Short-Term Agricultural Landscape Dynamics: A Quantitative Analysis Using AI-Supported LULC Data and Landscape Metrics

OpenAlex İngilizce

This study aims to investigate the short-term dynamics of agricultural landscapes using AI-supported multi-temporal land use/land cover (LULC) data. The Finike district, located within the Mediterranean climate zone, was selected as the study area, and 10 m spatial resolution ArcGIS Living Atlas LULC raster datasets for the years 2017 and 2024 were used. Spatial dynamics of agricultural areas were analyzed using Fragstats by quantifying changes in area and dominance (CA, PLAND), fragmentation and patch density (NP, PD), spatial integrity and largest patch structure (LPI), shape complexity (PARA_MN), and aggregation–connectivity patterns (CLUMPY, AI), thereby providing a comprehensive assessment of fragmentation, dispersion, clustering, and landscape cohesion over time. The analyses were conducted specifically for the agricultural class for both class-level and landscape-level metrics. The findings indicate that agricultural areas, which covered approximately 3128 hectares in 2017, decreased to 2643 hectares by 2024, as shown in the quantitative results of landscape metrics, accompanied by a pronounced increase in fragmentation. The increase in the number of patches, the decrease in mean patch size, and the rise in patch density demonstrate that the agricultural landscape has transformed into a more fragmented and irregular structure. The results further reveal a weakening of spatial integrity in agricultural areas, suggesting increased pressure from land use change processes, particularly urban expansion, in the study area, and highlighting potential risks for land management, agricultural sustainability, and ecological functions. Overall, the study highlights that the integrated use of high-resolution, AI-supported LULC data and landscape metrics provides a robust and effective framework for monitoring short-term dynamics in agricultural landscapes and supporting evidence-based planning processes.