Аннотация
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.