Tracking Forest Change in Peri-Urban Landscapes of Mexico City Using Landsat Imagery and Neural Network Regression

Peri-urban forest ecosystems play a crucial role in sustaining biodiversity, regulating climate, and providing essential ecosystem services; however, they are increasingly threatened by rapid urban expansion. Despite advances in remote sensing-based forest monitoring, long-term reconstruction of continuous forest canopy cover dynamics remains challenging in highly fragmented peri-urban landscapes. This study developed a machine-learning workflow to reconstruct forest canopy cover dynamics within the “Suelo de Conservación” of Mexico City between 1994 and 2024 using Landsat imagery and forest canopy cover information derived from the Hansen Global Forest Change dataset. A balanced training dataset comprising 5000 samples distributed across five forest canopy cover classes was used to compare four regression algorithms (Multiple Linear Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron) under five-fold spatial cross-validation. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), bias, Pearson’s correlation coefficient (r), coefficient of determination (R2), and Lin’s Concordance Correlation Coefficient (CCC). The best-performing model was applied to generate forest canopy cover maps for 1994, 2003, 2014, and 2024, and forest-cover change was quantified using propagated uncertainty and threshold sensitivity analysis. The reconstructed forest canopy cover maps revealed an initial decline between 1994 and 2003, followed by partial recovery during 2003–2014 and relatively stable forest canopy cover conditions through 2024. Independent comparison with the National Forest and Soils Inventory (INFyS) and Global Forest Watch forest canopy cover products indicated moderate agreement in the spatial distribution of canopy cover while highlighting uncertainties associated with differences in reference datasets and acquisition periods. The proposed workflow provides a transparent and reproducible framework for long-term forest canopy cover reconstruction using freely available satellite imagery and supports forest monitoring and conservation planning in peri-urban landscapes.