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Article · Sustainability · July 18, 2024

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

Moldir Seitkazy, Nail Beisekenov, Omirzhan Taukebayev, Kanat Zulpykharov, Aigul Tokbergenova, С.М. Дуйсенбаев

Nail Beisekenov
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Abstract

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Remote Sensing and Land UseLand Use and Ecosystem ServicesRemote-Sensing Image Classificationland use change forecastingland cover changeZhambyl region

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