Deforestation presents significant challenges for land-use monitoring, requiring advanced computational approaches to derive meaningful insights from spatio-temporal data. This paper introduces an interest-driven pattern mining framework to analyze deforestation dynamics in the Kroumiria region of Tunisia from 2001 to 2022. By leveraging frequent pattern mining techniques, specifically the FP-Growth algorithm, and validating the results through statistical permutation tests, our methodology ensures both interpretability and reliability. Unlike traditional GIS-based or predictive models, this approach prioritizes patterns exhibiting statistically significant variations before and after the 2011 revolution. Our findings highlight the effectiveness of integrating pattern mining with statistical validation to extract actionable knowledge from large-scale environmental datasets, ultimately enhancing decision-making processes in land management and spatial analytics.