Article

Spatiotemporal analysis and identifying the driving forces of land use change in the Abay district (Karagandy Region, Kazakhstan)

Onggarbek AlipbekiAl-Farabi Kazakh National University, Department of Cartography and Geoinformatics, Almaty, KazakhstanPavel GrossulS.Seifullin Kazakh Agrotechnical Research University, Department of Geodesy and Cartography, Astana, KazakhstanDaniyar RakhimovAl-Farabi Kazakh National University, Department of Cartography and Geoinformatics, Almaty, KazakhstanErassyl AlikenAl-Farabi Kazakh National University, Department of Cartography and Geoinformatics, Almaty, KazakhstanKairat AugambayevAl-Farabi Kazakh National University, Department of Cartography and Geoinformatics, Almaty, KazakhstanChaimgul AlipbekovaKazakh National Women’s Pedagogical University, Department of Biology, Almaty, KazakhstanMaira BegaliyevaAl-Farabi Kazakh National University, Department of Geography, Land Management and Cadastre, Almaty, KazakhstanAlisher Ravshanov"TIIAME" National Research University, Department of Humanities, str. Kory Niyoziy, 39, Tashkent 100000, Uzbekistan

E3S Web of Conferencesjournal2024en

ABI

Abstract

Land use and cover change (LUCC) affects the nature of human activities in a particular area. Therefore, the manifestation of the driving forces of these changes plays a decisive role. This paper analyses the LULC dynamics of the Abay district of Karagandy oblast from 2016 to 2023. The study’s main objective is to find the driving forces of land use based on the integrated assessment of spatio-temporal data (STD) and socio-economic, climatic and environmental indicators (SECEI). Classification of Sentinel- 2 images into LULC classes is carried out using the Random Forest (RF) algorithm on the Google Earth Engine (GEE) platform. The driving factors were assessed using principal component analysis (PCA) and linear regression (LR). The results obtained can be used to guide the development planning of the territory.

Topics

Land Use and Ecosystem Services Soil and Environmental Studies Remote Sensing in Agriculture

Identifiers

DOI: 10.1051/e3sconf/202459004007

Citations and references

Cited by 017 references

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