Spatıotemporal analysıs of urban development and land USE in sakarya provınce, Türkiye: ımplıcatıons for future urban growth modelıng

Abstract Understanding urban expansion patterns is critical for sustainable land-use planning, particularly in rapidly developing regions. This study employs an integrated geographic ınformation system-based approach utilizing the MOLUSCE plugin in QGIS to predict future Land Use and Land Cover (LULC) changes in Sakarya Province, Türkiye, from 2011 to 2040. While cellular automata-artificial neural network and Markov Chain models have been extensively utilized in LULC research, their application to mid-sized Turkish provinces remains insufficiently explored. This study addresses that gap by providing the first comprehensive spatiotemporal analysis of Sakarya’s urban growth trends, incorporating environmental impact assessments alongside predictive modeling. The results indicate a 3.43% decline in agricultural areas and a 0.43% decrease in forests from 2011 to 2024, with continued urban expansion projected through 2040. These findings underscore the urgent need for sustainable zoning policies and conservation strategies to mitigate the adverse effects of urbanization on natural resources. The study not only enhances regional LULC modeling efforts but also provides a scalable framework for other developing urban centers facing similar land-use challenges.