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Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020)

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Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020)

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dc.contributor.author Cortés-Macías, Lizette Zareh es_ES
dc.contributor.author Rivera-Caicedo, Juan Pablo es_ES
dc.contributor.author Cepeda-Morales, Jushiro es_ES
dc.contributor.author Hernández-Almeida, Óscar Ubisha es_ES
dc.contributor.author García-Morales, Ricardo es_ES
dc.contributor.author Velarde-Alvarado, Pablo es_ES
dc.coverage.spatial east=-104.5695473; north=21.3632852; name=Lago-cráter de Santa María del Oro, Nayarit, México es_ES
dc.date.accessioned 2023-11-06T13:13:27Z
dc.date.available 2023-11-06T13:13:27Z
dc.date.issued 2023-07-28
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/199310
dc.description.abstract [EN] The crater lake of Santa María del Oro in Nayarit, presents Algal Blooms (AB) in a cyclical annual manner, the blooming and subsequent decline of these populations creates color changes in the water, generally in the first half of the year. This work evaluated supervised classification algorithms that allow these changes to be identified using data from the MOD09GQ and MYD09GQ products of MODIS sensor in the period from January 2003 to December 2020. Based on a review of AB recorded in the literature and statistical analysis of dispersion graphs, a database of spectral information and lake color state labels were built to evaluate the different classification algorithms. The best classifier was Random Forest with an accuracy of 87.1%. The temporal analysis and spatial evaluation of the blooms incidence showed that may, april and march are the months with the greatest presence of color changes related to AB in the lake. The spatial analysis found that the highest incidence of blooms occurs in the southeast region of the lake and the largest amounts of events occurred in the years 2011, 2008 and 2012 respectively. The influence of the El Niño-Southern Oscillation (ENSO) phenomenon on the incidence of algal blooms in the crater lake is determined due to the temporal pattern between the anomalies in the AB and the Multivariate ENSO Index, where the greater number of AF events occurred in the cold phases of the ENSO. es_ES
dc.description.abstract [ES] El lago-cráter de Santa María del Oro en el estado de Nayarit, México, presenta Florecimientos Algales (FA) de manera cíclica anual, el florecimiento y posterior decaimiento de estas poblaciones de crea cambios de color en el agua, generalmente en la primera mitad del año. Este trabajo evalúo algoritmos de clasificación supervisada que permitan identificar estos cambios usando datos de los productos MOD09GQ y MYD09GQ del sensor MODIS en el período de enero 2003 a diciembre 2020. A partir de una revisión de FA registrados en la literatura y análisis estadísticos de gráficos de dispersión, se construyó una base de datos de información espectral y etiquetas del estado de color del lago para evaluar los diferentes algoritmos de clasificación. El mejor clasificador fue Random Forest con una precisión de 87.1 %, El análisis temporal y la evaluación espacial de la incidencia de los florecimientos mostraron que mayo, abril y marzo son los meses con mayor presencia de cambios de color en el lago relacionados a FA. En el análisis espacial se encontró que la mayor incidencia de florecimientos se da en la región sureste del lago y las mayores cantidades de eventos ocurrieron en los años 2011, 2008 y 2012 respectivamente. Se determina la influencia del fenómeno El Niño- Oscilación del Sur (ENSO) en la incidencia de florecimientos algales en el lago-cráter debido al patrón temporal entre las anomalías en los FA y el índice multivariado de El Niño-Oscilación del Sur, donde el mayor número de eventos de FA se presentaron en las fases frías del ENSO. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista de Teledetección es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject MODIS es_ES
dc.subject Harmful algal bloom es_ES
dc.subject Turquoise lake es_ES
dc.subject Algoritmos de clasificación de aprendizaje automático es_ES
dc.subject Florecimientos algales nocivos es_ES
dc.subject Lago turquesa es_ES
dc.subject Machine Learning classification algorithms es_ES
dc.title Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020) es_ES
dc.title.alternative Spatio-temporal analysis of algal blooms in tropical crater-lake from MODIS data (2003-2020) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/raet.2023.19673
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Cortés-Macías, LZ.; Rivera-Caicedo, JP.; Cepeda-Morales, J.; Hernández-Almeida, ÓU.; García-Morales, R.; Velarde-Alvarado, P. (2023). Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020). Revista de Teledetección. (62):39-55. https://doi.org/10.4995/raet.2023.19673 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2023.19673 es_ES
dc.description.upvformatpinicio 39 es_ES
dc.description.upvformatpfin 55 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 62 es_ES
dc.identifier.eissn 1988-8740
dc.relation.pasarela OJS\19673 es_ES
dc.description.references Aghababaei, M., Ebrahimi, A., Naghipour, A.A., Asadi, E., Pérez-Suay, A., Morata, M., & Verrelst, J. 2022. Introducing artmo's machine-learning classification algorithms toolbox: Application to plant-type detection in a semi-steppe iranian landscape. Remote Sensing, 14(18), 4452. https://doi.org/10.3390/rs14184452 es_ES
