Advanced Machine Learning Methods as a Planning Strategy in the Capellanía Wetland

This study evaluated the spatio-temporal dynamics of vegetation cover in the Capellanía wetland (Bogotá, Colombia) between 2013 and 2032 through spectral indices, machine learning, and spatial simulation. A multitemporal Random Forest model (R2 = 0.991; RMSE = 0.0214; MAE = 0.0127) was integrated with cellular automata (MOLUSCE) to project vegetation trajectories under different urban growth scenarios. NDVI-based classification revealed a marked transition: degraded classes (bare soil and sparse vegetation) decreased from over 80% in 2013 to less than 10% in 2032, while moderate and dense vegetation surpassed 90%. Cellular automata achieved moderate agreement (Kappa = 0.640) and high internal calibration (pseudo-R2 = 1.00); the transition matrix in scenario II, simulating the construction of the Avenida Longitudinal de Occidente (ALO), indicated a conversion 0→1 = 0.414 and persistence 1→1 = 0.709, evidencing intense urbanization pressure in peripheral areas. The Shannon index confirmed recovery but highlighted structural homogenization, underscoring the need to preserve heterogeneity to sustain ecosystem resilience. Scenario analysis showed that the ALO would act as a catalyst for urban expansion, threatening ecological connectivity and increasing pressure on vegetation. Overall, this study provides quantitative, spatial, and prospective evidence to promote preventive, integrated, and data-driven approaches for the conservation of strategic urban wetlands.