IDENTIFICATION OF SMALL LAKES IN KAZAKHSTAN USING REMOTE SENSING DATA

Akmaral Tolepbayeva; Dinara Abiyeva; Roza Karagulova; Aigerim Tanbayeva; Zhanerke Sharapkhanova

doi:10.2298/IJGI241025005T

Authors

Akmaral Tolepbayeva Institute of Geography and Water Security, Laboratory of Geographic Information Systems, Almaty https://orcid.org/0000-0002-2194-3142
Dinara Abiyeva Institute of Geography and Water Security, Laboratory of Geographic Information Systems, Almaty https://orcid.org/0000-0002-1966-5569
Roza Karagulova Institute of Geography and Water Security, Laboratory of Geographic Information Systems, Almaty https://orcid.org/0000-0002-6626-8515
Aigerim Tanbayeva Institute of Geography and Water Security, Laboratory of Geographic Information Systems, Almaty; Kazakh National Agrarian Research University, Almaty https://orcid.org/0000-0003-3083-4383
Zhanerke Sharapkhanova Institute of Geography and Water Security, Laboratory of Geographic Information Systems, Almaty; Al-Farabi Kazakh National University, Department of Geography and Environmental Sciences, Almaty https://orcid.org/0000-0003-0904-2840

DOI:

https://doi.org/10.2298/IJGI241025005T

Keywords:

surface water, small lakes, remote sensing data, Sentinel-2, Kazakhstan

Abstract

The purpose of this study is to develop a methodology for automated determination of water surfaces and identification of small lakes in Kazakhstan using cartographic methods and an array of multi-time remote sensing (RS) data. The methodology involved automated surface water classification using multi-temporal Sentinel-2 satellite imagery (spanning the period 2016–2021, focusing on the warm months from May to September), Python-based processing on the Google Earth Engine platform, geographic information system (GIS) based morphometric analysis, and field validation to accurately identify and characterize small lakes in Kazakhstan. The study applied the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Vegetation Index (NDVI) to enhance surface water detection, reduce noise from vegetation, and improve the accuracy of lake boundary delineation from multi-temporal Sentinel-2 imagery. A technique for automated extraction of morphometric characteristics of small lakes has been developed, data on lake morphometry have been obtained. Verification against field measurements demonstrated a high degree of accuracy, with relative error rates of 12% for lake lengths and 13% for the widths. However, challenges such as dense vegetation, high salinity, and the color of shallow lake bottoms led to some classification errors, highlighting the need for further refinement of automated algorithms. As a result, a list of small lakes in Kazakhstan with a surface area from 1 to 10 km² was identified.

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IDENTIFICATION OF SMALL LAKES IN KAZAKHSTAN USING REMOTE SENSING DATA

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