Dynamics of environmental flooding in wetlands of the Lower Grijalva River Basin: spatiotemporal approach through Landsat images
DOI:
https://doi.org/10.4995/raet.2024.21222Keywords:
MNDWI, MBWI, flood plain, probabilityAbstract
The diversity of existing methodologies to define and analyze the dynamics of water surfaces demonstrates the difficulty in investigating their behavior. This is compounded by variables that complicate their delineation, such as precipitation, evapotranspiration, and their reflective behavior. This study aimed to analyze the spatiotemporal dynamics of wetlands with high socio-environmental impact in the Lower Grijalva River Basin for the period from 1986 to 2018. For the analysis, a satellite database was integrated with 169 images from Landsat 5 and Landsat 8. Spectral indices (MNDWI and MBWI) were calculated, and thresholds characterizing water surfaces in the study area were identified. The results showed that the MBWI was superior in estimating water surfaces. Finally, maps of the spatiotemporal dynamics’ probabilities were generated for the wetlands of the greatest ecological and economic importance in the Lower Grijalva River Basin. These maps revealed the return periods of the expansion and longitudinal retreat processes in the wetlands and indicated that during La Niña periods, the formation of temporary wetlands could be associated with groundwater saturation rather than surface water contributions.
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Acharya, T.D., Subedi, A., Huang, H., Lee, D.H. 2019. Application of Water Indices in Surface Water Change Detection Using Landsat Imagery in Nepal. Sensors and Materials, 31(5), 1429. https://doi.org/10.18494/SAM.2019.2264
Andrade-Velázquez, M., Medrano-Pérez, O.R. 2020. Precipitation pattern in Usumacinta and Grijalva basins (southern Mexico) under a changing climate. Revista Bio Ciencias, 7, e905. https://doi.org/10.15741/revbio.07.e905
Asif, Z., Chen, Z., Sadiq, R., Zhu. 2023. Climate Change Impacts on Water Resources and Sustainable Water Management Strategies in North America. Water Resources Management, 37(6), 2771-2786. https://doi.org/10.1007/s11269-023-03474-4
Aroma, R.J., Raimond, K., Estrela, V.V., de Jesus, M.A. 2024. A coastal band spectral combination for water body extraction using Landsat 8 images. International Journal of Environmental Science and Technology, 21(2), 1767-1784. https://doi.org/10.1007/s13762-023-05027-z
Becerril-Piña, R., Díaz-Delgado, C., Mastachi-Loza, C.A., González-Sosa, E. 2016. Integration of remote sensing techniques for monitoring desertification in Mexico. Human and Ecological Risk Assessment: An International Journal, 22(6), 1323-1340. https://doi.org/10.1080/10807039.2016.1169914
Chander, B.L., Markham, D.L., Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors-. Remote Sensing of Environment, 113(5), 893-903. https://doi.org/10.1016/j.rse.2009.01.007
Comisión Nacional de Áreas Naturales Protegidas. 2018. 100 Años de Conservación en México (SEMARNATCONANP): Ciudad de México. http://www.gob.mx/conanp/documentos/libro-100-anos-de-conservacion
Comisión Nacional de Áreas Naturales Protegidas. 2022. Listado de las Áreas Naturales Protegidas. http://sig.conanp.gob.mx/website/pagsig/listanp/
Cruz, Z.G.C., Pérez, O.R.M. 2023. Análisis geomorfológico de las subcuencas Usumacinta y Grijalva en el sureste de México. Acta Universitaria, 33, 1-20. https://doi.org/10.15174/au.2023.3684
Del Aguila, S., Mejía, A. 2021. Caracterización morfométrica de dos cuencas altoandinas del Perú utilizando sistemas de información geográfica. Tecnología y ciencias del agua, 12(2), 538-562. https://doi.org/10.24850/j-tyca-2021-02-12
DOF. 2018. Diario Oficial de la Federación. Acuerdo por lo que se dan a conocer los resultados del estudio técnico de las aguas superficiales en las cuencas hidrológicas. SEGOB: Ciudad de México. http://dof.gob.mx/nota_detalle.php?codigo=5518766&fecha=10/04/2018
Eastman, R., McCoy, I.L., Schulz, H., Wood, R. 2023. A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies. EGUsphere, 2023, 1-33. https://doi.org/10.5194/egusphere-2023-2118
Elekwachi, W., Muktar, A., Hemba, S., Odinaka, A.W. 2021. Spatial Temporal Dynamics of Urban Wetlands Around Obio/Akpor and Its Environs: Implications for Sustainable Development Goals, 5(5), 10.
