Spatio-temporal change analysis and prediction of land use and land cover changes using CA-ANN model

The spatial and temporal representation of land use and land cover (LULC) changes helps to understand the interactions between natural habitats and other areas and to plan for sustainability. Research on the models used to determine the spatio-temporal change of LULC and simulation of possible future scenarios provides a perspective for future planning and development strategies. Landsat 5 TM for 1990, Landsat 7 ETM + for 2006, and Landsat 8 OLI for 2022 satellite imageries were used to estimate spatial and temporal variations of transition potentials and future LULC simulation. Independent variables (DEM, slope, and distances to roads and buildings) and the cellular automata-artificial neural network (CA-ANN) model integrated in the MOLUSCE plugin of QGIS were used. The CA-ANN model was used to predict the LULC maps for 2038 and 2054, and the results suggest that artificial surfaces will continue to increase. The Duzce City center's artificial surfaces grew by 100% between 1990 and 2022, from 16.04 to 33.10 km2, and are projected to be 41.13 km2 and 50.32 km2 in 2038 and 2054, respectively. Artificial surfaces, which covered 20% of the study area in 1990, are estimated to cover 64.07% in 2054. If this trend continues, most of the 1st-class agricultural lands may be lost. The study's results can assist local governments in their land management strategies and aid them in planning for the future. The results suggest that policies are necessary to control the expansion of artificial surfaces, ensuring a balanced distribution of land use.

Automated summary

The spatial and temporal changes in land use and land cover are crucial for understanding interactions between different areas and planning for future sustainability. In this study, satellite imagery and models were used to analyze the changes in land use and land cover, and predict future development. The results indicate a continued increase in artificial surfaces, which may result in the loss of agricultural land.