Vol. 36 (2026): Volumen 36
Artículos de Investigación

Modelado espaciotemporal del paisaje mediante autómatas celulares: una aproximación prospectiva en la cuenca de la Presa Solís

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  • Martha Sofia Medina Acuña,
  • Michelle Farfán Gutierréz,
  • Ismael Orozco Medina
DOI: https://doi.org/10.15174/au.2026.4609

Publicado 2026-03-04

Cómo citar

Medina Acuña, M. S., Farfán Gutierréz, M., & Orozco Medina, I. (2026). Modelado espaciotemporal del paisaje mediante autómatas celulares: una aproximación prospectiva en la cuenca de la Presa Solís. Acta Universitaria, 36, 1–13. https://doi.org/10.15174/au.2026.4609

Derechos de autor 2026 Martha Sofia Medina Acuña, Michelle Farfán Gutierréz, Ismael Orozco Medina

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

Resumen

Anticipar el cambio en la cobertura y uso de suelo es fundamental para comprender y mitigar sus repercusiones en el ciclo hidrológico, la recarga de acuíferos, los riesgos de sequías, las inundaciones y la degradación ambiental. Es por ello que este estudio se enfocó en evaluar, mediante autómatas celulares, los cambios en la cobertura y uso de suelo proyectados hasta el año 2035. Lo anterior se realizó usando como caso de estudio la cuenca de aportación a la presa Solís. Los resultados muestran un incremento del 90.54% en la superficie destinada a uso urbano, una reducción del 25.07% en el área de bosques y una disminución del 2.72% en las tierras agropecuarias. Se resalta la necesidad de impulsar instrumentos de planificación territorial que promuevan prácticas de uso responsable del suelo y que favorezcan la restauración y protección de la cobertura forestal.

