
Las subcuencas La Sabana y Tres Palos (Guerrero, México) enfrentan presiones ambientales por expansión urbana no regulada, con vacíos en estudios predictivos que integren variables geoespaciales. Este trabajo analiza cambios históricos (1993–2018) y proyecta escenarios al 2030 y 2050 mediante el módulo Land Change Modeler (LCM) del software TerrSet, utilizando su algoritmo de Perceptrón Multicapa (MLP). Se emplearon variables estáticas (elevación, pendientes, distancia a drenaje) y dinámicas (cercanía a carreteras y localidades). Los resultados revelan una pérdida del 96,1 % en bosques primarios (de 11 699,3 ha en 1993 a 454,3 ha en 2018) y un incremento del 255,4 % en zonas urbanas (de 2 547,8 ha a 9 054,5 ha). Las proyecciones (validadas con Kstandard = 0,877) indican que la superficie urbana alcanzará 14 687,5 ha (19,9 % del área total) para 2050, desplazando 1 800,5 ha de vegetación secundaria (de 32 311,5 ha en 2018 a 30 511,0 ha en 2050) y 268,3 ha agrícolas. Estos cambios conllevarían impactos críticos: (1) fragmentación de corredores biológicos por la pérdida del 73 % adicional de bosques primarios entre 2018 y 2050, (2) reducción de la recarga hídrica, exacerbada por la pérdida del 61,4 % de pastizales (de 5 893,3 ha a 2 271,8 ha), y (3) aumento en zonas inundables, asociado a la expansión urbana en áreas adyacentes a humedales, que aunque crecen (de 690,3 ha a 2 151,3 ha en 2050), no compensan la pérdida de cobertura permeable. Se concluye que integrar herramientas como el MLP en políticas públicas es clave para impulsar estrategias de ordenamiento basadas en zonificación de riesgos y corredores de restauración, priorizando regiones tropicales vulnerables como Guerrero, donde convergen biodiversidad y presión socioambiental.
The La Sabana and Tres Palos sub-basins (Guerrero, Mexico) face environmental pressures from unregulated urban expansion, with gaps in predictive studies that integrate geospatial variables. This study analyzes historical changes (1993–2018) and projects scenarios for 2030 and 2050 using the Land Change Modeler (LCM) module of TerrSet software, employing its Multilayer Perceptron (MLP) algorithm. Static variables (elevation, slope, distance to drainage) and dynamic variables (proximity to roads and settlements) were used. The results reveal a 96.1% loss of primary forests (from 11,699.3 ha in 1993 to 454.3 ha in 2018) and a 255.4% increase in urban areas (from 2,547.8 ha to 9,054.5 ha). The projections (validated with Kstandard = 0.877) indicate that urban area will reach 14,687.5 ha (19.9% of the total area) by 2050, displacing 1,800.5 ha of secondary vegetation (from 32,311.5 ha in 2018 to 30,511.0 ha in 2050) and 268.3 ha of agricultural land. These changes would entail critical impacts: (1) fragmentation of biological corridors due to an additional 73% loss of primary forests between 2018 and 2050, (2) reduced groundwater recharge, exacerbated by the 61.4% loss of grasslands (from 5,893.3 ha to 2,271.8 ha), and (3) increased flood-prone areas, associated with urban expansion into areas adjacent to wetlands, which, although they increase (from 690.3 ha to 2,151.3 ha by 2050), do not compensate for the loss of permeable cover. It is concluded that integrating tools such as MLP into public policy is key to promoting land-use planning strategies based on risk zoning and restoration corridors, prioritizing vulnerable tropical regions such as Guerrero, where biodiversity and socio-environmental pressures converge.
cambio de uso de suelo, land-use change, ordenamiento territorial, expansión urbana, modelación predictiva, multilayer perceptron, land-use change, urban expansion, multilayer perceptron, predictive modeling, land-use planning, cambio de uso de suelo, expansión urbana, perceptrón multicapa, modelación predictiva, ordenamiento territorial, TerrSet, LCLUC, Guerrero, México, perceptrón multicapa, predictive modeling, land-use planning, urban expansion
cambio de uso de suelo, land-use change, ordenamiento territorial, expansión urbana, modelación predictiva, multilayer perceptron, land-use change, urban expansion, multilayer perceptron, predictive modeling, land-use planning, cambio de uso de suelo, expansión urbana, perceptrón multicapa, modelación predictiva, ordenamiento territorial, TerrSet, LCLUC, Guerrero, México, perceptrón multicapa, predictive modeling, land-use planning, urban expansion
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