Key points are not available for this paper at this time.
The aim of this study was to identify areas at risk of land cover (LC) changes using the case study of the Sokółka municipality, utilizing advanced spatial analysis tools available in the GIS environment. The MOLUSCE module (Modules for Land Use Change Evaluation), operating within QGIS software, enables integrated analysis of spatial data using Artificial Neural Networks (ANN) and Cellular Automata (CA). The analysis encompassed data from the years 2014‑2023, derived from the BDOT10k and Digital Terrain Model databases, which allowed for the identification of areas at risk of changes that could cause spatial conflicts. Model validation demonstrated high effectiveness (Kappa coefficient of 0.97), confirming its suitability for predictive analyses. A total of 223.42 ha of land was identified as particularly vulnerable to land cover changes, located mainly in the central part of the municipality – at the interface of industrial areas, water reservoirs, and raw material extraction sites. The obtained results confirm the effectiveness of the applied tools and methods and emphasize the need to implement an informed spatial policy that accounts for the potential occurrence of spatial conflicts and the necessity of protecting areas of high environmental value.
Eźlakowski et al. (Fri,) studied this question.