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, Salavat Duisenbayev, Edil Sarybaev and Zhanarys Turymtayev
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Moldir Seitkazy: Space Technologies, and Remote Sensing Center, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Nail Beisekenov: Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Niigata, Japan
Omirzhan Taukebayev: Space Technologies, and Remote Sensing Center, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Kanat Zulpykharov: Space Technologies, and Remote Sensing Center, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Aigul Tokbergenova: Department of Geography, Land Management, and Cadastre, Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Salavat Duisenbayev: Department of Geography, Land Management, and Cadastre, Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Edil Sarybaev: Department of Cartography and Geoinformatics, Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan
Zhanarys Turymtayev: Space Technologies, and Remote Sensing Center, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan

Sustainability, 2024, vol. 16, issue 14, 1-18

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 km 2 of herbaceous wetlands, 2271.41 km 2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km 2 . 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.

Keywords: land use; land cover; forecasting; sustainability; remote sensing; GEE; Landsat satellite data; ecological impact (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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