Spatial analysis of changes in land use, vegetation and water bodies in the state of Nayarit, Mexico, 1993-2014

Autores/as

DOI:

https://doi.org/10.15359/rgac.69-2.7

Palabras clave:

Land use change, vegetation and bodies of water, change matrix, spatial analysis

Resumen

To fully understand the dynamics of land use change it is necessary investigate the net change, exchanges and transitions occurring between land use categories. Therefore, this research explores the spatial and temporal trends of changes in land use, vegetation and water bodies in the state of Nayarit from 1993 to 2014. To do so, the INEGI vegetation maps (series II and IV) for both dates were validated with field observation, resampled and overlaid using TerrSet environment in order to calculate the change matrix, and from it estimate the losses, gains, net changes, total changes and exchanges between land use categories. Results indicate that of the 2,783,572.50 hectares of the total area, 58.06% remained without any change and 41.94% experienced some change. Within such area, 15.42% were exchanges between land categories whereas 26.52% were net change. Agriculture is the category that gained most area; occupying 18.17% of the total in 1993 and 22.14% in 2014. Oppositely low forest has decreased from 20.82% to 13.76% during the same period.

Biografía del autor/a

Isaías Moreno-González, Mtro., Universidad Autónoma del Estado de México

Master's in spatial analysis and geoinformatics. Facultad de Geografía, Universidad Autónoma del Estado
de México, Toluca, México. He has collaborated in the area of Cartography and Territory, in the “Subdirección de Geografía y Medio Ambiente, Western State Coordination of the Central South Regional Directorate of the National Institute of Statistics and Geography of Mexico. Correo electrónico: isamoreg@gmail.
com https://orcid.org/0000-0002-6356-4319.

Noel Bonfilio Pineda-Jaimes, Dr., Universidad Autónoma del Estado de México

 Doctor. Facultad de Geografía, Universidad Autónoma del Estado de México, Toluca, México. Currently
works at the Faculty of Geography of the Autonomous University of the State of Mexico. His research and
teaching focuses on the application of Geographic Information Systems in the areas of Land Management,
Multi-criteria Evaluation, Land Cover and Land Use Change Models and Geospatial Analysis. He currently
collaborates in the Academic Area of Geography, Planning and Sustainable Land Management. Correo
electrónico: nbpinedaj@guaemex.mx https://orcid.org/0000-0002-0861-0853.

Luis Ricardo Manzano-Solís, Dr., Universidad Autónoma del Estado de México

Doctor. Facultad de Geografía, Universidad Autónoma del Estado de México, Toluca, México. Currently
works at the Faculty of Geography of the Autonomous University of the State of Mexico. His research interests are focused on the application of Geographic Information Systems in the areas of Water Management
for Integrated Water Resources Management, Multi-criteria Evaluation, Climate Change and Geoinformatics Applications. Correo electrónico: luisrms@gmail.com https://orcid.org/0000-0002-6634-2930.

Xanat Antonio Némiga

Doctora. Facultad de Geografía, Universidad Autónoma del Estado de México, Toluca, México. Currently
works at the Faculty of Geography of the Autonomous University of the State of Mexico. Has a PhD. on
natural resources management held by the UANL in México, and a MsC. On Geoinformation by the ITC in
the Netherlands. She is currenlty a full time profesor at the (UAEMEX). Her research field is the applicaton
of remote sensing and GIS spatial modelling to enrivonmental processes, particularly forest los and forest
fires. Correo electrónico: xantonion@uaemex.mx https://orcid.org/0000-0002-8827-6575.

Referencias

Abel, C., Horion, S., Tagesson, T., Brandt, M., & Fensholt, R. (2019). Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS). Remote Sensing of Environment, 224, 317-332. Obtenido de https://doi.org/10.1016/j.rse.2019.02.010

Abreu, C. G., & Ralha, C. G. (2018). An empirical workflow to integrate uncertainty and sensitivity analysis to evaluate agent-based simulation outputs. Environmental Modelling & Software, 107, 281-297. Obtenido de https://doi.org/10.1016/j.envsoft.2018.06.013

Amadou, M. L., Villamor, G. B., & Kyei-Baffour, N. (2018). Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana. Agricultural Systems, 166, 196-209. Obtenido de https://doi.org/10.1016/j.agsy.2017.10.015

Bera, B., Saha, S., & Bhattacharjee, S. (2020). Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India. Trees, Forests and People, 2(100034), 1-10. Obtenido de https://doi.org/10.1016/j.tfp.2020.100034.

