
6. Piestova I., Lubskyi M., Svideniuk M., Golubov S., Sedlacek P. (2018). Satellite Imagery Resolution Enhancement for Urban Area Thermal Micromapping. Cent. Eur. Res. J., 4, No. 1, 35—39.
7. Protasov A. A., Barinova S. S., Novoselova T. N., Buseva Zh. F., Tomchenko O. V., Sylaieva A. A., Lubskiy N. S., Semenchenko V. P., Sysova E. A. (2019). The heterogeneity of the abiotic and biotic components of techno-ecosystems: view from space and from the Earth. Eur. Scientific J. Jan. edition 15, No. 3, 423—448.