A long‑term analysis, modeling and drivers of forest recovery in Central Mexico

Abstract

This study aims to evaluate the changes in forest cover from 1994 to 2015, identify the key drivers of forest recovery, and predict future trends. Using high-resolution remote sensing data, we mapped forest canopy density into detailed categories (closed > 50%, open 10–50%, and deforested < 10%) to differentiate processes like degradation, deforestation, densification, reforestation, and afforestation. A multinomial logistic regression was used to explore the relationship between the forest processes and socioeconomic, proximity, planning, and policy potential drivers. Future trends were modeled using the Land Change Modeler. The analysis showed that 81.5% of the area remained unchanged, 14% experienced recovery, and 4.5% faced disturbances. Factors such as elevation, proximity to roads, and participation in payment for environmental services (PES) programs significantly influenced recovery trends.