Classification of Land Use and Vegetation in Marismas Nacionales, Mexico
Main Article Content
Abstract
In recent decades, satellite imagery and GIS technologies have transformed land-use analysis, enabling more precise and accessible studies. In Mexico, efforts such as the National Forest and Soil Inventory, INEGI’s Land Use and Vegetation Series, and mangrove inventories provide key information, although they present limitations that generate inconsistencies in areas such as Marismas Nacionales. In this region, official classifications do not match current on-the-ground conditions, often identifying agricultural or aquaculture areas as natural vegetation. This study produced an updated 2022 land-use map using Landsat 8 imagery, classified through supervised methods and manual editing, integrating fieldwork and accuracy assessment. A total of 19 land-cover classes were defined, revealing a highly transformed landscape dominated by agriculture but still containing key ecosystems such as mangroves, wetlands, and tropical forests. As a final product, an interactive digital map was developed for public consultation and to support environmental decision-making processes.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
PLUMX Metrics
References
Blanco y Correa, M. et al. (2011) Diagnóstico Funcional de Marismas Nacionales.
Bocco, G., Mendoza, M. and Masera, O. R. (2001) ‘La dinámica del cambio del uso del suelo en Michoacán.Una propuesta metodológica para el estudio de los procesos de deforestación’, Investigaciones Geograficas, 44, pp. 18–38. doi: 10.14350/rig.59133.
Chen, C. et al. (2024) ‘A new strategy based on multi- source remote sensing data for improving the accuracy of land use / cover change classification’, Nature Scientific Reports, 14(26855), pp. 1–28.
CONABIO (2020) Distribución de los manglares en México.
CONABIO (2022) Extensión y distribución de manglares. Sistema de Monitoreo de Manglares de México (SMMM).
Congedo, L. (2021) ‘Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS’, Journal of Open Source Software, 6(64), p. 3172. doi: 10.21105/joss.03172.
Cowardin, L. M. et al. (1979) Classification of wetlands and deepwater habitats of the United States. Edited by U. D. of the I. Fish and Wildlife Service. U.S. Department of the Interior.
Hemati, M. et al. (2021) ‘A Systematic Review of Landsat Data for Change Detection Applications : 50 Years of Monitoring the Earth’, Remote Sensing, 13(2869), p. 33.
IDEFOR (2025) Plataforma geoespacial de datos forestales, CONAFOR. Available at: https://idefor.cnf.gob.mx/?utm_source=chatgpt.com.
INEGI (2021) ‘Serie VII. Uso del Suelo y Vegetación. Escala 1:250,000’.
Lillesand, T. and Kiefer, R. W. (2014) Remote sensing and image interpretation. Edited by J. W. & Sons.
Macarringue, L. S. et al. (2022) ‘Developments in Land Use and Land Cover Classification Techniques in Remote Sensing : A Review’, Journal of Geographic Information System, 14(February), pp. 1–28. doi: 10.4236/jgis.2022.141001.
Mas, J.-F., Velázquez, A. and Couturier, S. (2009) ‘La evaluación de los cambios de cobertura / uso del suelo en la República Mexicana’, Investigación Ambiental, 1(1), pp. 23–39.
Patil, M. B., Desai, C. G. and Umrikar, B. N. (2012) ‘Image Classification Tool for Land Use / Land Cover Analysis : A Comparative Study of Maximum Likelihood’, International Journal of Geology, Earth, and Environmental Sciences, 2(3), pp. 189–196.
Ricker, M., Villela, S. A. and Mondragón, E. (2020) Información por conglomerado del Inventario Nacional Forestal y de Suelos (INFyS) de México. Edited by CONAFOR. México. doi: 10.13140/RG.2.2.35451.54568.
Rosete, F. A., Damián, J. L. P. and Bocco, G. (2008) ‘Cambio de uso del suelo y vegetación en la península de Baja California, México’, Investigaciones Geograficas, 67, pp. 39–58.
Rzedowski, J. and Huerta, L. (1978) Vegetación de México. Edited by E. Limusa. México.
Salinas, C. et al. (2025) ‘Land use change for shrimp aquaculture ponds and its impact on water use in Marismas Nacionales , Mexico’, Wetlands Ecology and Management.
Springer Netherlands, (3917). doi: 10.1007/s11273-025-10048-1.
Salitchev, K. A. (1979) Cartografía. Edited by Editorial Pueblo y Educación. La Habana, Cuba.
Zhang, X. et al. (2024) ‘GLC _ FCS30D : the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method’, pp. 1353–1381.