Berlanga-Robles, C. A., & Ruiz-Luna, A. (2007). Analisis de las tendencias de cambio del bosque de mangle del sistema lagunar Teacapan-Agua Brava, Mexico. Una aproximacion con el uso de imagenes de satelite Landsat. Universidad y Ciencia, 23(1), 29+.

Berlanga-Robles, C. A., & Ruiz-Luna, A. (2020). Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management, 28, 229–249. doi: https://doi.org/10.1007/s11273-020-09709-0

Berlanga-Robles, C. A., García-Campos, R. R., López-Blanco, J., & Ruiz-Luna, A. (2009). Patrones de cambio de coberturas y usos del suelo en la región costa norte de Nayarit (1973-2000). Investigaciones Geográficas, Boletín del Instituto de Geografía, UNAM(72), 7-22. doi: http://www.journals.unam.mx/index.php/rig/article/view/19272/18272

Blanco, C. M., Flores, F., & Ortiz, M. A. (2011). Diagnostico funcional de Marismas Nacionales. Universidad Autónoma de Nayarit/Comisión Nacional Forestal, México.

Bonilla-Moheno, M., & Aide, T. M. (2020). Beyond deforestation: Land cover transitions in Mexico. Agricultural Systems, 178(102734). Obtenido de https://doi.org/10.1016/j.agsy.2019.102734

Bosque Sendra, J. (1997). Sistemas de Información Geográfica (2a ed.). Madrid: Rialp.

Calderón-Loor, M., Hadjikakou, M., & Bryan, B. A. (2021). High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015. Remote Sensing of Environment, 252. doi: https://doi.org/10.1016/j.rse.2020.112148

Calzada, L., Meave, J. A., Bonfil, C., & Figueroa, F. (2018). Lands at risk: Land use/land cover change in two contrasting tropical dry regions of Mexico. Applied Geography, 99, 22-30. Obtenido de https://doi.org/10.1016/j.apgeog.2018.07.021

Camacho-Sanabria, R., Camacho-Sanabria, J. M., Balderas-Plata, M. Á., & Sánchez-López, M. (2017). Cambios de cobertura y uso de suelo: estudio de caso en Progreso Hidalgo, Estado de México. Madera y Bosques, 23(3), 39-60. Obtenido de https://doi.org/10.21829/myb.2017.2331516

Cao, Y., Zhang, X., Fu, Y., Lu, Z., & Shen, X. (2020). Urban spatial growth modeling using logistic regression and cellular automata: A case study of Hangzhou. Ecological Indicators, 113(106200), 1-12. Obtenido de https://doi.org/10.1016/j.ecolind.2020.106200

Chandra Paul, G., Saha, S., & Kanti Hembram, T. (2020). Application of phenology-based algorithm and linear regression model for estimating rice cultivated areas and yield using remote sensing data in Bansloi River Basin, Eastern India. Remote Sensing Applications: Society and Environment, 19(100367), 1-12. Obtenido de https://doi.org/10.1016/j.rsase.2020.100367

Darvishi, A., Yousefi, M., & Marull, J. (2020). Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy, 99(105098). Obtenido de https://doi.org/10.1016/j.landusepol.2020.105098

Deng, X., Liu, J., Lin, Y., & Shi, C. (2013). A Framework for the Land Use Change Dynamics Model Compatible with RCMs. Advances in Meteorology, 2013, 1-7. Obtenido de http://dx.doi.org/10.1155/2013/658941

Dhulipala, S., & Patil, G. R. (2020). Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling. Transport Policy, 97, 245-258. Obtenido de https://doi.org/10.1016/j.tranpol.2020.06.012

Dupuy Rada, J. M., González Iturbe, J. A., Iriarte Vivar, S., Calvo Irabien, L., Espadas Manrique, C., Tun Dzul, F., & Dorantes Euán, A. (2007). Cambio de cobertura y uso del suelo (1979-2000) en dos comunidades rurales en el noroeste de Quintana Roo. Investigaciones Geográficas, Boletín del Instituto de Geografía, UNAM(62), 104-124.

Fuenzalida, M., Buzai, G. D., Moreno Jiménez, A., & García de León, A. (2015). Geografía, geotecnología y análisis espacial: tendencias, métodos y aplicaciones. Santiago de Chile: Triángulo.

