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. Predictive modeling for 2035 suggests forest cover will increase by 7%, reaching 77% coverage of the study area, and closed forest areas will rise by 12% compared to 1994. The findings underscore the effectiveness of conservation efforts and natural regeneration in enhancing forest cover, offering valuable insights for global forest management and policy-making efforts.