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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 5, pp. 28-38

Method for determining the coastlines of water bodies based on processing Landsat ETM+ remote sensing data

A.S. Tertychnaya 1 , K.S. Tertychniy 1 , A.V. Khoperskov 1 
1 Volgograd State University, Volgograd, Russia
Accepted: 25.08.2023
DOI: 10.21046/2070-7401-2023-20-5-28-38
The problem of determining coastlines is important for various hydrological, ecological and geophysical studies. A complex system of water bodies of different sizes on the territory of large floodplains in the presence of plant and wetlands requires special approaches to identify the water bodies boundaries. Multi-channel satellite data including infrared ranges make it possible to reliably identify water bodies. We consider the efficiency of coastline detection based on the analysis of spatial data for two infrared bands (channels 4 and 5) of Landsat ETM+. A software for the selection of water bodies from two-channel infrared images has been created. The algorithm is tested on the example of the Volga-Akhtuba floodplain, which contains both the large Volga and Akhtuba rivers and a complex small-scale system of water bodies, including narrow watercourses (eriks and small channels) and lakes of various sizes and depths. The critical values of radiation intensities which make it possible to distinguish coastlines in the entire image are 40 and 42 for the 4th and 5th channels, respectively, for the territory of the northern part of the Volga-Akhtuba floodplain. We also analyzed the errors in the identification of water bodies when varying the critical values of radiation intensities.
Keywords: satellite data, near infrared range, water bodies, boundaries of water bodies, Volga-Akhtuba floodplain
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References:

  1. Bychkov I. V., Ruzhnikov G. M., Fedorov R. K. et al., Classification of Sentinel-2 satellite images of the Baikal Natural Territory, Computer Optics, 2022, Vol. 46, No. 1, pp. 90–96 (in Russian), DOI: 10.18287/2412-6179-CO-1022.
  2. Ginzburg A. I., Kostianoy A. G., Sheremet N. A. et al., The drying dynamics of the Western Large Aral Sea from satellite data (2002–2021), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 246–263 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-246-263.
  3. Klikunova A.Yu., Dyakonova T. A., Agafonnikova E. O. et al., Modeling of flooding of settlements during the spring flood, Mathematical Physics and Computer Simulation, 2021, Vol. 24, No. 3, pp. 63–72 (in Russian), https://doi.org/10.15688/mpcm.jvolsu.2021.3.6.
  4. Kravtsova V., Vachnina O., Lebedeva S. et al., Features of the methodology for studying the dynamics of tidal rivers deltas in the northern regions using satellite images (the northern Dvina as example), Geoinformatika, 2022, No. 1, pp. 17–31 (in Russian), DOI: 10.47148/1609-364X-2022-1-17-31.
  5. Polishchuk Yu.M., Muratov I. N., Baisalyamova O. A., Nabiullina P. A., Study of the tortuosity of thermokarst lake coastlines in the eastern Russian Arctic using Kanopus-V high-resolution images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 6, pp. 177–184 (in Russian), DOI: 10.21046/2070-7401-2021-18-6-177-184.
  6. Sterlyadkin V. V., Ermakov D. M., Kuzmin A. V., Pashinov E. V., Flood prediction on major rivers from radiometric microwave measurements from space. Is it possible? Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 40–52 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-40-52.
  7. Terekhin E. A., Indication of long-term changes in the vegetation of abandoned agricultural lands for the forest-steppe zone using NDVI time series, Computer Optics, 2021, Vol. 45, No. 2, pp. 245–252 (in Russian), DOI: 10.18287/2412-6179-CO-797.
  8. Terekhin E. A., Changes in intrazonal differences in the natural vegetation cover of forest-steppe landscapes in the late 20th and early 21st century, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 1, pp. 179–192 (in Russian), DOI: 10.21046/2070-7401-2022-19-1-179-192.
  9. Shinkarenko S. S., Kosheleva O.Yu., Solodovnikov D. A., Rulev A. S., Coastline dynamics of the Sarpinsky Island on the Lower Volga, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 5, pp. 120–129 (in Russian), DOI: 10.21046/2070-7401-2019-16-5-120-129.
  10. Shinkarenko S. S., Bartalev S. A., Berdengalieva A. N., Doroshenko V. V., Satellite monitoring of desertification processes in the south of European Russia in 2019–2022, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 319–327 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-319-327.
  11. Abou Samra R. M., Ali R. R., Applying DSAS tool to detect coastal changes along Nile Delta, Egypt, Egyptian J. Remote Sensing and Space Science, 2021, Vol. 24(3), pp. 463–470, https://doi.org/10.1016/j.ejrs.2020.11.002.
  12. Beale J., Grabowski R. C., Long’or Lokidor P. et al., Vegetation cover dynamics along two Himalayan rivers: Drivers and implications of change, Science of the Total Environment, 2022, Vol. 849, Article 157826, https://doi.org/10.1016/j.scitotenv.2022.157826.
  13. Bishop-Taylor R., Nanson R., Sagar S., Lymburner L., Mapping Australia’s dynamic coastline at mean sea level using three decades of Landsat imagery, Remote Sensing of Environment, 2021, Vol. 267, Article 112734, https://doi.org/10.1016/j.rse.2021.112734.
  14. Boori M. S., Choudhary K., Kupriyanov A. V., Crop growth monitoring through Sentinel and Landsat data based NDVI time-series, Computer Optics, 2020, Vol. 44, No. 3, pp. 409–419, DOI: 10.18287/2412-6179-CO-635.
  15. Brown A. G., Lespez L., Sear D. A. et al., Natural vs anthropogenic streams in Europe: History, ecology and implications for restoration, river-rewilding and riverine ecosystem services, Earth-Science Reviews, 2018, Vol. 180, pp. 185–205, https://doi.org/10.1016/j.earscirev.2018.02.001.
  16. Hauer C., Flödl P., Habersack H., Pulg U., Critical flows in semi-alluvial channels during extraordinarily high discharges: Implications for flood risk management, J. Flood Risk Management, 2021, Vol. 14, No. 4, Article e12741, https://doi.org/10.1111/jfr3.12741.
  17. Isaeva I. I., Voronin A. A., Khoperskov A. V., Kharitonov M. A., Modeling the Territorial Structure Dynamics of the Northern Part of the Volga-Akhtuba Floodplain, Computation, 2022, Vol. 10, No. 4, Id. 62, https://doi.org/10.3390/computation10040062.
  18. Khrapov S. S., Khoperskov A. V., Application of Graphics Processing Units for Self-Consistent Modelling of Shallow Water Dynamics and Sediment Transport, Lobachevskii J. Mathematics, 2020, Vol. 41, No. 8, pp. 1475–1484, https://doi.org/10.1134/ S1995080220080089.
  19. Klemas V. V., Remote sensing of landscape-level coastal environmental indicators, Environmental Management, 2001, Vol. 27, No. 1, pp. 47–57, DOI: 10.1007/s002670010133.
  20. Klikunova A.Yu., Khoperskov A. V., Creation of digital elevation models for river floodplains, CEUR Workshop Proc., 2019, Vol. 2391, pp. 275–284, https://doi.org/10.48550/arXiv.1908.09005.
  21. Kuz’mina Z. V., Shinkarenko S. S., Solodovnikov D. A., Main Tendencies in the Dynamics of Floodplain Ecosystems and Landscapes of the Lower Reaches of the Syr Darya River under Modern Changing Conditions, Arid Ecosystems, 2019, Vol. 9, No. 4, pp. 226–236, DOI: 10.1134/s 207909611904005x.
  22. Markham B. L., Helder D. L., Forty-year calibrated record of earth-reflected radiance from Landsat: A review, Remote Sensing of Environment, 2012, Vol. 122, pp. 30–40, DOI: 10.1016/j.rse.2011.06.026.
  23. Matongera T. N., Mutanga O., Sibanda M., Odindi J., Estimating and monitoring land surface phenology in rangelands: A review of progress and challenges, Remote Sensing, 2021, Vol. 13, No. 11, Article 2060, https://doi.org/10.3390/rs13112060.
  24. Polidori L., Caldeira C. R.T., Smessaert M., El Hage M., Digital elevation modeling through forests: the challenge of the Amazon, Acta Amazonica, 2022, Vol. 52, No. 1, pp. 69–80, DOI: 10.1590/1809-4392202103091.
  25. Rivera-Marin D., Dash J., Ogutu B., The use of remote sensing for desertification studies: A review, J. Arid Environments, 2022, Vol. 206, Article 104829, DOI: 10.1016/j.jaridenv.2022.104829.
  26. Singh A., Vyas V., A Review on remote sensing application in river ecosystem evaluation, Spatial Information Research, 2022, Vol. 30, No. 6, pp. 759–772, DOI: 10.1007/s41324-022-00470-5.
  27. Zhao Y., Zhu Z., ASI: An artificial surface Index for Landsat-8 imagery, Intern. J. Applied Earth Observation and Geoinformation, 2022, Vol. 107, Article 102703, https://doi.org/10.1016/j.jag.2022.102703.