Geografía, I. N. (2018). Norma técnica del proceso de producción de información estadística y geográfica para el Instituto Nacional de Estadística y Geografía, Comité de aseguramiento de la calidad. doi: https://sc.inegi.org.mx/repositorioNormateca/O_05Sep18.pdf

Hernández-Guzmán, R., Ruiz-Luna, A., & González, C. (2018). Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sensing Applications: Society and Environment, 13, 318-327. Obtenido de https://doi.org/10.1016/j.rsase.2018.12.005

Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., . . . Riitters, K. (2020). Conterminous United States land cover change patterns 2001-2016 from the 2016 National Land Cover Database. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database, 162, 184-199. Obtenido de https://doi.org/10.1016/j.isprsjprs.2020.02.019

Instituto Nacional de Estadística y Geografía. (2014). Encuesta Nacional Agropecuaria. doi: https://www.inegi.org.mx/programas/ena/2014/#Tabulados

Instituto Nacional de Estadística y Geografía. (2017). Marco Geoestadístico, México. Obtenido de https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463142683

Instituto Nacional de Estadística y Geografía. (2018). Norma técnica del proceso de producción de información estadística y geográfica para el Instituto Nacional de Estadística y Geografía, Comité de aseguramiento de la calidad. doi: https://sc.inegi.org.mx/repositorioNormateca/O_05Sep18.pdf

Instituto Nacional de Estadística y Geografía. (2021). Censo de Población y Vivienda 2020. doi: https://www.inegi.org.mx/programas/ccpv/2020/#Tabulados

Kovacs, J. M., Blanco-Correa, M., & Flores-Verdugo, F. (2001). A logistic regression model of hurricane impacts in a mangrove forest of Mexican Pacific. Journal of Coastal Resarch, 17(1), 30-37.

Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205-241. Obtenido de doi: https://doi.org/10.1146/annurev.energy.28.050302.105459

Leh, M. D., Matlock, M. D., Cummings, E. C., & Nalley, L. L. (2013). Quantifying and mapping multiple ecosystem services change in West Africa. Agriculture, Ecosystems & Environment, 165, 6-18. Obtenido de https://doi.org/10.1016/j.agee.2012.12.001

Li, F., Li, Z., Chen, H., Chen, Z., & Li, M. (2020). An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China. Land Use Policy, 95(104620). Obtenido de https://doi.org/10.1016/j.landusepol.2020.104620

Liu, D., Zheng, X., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417(108924). Obtenido de https://doi.org/10.1016/j.ecolmodel.2019.108924

Matsa, M., Mupepi, O., Musasa, T., & Defe, R. (2020). A GIS and remote sensing aided assessment of land use/cover changes in resettlement areas; a case of ward 32 of Mazowe district, Zimbabwe. Journal of Environmental Management, 276(111312). Obtenido de https://doi.org/10.1016/j.jenvman.2020.111312

Mendoza-Ponce, A., Corona-Núñez, R., Kraxner, F., Leduc, S., & Patrizio, P. (2018). Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Global Environmental Change, 53, 12-23. Obtenido de https://doi.org/10.1016/j.gloenvcha.2018.08.004

Meyer, W. B., & Turner, B. L. (1994). Changes in Land Use and Land Cover: A Global Perspective. (Cambridge, Ed.) Cambridge University Press.

Moreno, M. V., & Chivieco, E. (2009). Validación de productos globales de cobertura del suelo en la España Peninsular. Validation of global land cover products for the Spanish Peninsular area. Revista de Teledetección, 31, 5-22. Obtenido de Retrieved from: http://www.aet.org.es/revistas/revista31/Numero31_1.pdf

Müller-Hansen, F., Heitzig, J., Donges, J. F., Cardoso, M. F., Dalla-Nora, E. L., Andrade, P., . . . Thonicke, K. (2019). Can Intensification of Cattle Ranching Reduce Deforestation in the Amazon? Insights From an Agent-based Social-Ecological Model. Ecological Economics, 159, 198-211. Obtenido de https://doi.org/10.1016/j.ecolecon.2018.12.025

Naikoo, M. W., Rihan, M., Ishtiaque, M., & Shahfahad. (2020). Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets. Journal of Urban Management, 9, Issue 3, 347-359. Obtenido de https://doi.org/10.1016/j.jum.2020.05.004

Newbold, T., Hudson, L. N., Hill, S. L., Contu, S., Lysenko, I., Senior, R. A., . . . Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520, 45-50. Obtenido de DOI: 10.1038/nature14324

Páez-Osuna, F. (2001). The environmental impact of shrimp aquaculture: Causes, effects, and mitigating alternatives. Environmental Management, 28(1), 131-140.

