Polygonal Vectorization and Spatial Assessment of Water Bodies in Krasnokutsk Using Remote Sensing and QGIS Tools

Remote Sensing for Environmental Monitoring

Authors

First and Last Name Academic degree E-mail Affiliation
Ruslan Kalashnikov No ruslancandyy [at] gmail.com V.N. Karazin Kharkiv National University
Kharkiv, Ukraine
Ruslan Lysak No ruslan2287406 [at] gmail.com V.N. Karazin Kharkiv National University
Poltava, Ukraine
Andrii Achasov Ph.D. achasov [at] karazin.ua V.N. Karazin Kharkiv National University
Kharkiv, Ukraine
Arkady Siedov No shakhmet1985 [at] gmail.com State Biotechnological University
Kharkiv, Ukraine
Oleg Seliverstov No oleg.seliverstov [at] karazin.ua V.N. Karazin Kharkiv National University
Kharkiv, Ukraine

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 26.07.2025 - 21:16
Abstract 

The study outlines a practical approach for identifying and mapping surface water bodies in the Krasnokutsk territorial community region of Ukraine, based entirely on open-source orthophoto imagery and geospatial analysis in QGIS. The research addresses the challenge of accurately delineating water features in territories where spectral satellite data is either insufficient or inapplicable, particularly in the absence of reliable hydrological inventories at the local level.

The goal was to develop a reliable and repeatable mapping technique that uses only visual sources, without applying spectral indices or automated classifications. The approach is based on manual interpretation of high-resolution imagery from platforms such as Google Earth, Bing Maps, and ESRI Imagery, combined with a topographic map of the area.

All raster layers were manually georeferenced in the coordinate reference system WGS 84 / Pseudo-Mercator (EPSG:3857). Water bodies were identified based on their shape, color, and location in the landscape. Multi-year imagery comparisons helped to distinguish between permanent and seasonal features. The digitization process was done manually using QGIS tools, with line geometry applied to rivers and streams, and polygon geometry for lakes, ponds, and oxbows.

Each water body was assigned to one of four categories according to its type and hydrological behavior.

The resulting workflow offers a practical solution for mapping hydrographic features in regions with limited technical resources, and can be applied in research, education.

 

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