Feyisa, G.L., Meilby, H., Fensholt, R., Proud, S.R. 2014. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23-35. https://doi.org/10.1016/j.rse.2013.08.029
Gu, X., Ye, L., Xin, Q., Zhang, C., Zeng, F., Nerantzaki, S.D., Papalexiou, S.M. 2022. Extreme Precipitation in China: A Review on Statistical Methods and Applications. Advances in Water Resources, 163, 104144. https://doi.org/10.1016/j.advwatres.2022.104144
Instituto Nacional de Estadística, Geografía e Informática. 2001. Diccionario de datos de hidrología superficial. Sistema Nacional de Información Geográfica. INEGI: Aguas Calientes, México. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/historicos/2104/702825224042/702825224042_1.pdf
Liu, Z., Xu, J., Liu, M., Yin, Z., Liu, X., Yin, L., Zheng, W. 2023. Remote sensing and geostatistics in urban water-resource monitoring: A review. Marine and Freshwater Research, 74(10), 747-765. https://doi.org/10.1071/MF22167
Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., Ip, A. 2016. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341-352. https://doi.org/10.1016/j.rse.2015.11.003
Muñoz-Salinas, E., Castillo, M. 2015. Streamflow and sediment load assessment from 1950 to 2006 in the Usumacinta and Grijalva Rivers (Southern Mexico) and the influence of ENSO. CATENA, 127, 270-278. https://doi.org/10.1016/j.catena.2015.01.007
Musalem-Castillejos, K., Laino-Guanes, R., BelloMendoza, R., González-Espinoza, M., RamírezMarcial, N. 2018. Water quality of the Grijalva river in the Chiapas and Tabasco border. Ecosistemas y Recursos Agropecuarios, 5(13), 55. https://doi.org/10.19136/era.a5n13.1334
Nandi, D., Chowdhury, R., Mohapatra, J., Mohanta, K., Ray, D. 2018. Automatic delineation of water bodies using multiple spectral indices. International Journal of Scientific Research in Science, Engineering and Technology, 4(4), 498-512.
Plascencia-Vargas, H., González-Espinosa, M., RamírezMarcial, R., Álvarez-Solís, D., Musálem-Castillejos, K. 2014. Características físico-bióticas de la cuenca del río Grijalva. In M. González-Espinosa., BrunelManse. M.C. (Eds.). Montañas, pueblos y agua: dimensiones y realidades de la cuenca Grijalva (pp. 1-25). Ciudad de México: Editorial Juan Pablos.
Qi, Y., Dou, H., Wang, Z. 2022. An Adaptive Threshold Selected Method from Remote Sensing Image based on Water Index. Journal of Physics: ConferenceSeries, 2228(1), 012001. https://doi.org/10.1088/1742-6596/2228/1/012001
Sajjad, A., Lu, J., Chen, X., Chisenga, C., Mazhar, N. 2023. Rapid assessment of riverine flood inundation in Chenab floodplain using remote sensing techniques. Geoenvironmental Disasters, 10(1), 9. https://doi.org/10.1186/s40677-023-00236-7
Saravanan, S., Abijith, D. 2022. Flood susceptibility mapping of Northeast coastal districts of Tamil Nadu India using Multi-source Geospatial data and Machine Learning techniques. Geocarto International, 37(27), 15252-15281. https://doi.org/10.1080/10106049.2022.2096702
Thornton, M.M., Shrestha, R., Wei, Y., Thornton, P.E., Kao, S.-C., Wilson, B.E. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/1840
Valdés-Manzanilla, A. 2016. Historical floods in Tabasco and Chiapas during sixteenth-twentieth centuries. Natural Hazards, 80(3), 1563-1577. https://doi.org/10.1007/s11069-015-2039-5
Wang, G., Meng, D., Chen, R., Yang, G., Wang, L., Jin, H., Ge, X., Feng, H. 2024. Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images. Remote Sensing, 16(2). https://doi.org/10.3390/rs16020277
Wang, X., Xie, S., Zhang, X., Chen, C., Guo, H., Du, J., Duan, Z. 2018. A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 68, 73-91. https://doi.org/10.1016/j.jag.2018.01.018
Xue, F., Gao, W., Yin, C., Chen, X., Xia, Z., Lv, Y., Zhou, Y., Wang, M. 2022. Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4170-4179. https://doi.org/10.1109/JSTARS.2022.3176388
Yanagi, M. 2024. Climate change impacts on wheat production: Reviewing challenges and adaptation strategies. Advances in Resources Research, 4(1), 89-107.
Yudha, I.S. 2023. Detection of Changes in Water Surface Area in Limboto Lake Using Landsat Data From 1990 to 2020. IOP Conference Series: Earth and Environmental Science, 1127(1), 012021. https://doi.org/10.1088/1755-1315/1127/1/012021
Zamora-Saud, N., Pérez-Sánchez, E., Carballo Cruz, V.R., Galindo Alcántara, A. 2019. Dinámica de las terrazas fluviales en la subcuenca Grijalva-Villahermosa, México. Boletín de la Sociedad Geológica Mexicana, 71(3), 805-817. https://doi.org/10.18268/BSGM2019v71n3a10
Zavala-Cruz, J., Jiménez-Ramírez, R., Palma-López, D.J., Bautista-Zúñiga, F., Gavi-Reyes, F. 2016. Paisajes geomorfológicos: Base para el levantamiento de suelos en Tabasco, México. Ecosistemas y recursos agropecuarios, 3(8), 161-171.
Zhang, F., Li, J., Zhang, B., Shen, Q., Ye, H., Wang, S., Lu, Z. 2018. A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images. International Journal of Remote Sensing, 39(11), 3429-3451. https://doi.org/10.1080/01431161.2018.1444292
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Copyright (c) 2024 Tania G. Núñez-Magaña, Adalberto Galindo-Alcántara, Carlos A. Mastachi-Loza, Rocío Becerril-Piña, Miguel A. Palomeque de la Cruz, Silvia del C. Ruiz-Acosta
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