Pérez-Hoyos, A., Udías, A., & Rembold, F. (2020). Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa. International Journal of Applied Earth Observation and Geoinformation, 88(102064). Obtenido de https://doi.org/10.1016/j.jag.2020.102064

Pineda Jaimes, N. B. (2010). Descripción, Análisis y Simulación de Procesos Forestales en el Estado de México Mediante Tecnologías de la Información Geográfica (Vols. Tesis de doctorado, Universidad de Alcalá). Madrid, España.

Pineda Jaimes, N. B., & Santana Castañeda, G. (2019). Cambios en la cobertura y uso del suelo en el Estado de México, en el período 2002-2014. Geografía y Sistemas de Información Geográfica (GEOSIG), 11(15), 72-90. Obtenido de http://www.revistageosig.wixsite.com/geosig

Ponce Palafox, J. T. (2015). Manifestación de impacto ambiental modalidad regional, sector acuícola, operación, mantenimiento y abandono del cultivo de camarón en la unidad de manejo acuícola, Pericos-Pimientillo. UAN, Gobierno del Estado de Nayarit, Comité Estatal de Sanidad del Estado de Nayarit A. C., Unión de Acuicultores del estado de Nayarit AC.

Pontius, R. G., Shusas, E., & McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, 101(2-3), 251–268. Obtenido de https://doi.org/10.1016/j.agee.2003.09.008

Prashar, S., Shaw, R., & Takeuchi, Y. (2013). Community action planning in East Delhi: A participatory approach to build urban disaster resilience. Mitigation and Adaptation Strategies for Global Change, 18(4), 429–448. Obtenido de https://doi.org/10.1007/s11027-012-9368-4

Ramı́rez-Garcı́a, P., López-Blanco, J., & Ocaña, D. (1998). Mangrove vegetation assessment in the Santiago River Mouth, Mexico, by means of supervised classification using LandsatTM imagery. Forest Ecology and Management, 105, 217-229. doi: https://doi.org/10.1016/S0378-1127(97)00289-2

Roldán-Aragón, I. E., & Sevilla-Salcedo, Y. (2014). Cambios de uso del suelo y vegetación (1970-2005) en la cuenca del río Eslava, Distrito Federal, México. El Hombre y su Ambiente, 1(5), 1-10.

Saha, S., Saha, M., Mukherjee, K., Arabameri, A., Thao, N., & Paul, G. (2020). Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India. Science of The Total Environment, 730, 1-20. Obtenido de https://doi.org/10.1016/j.scitotenv.2020.139197

Schürmann, A., Kleemann, J., Fürst, C., & Teucher, M. (2020). Assessing the relationship between land tenure issues and land cover changes around the Arabuko Sokoke Forest in Kenya. Land Use Policy, 95(104625). Obtenido de https://doi.org/10.1016/j.landusepol.2020.104625

Velázquez, A., Mas, J. F., Díaz Gallegos, J. R., Mayorga Saucedo, R., Alcántara, P. C., Castro, R., . . . Palacio, J. L. (2002). Patrones y tasas de cambio de uso del suelo en México. Gaceta Ecológica(62), 21-37. doi: http://www.redalyc.org/articulo.oa?id=53906202

Zhang, F., Zhan, J., Zhang, Q., Yao, L., & Liu, W. (2017). Impacts of land use/cover change on terrestrial carbon stocks in Uganda. Physics and Chemistry of the Earth, Parts A/B/C, 101, 195-203. Obtenido de https://doi.org/10.1016/j.pce.2017.03.005

Publicado

2022-04-17

Cómo citar

Moreno-González, I., Pineda-Jaimes, N. B., Manzano-Solís, L. R., & Némiga, X. A. (2022). Spatial analysis of changes in land use, vegetation and water bodies in the state of Nayarit, Mexico, 1993-2014. Revista Geográfica De América Central, 2(69), 199-223. https://doi.org/10.15359/rgac.69-2.7

Número

Sección

Estudios de Caso (Evaluados por pares)

Cómo citar

Moreno-González, I., Pineda-Jaimes, N. B., Manzano-Solís, L. R., & Némiga, X. A. (2022). Spatial analysis of changes in land use, vegetation and water bodies in the state of Nayarit, Mexico, 1993-2014. Revista Geográfica De América Central, 2(69), 199-223. https://doi.org/10.15359/rgac.69-2.7

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