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Citas

  1. Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93. https://doi.org/10.1016/j.tjem.2018.08.001
  2. Alaoui, A., Rogger, M., Peth, S., & Blöschl, G. (2018). Does soil compaction increase floods? A review. Journal of Hydrology, 557, 631–642. https://doi.org/10.1016/j.jhydrol.2017.12.052
  3. Al-sharif, A. A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291–4301. https://doi.org/10.1007/s12517-013-1119-7
  4. Barcia-Sardiñas, S., Fontes-Leandro, M., Ramírez-González, M., & Viera-González, E. Y. (2019). La sequía meteorológica 2014-1017, características e impactos en la provincia Cienfuegos. Revista Cubana de Meteorología, 25, 319–333. https://www.redalyc.org/journal/7019/701977516007/html/
  5. Barni, P. E. (2009). Reconstrução e asfaltamento da Rodovia BR-319: Efeito “dominó” pode elevar as taxas de desmatamento no Sul do Estado de Roraima [Tesis]. Universidad Estatal de Roraima. https://doi.org/10.13140/RG.2.2.33413.50403
  6. Boavida-Portugal, I., Rocha, J., & Ferreira, C. C. (2016). Exploring the impacts of future tourism development on land use/cover changes. Applied Geography, 77, 82–91. https://doi.org/10.1016/j.apgeog.2016.10.009
  7. Bucała, A. (2014). The impact of human activities on land use and land cover changes and environmental processes in the Gorce Mountains (Western Polish Carpathians) in the past 50 years. Journal of Environmental Management, 138, 4–14. https://doi.org/10.1016/j.jenvman.2014.01.036
  8. Camacho, M. T., García-Álvarez, D., Gallardo, M., Mas, J. F., Paegelow, M., Castillo-Santiago, M. A., & Molinero-Parejo, R. (2022). Validation of land use cover maps: a guideline. En D. García-Álvarez, M. T. Camacho-Olmedo, M. Paegelow & Mas, J. F. (eds.), Land use cover datasets and validation tools (pp. 35-46). Springer. https://doi.org/10.1007/978-3-030-90998-7_3
  9. Camacho-Sanabria, J. M., Juan-Pérez, J. I., & Pineda-Jaimes, N. B. (2015). Modeling of land use/cover changes: prospective scenarios in the Estado de Mexico. Case study - Amanalco de Becerra. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 21(2), 203-220. https://doi.org/10.5154/r.rchscfa.2014.10.049
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://www.semanticscholar.org/paper/A-Coefficient-of-Agreement-for-Nominal-Scales-Cohen/9e463eefadbcd336c69270a299666e4104d50159
  11. Denvir, A. (2023). Avocado expansion and the threat of forest loss in Michoacán, Mexico under climate change scenarios. Applied Geography, 151, 102856. https://doi.org/10.1016/j.apgeog.2022.102856
  12. Díaz, U. S., López, R., Ortiz, T. L., Torres, I., & Galván, E. (2019). Análisis del índice de sequía en caudales (SDI) empleando escurrimiento natural en la cuenca mexicana del río Lerma. Aqua-LAC, 11(2), 17–28. https://doi.org/10.29104/phi-aqualac/2019-v11-2-02
  13. Dinámica EGO. (2024). [Documentation]. https://dinamicaego.com/dokuwiki/doku.php?id=start
  14. Escobar, B. (2006). La cuenca Lerma-Chapala: el agua de la discordia. Gestión y Política Pública, 15(2), 369–392. https://www.redalyc.org/pdf/133/13315204.pdf
  15. Fleiss, J. L., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions (3a ed.). John Wiley & Sons. https://onlinelibrary.wiley.com/doi/book/10.1002/0471445428
  16. Gharbia, S. S., Alfatah, S. A., Gill, L., Johnston, P., & Pilla, F. (2016). Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Modeling Earth Systems and Environment, 2(151), 1–20. https://doi.org/10.1007/s40808-016-0210-y
  17. Gobierno del Estado de Guanajuato. (14 de mayo de 2024). Abren Presa Solís para riego del Primavera – Verano 2024. https://boletines.guanajuato.gob.mx/2024/05/14/abren-presa-solis-para-riego-del-primavera-verano-2024/
  18. Gomes, E., Inácio, M., Bogdzevič, K., Kalinauskas, M., Karnauskaitė, D., & Pereira, P. (2021). Future land-use changes and its impacts on terrestrial ecosystem services: a review. Science of the Total Environment, 781, 146716. https://doi.org/10.1016/j.scitotenv.2021.146716
  19. Hossain, A., Krupnik, T. J., Timsina, J., Mahboob, M. G., Chaki, A. K., Farooq, M., Bhatt, R., Fahad, S., & Hasanuzzaman, M. (2020). Agricultural Land Degradation: Processes and Problems Undermining Future Food Security. En Fahad, S., et al. Environment, Climate, Plant and Vegetation Growth. Springer, Cham. https://doi.org/10.1007/978-3-030-49732-3_2
  20. Instituto Nacional de Estadística y Geografía (INEGI). (2021). Tipos de clima. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825199258
