SURFACE WATER MONITORING IN KAZAKHSTAN USING NDWI AND RANDOM FOREST: A CASE STUDY OF LAKE AKKOL

For nations like Kazakhstan, where dry and semi-arid climates together with human activity put increasing strain on lakes and rivers, monitoring water resources has become ever more crucial in recent years. Accurate and timely information on surface water dynamics is essential for effective water management, environmental protection, and adaptation to climate change. Advances in remote sensing technologies, particularly the use of indices like NDWI and machine learning algorithms such as Random Forest, have significantly enhanced the ability to detect and analyze surface water changes over time. These tools offer scalable, cost-effective solutions for continuous monitoring, especially in remote and vast landscapes typical of Central Asia. This work offers a useful method based on the Normalized Difference Water Index (NDWI) to detect water bodies. Each of the tools we used – QGIS, Python and Google Earth Engine (GEE) – had unique benefits for the work. We applied a supervised Random Forest technique using several spectral bands and indices to separate water covered from dry areas. Examining seasonal and long term fluctuations in water levels, our main case study was on Lake Akkol in the Zhambyl Region. To grasp their influence on local water dynamics, we also examined information from the Assy and Talas rivers. The consistent and dependable results across platforms underlined the great spatial and temporal heterogeneity of water distribution in the area and supported the need for continuous satellite based monitoring.