ArticleEnvironmental monitoring and assessment2024
A long-term analysis, modeling and drivers of forest recovery in Central Mexico.
Article in Environmental monitoring and assessment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Post-Fire Forest Pulse Recovery: Superiority of Generalized Additive Models (GAM) in Long-Term Landsat Time-Series Analysis.Sensors (Basel, Switzerland) · 2026Article
- Multi-branch and multi-label tree species classification using deep learning for UAV aerial photography and Sentinel remote sensing images.Scientific reports · 2025Article
- A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques.BMC plant biology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.