  21. Jaman, T., Dharanirajan, K., & Rana, S. (2022). Land use and land cover change detection and its environmental impact on South Andaman Island, India using Kappa coefficient statistical analysis and geospatial techniques. International Research Journal of Engineering and Technology (IRJET), 9(4), 281-288. https://www.researchgate.net/publication/359822029
  22. Kuhnle, R. A., Bingner, R. L., Foster, G. R., & Grissinger, E. H. (1996). Effect of land use changes on sediment transport in Goodwin Creek. Water Resources Research, 32(10), 3189–3196. https://doi.org/10.1029/96WR02104
  23. Leija-Loredo, E. G., Pavón, N. P., Sánchez-González, A., Rodríguez-Laguna, R., & Ángeles-Pérez, G. (2018). Land cover change and carbon stores in a tropical montane cloud forest in the Sierra Madre Oriental, Mexico. Journal of Mountain Science, 15, 2136–2147. https://doi.org/10.1007/s11629-018-4937-y
  24. Llena, M., Vericat, D., Cavalli, M., Crema, S., & Smith, M. W. (2019). The effects of land use and topographic changes on sediment connectivity in mountain catchments. Science of the Total Environment, 660, 899–912. https://doi.org/10.1016/j.scitotenv.2018.12.479
  25. Maheng, D., Pathirana, A., & Zevenbergen, C. (2021). A preliminary study on the impact of landscape pattern changes due to urbanization: case study of Jakarta, Indonesia. Land, 10(2), 1–27. https://doi.org/10.3390/land10020218
  26. Niemelä, J., Kotze, J., Ashworth, A., Brandmayr, P., Desender, K., New, T., Penev, L., Samways, M., & Spence, J. (2000). The search for common anthropogenic impacts on biodiversity: a global network. Journal of Insect Conservation, 4(1), 3–9. https://doi.org/10.1023/A:1009655127440
  27. Ossa, J. C. (2013). Matrices de transición y patrones de variabilidad cognitiva. Universitas Psychologica, 12(2), 559–570. https://doi.org/10.11144/Javeriana.UPSY12-2.mtpv
  28. Pahlavani, P., Omran, H. A., & Bigdeli, B. (2017). A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation. Earth Observation and Geomatics Engineering, 1(2), 82–99. https://doi.org/10.22059/eoge.2017.220342.1006
  29. Pérez-Vega, A., Mas, J. F., & Ligmann-Zielinska, A. (2012). Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software, 29(1),11-23. https://doi.org/10.1016/j.envsoft.2011.09.011
  30. Piontekowski, V. J., Souza, S., Huamán, E. R., De Souza, W. L., & Campos, F. (2012). Modelagem do desmatamento para o Estado do Acre utilizando o programa DINAMICA EGO. 4o Simpósio de Geotecnologias no Pantanal, Bonito, MS, 20–24. https://www.geopantanal.cnptia.embrapa.br/2012/cd/p183.pdf
  31. Prieto-Amparán, J. A., Villarreal-Guerrero, F., Martínez-Salvador, M., Manjarrez-Domínguez, C., Vázquez-Quintero, G., & Pinedo-Alvarez, A. (2019). Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico. Peer Journal, 7, e6617. https://doi.org/10.7717/peerj.6617
  32. Razavi, B. S. (2014). Predicting the trend of land use changes using artificial neural network and Markov chain model (case study: Kermanshah City). Research Journal of Environmental and Earth Sciences, 6(4), 215–226. https://doi.org/10.19026/rjees.6.5763
  33. Servicio Meteorológico Nacional (SNM). (2024). Monitor de sequía de México. https://smn.conagua.gob.mx/es/climatologia/monitor-de-sequia/monitor-de-sequia-en-mexico
  34. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40) 16083-16088. https://doi.org/10.1073/pnas.1211658109
  35. Silveira, B., Coutinho, G., & Lopes, C. (2002). DINAMICA - a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154(3), 217–235. https://www.researchgate.net/publication/222830178_DINAMICA_-_A_stochastic_cellular_automata_model_designed_to_simulate_the_landscape_dynamics_in_an_Amazonian_colonization_frontier
  36. Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61–78. https://doi.org/10.1007/s40710-015-0062-x
  37. Taleshian, F., Ghajar, M., & Emadi, M. (2018). Impact of land use change on soil erodibility. Global Journal of Environmental Science and Management, 4(1), 59–70. https://doi.org/10.22034/gjesm.2018.04.01.006
  38. Wang, L., Wu, L., & Zhang, W. (2021). Impacts of land use change on landscape patterns in mountain human settlement: the case study of Hantai District (Shaanxi, China). Journal of Mountain Science, 18(3), 749–763. https://doi.org/10.1007/s11629-020-6236-7
  39. Woodward, J., & Foster, I. (1997). Erosion and suspended sediment transfer in river catchments: environmental controls, processes and problems. Geography, 82(4), 353–376. https://doi.org/10.1080/20436564.1997.12452621

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