dc.description.references Ananias, P.H.M., Negri, R.G., Dias, M.A., Silva, E.A., & Casaca, W. 2022. A fully unsupervised machine learning framework for algal bloom forecasting in inland waters using modis time series and climatic products. Remote Sensing, 14(17), 4283 https://doi.org/10.3390/rs14174283 es_ES
dc.description.references Armienta, M.A., Vilaclara, G., De la Cruz-Reyna, S., Ramos, S., Ceniceros, N., Cruz, O.,Arcega-Cabrera, F. 2008. Water chemistry of lakes related to active and inactive mexican volcanoes. Journal of Volcanology and Geothermal Research, 178(2), 249-258. https://doi.org/10.1016/j.jvolgeores.2008.06.019 es_ES
dc.description.references Breiman, L. 2001. Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324 es_ES
dc.description.references Caicedo, J.P.R., Verrelst, J., Muñoz-Marí, J., Moreno, J., & Camps-Valls, G. 2014. Towarda semiautomatic machine learning retrieval of biophysical parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1249-1259. https://doi.org/10.1109/JSTARS.2014.2298752 es_ES
dc.description.references Carlson, R.E. 1977. A trophic state index for lakes 1. Limnology and Oceanography, 22(2), 361-369. https://doi.org/10.4319/lo.1977.22.2.0361 es_ES
dc.description.references Carpenter, S.R., Stanley, E.H., & Vander Zanden, M.J. 2011. State of the world's freshwater ecosystems: physical, chemical, and biological changes. Annual review of Environment and Resources, 36, 75-99. https://doi.org/10.1146/annurev-environ-021810-094524 es_ES
dc.description.references Congalton, R.G., & Green, K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC press https://doi.org/10.1201/9780429052729 es_ES
dc.description.references Cortés-Macías, L.Z. 2018. Validación y calibración del algoritmo OC2 para Landsat 8 aplicado al lago cráter de Santa María del Oro, Nayarit. es_ES
dc.description.references Dörnhöfer, K., & Oppelt, N. 2016. Remote sensing for lake research and monitoring-recent advances. Ecological Indicators, 64, 105-122 https://doi.org/10.1016/j.ecolind.2015.12.009 es_ES
dc.description.references Eleveld, M.A., Ruescas, A.B., Hommersom, A., Moore, T.S., Peters, S.W., & Brockmann, C. 2017. An optical classification tool for global lake waters. Remote Sensing, 9(5), 420. https://doi.org/10.3390/rs9050420 es_ES
dc.description.references Fisher, R.A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x es_ES
dc.description.references Flach, P.A., & Lachiche, N. 2004. Naive bayesian classification of structured data. Machine learning, 57(3), 233-269. https://doi.org/10.1023/B:MACH.0000039778.69032.ab es_ES
dc.description.references German, A., Andreo, V., Tauro, C., Scavuzzo, C.M., & Ferral, A. 2020. A novel method based on time series satellite data analysis to detect algal blooms. Ecological Informatics, 59, 101131. https://doi.org/10.1016/j.ecoinf.2020.101131 es_ES
dc.description.references Germán, A., Tauro, C., Andreo, V., Bernasconi, I., & Ferral, A. 2016. Análisis de una serie temporal de clorofila-a a partir de imágenes modis de un embalse eutrófico. En 2016 IEEE Biennial Congress of Argentina (argencon) (pp. 1-6). https://doi.org/10.1109/ARGENCON.2016.7585365 es_ES
dc.description.references Germán, A., Tauro, C., Scavuzzo, M.C., & Ferral, A. 2017. Detection of algal blooms in a eutrophic reservoir based on chlorophyll-a time series data from modis. En 2017 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 4008-4011). https://doi.org/10.1109/IGARSS.2017.8127879 es_ES
dc.description.references Gitelson, A.A., Dall'Olmo, G., Moses, W., Rundquist, D.C., Barrow, T., Fisher, T.R.,... Holz,J. 2008. A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sensing of Environment, 112(9), 3582-3593. https://doi.org/10.1016/j.rse.2008.04.015 es_ES
dc.description.references Hamilton, J.D. 2020. Time series analysis. Princeton university press. https://doi.org/10.2307/j.ctv14jx6sm es_ES
dc.description.references Hovis, W.A., & Leung, K. 1977. Remote sensing of ocean color. Optical Engineering, 16(2),158-166. https://doi.org/10.1117/12.7972093 es_ES
dc.description.references Hsiao, S.I. 1988. Spatial and seasonal variations in primary production of sea ice microalgae and phytoplankton in frobisher bay, arctic canada. Marine Ecology Progress Series, 275-285. https://doi.org/10.3354/meps044275 es_ES
dc.description.references Goodfellow, I., Bengio, Y., & Courville, A. 2016. Deep learning. MIT press. es_ES
dc.description.references Hu, C., Lee, Z., Ma, R., Yu, K., Li, D., & Shang, S. 2010. Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research: Oceans, 115(C4). https://doi.org/10.1029/2009JC005511 es_ES
dc.description.references Huang, C., Li, Y., Yang, H., Sun, D., Yu, Z., Zhang, Z.,... & Xu, L. 2014. Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS. Environmental earth sciences, 71, 3705-3714. https://doi.org/10.1007/s12665-013-2764-6 es_ES
dc.description.references Jia, T., Zhang, X., & Dong, R. 2019. Long-term spatial and temporal monitoring of cianobacteria blooms using modis on google earth engine: A case study in taihu lake. Remote Sensing, 11(19), 2269. https://doi.org/10.3390/rs11192269 es_ES
dc.description.references Klima, E.F., & Roe, R.B. 1972. Report of the national marine fisheries service southeast fisheries center, pascagoula laboratory, fiscal years 1970 and 1971. es_ES
dc.description.references Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., Takahashi, K., 2015. The JRA-55 Reanalysis: general specifications and basic characteristics. J. Meteor. Soc. Jpn., 93, 5-48. https://doi.org/10.2151/jmsj.2015-001 es_ES
dc.description.references Li, J., Gao, M., Feng, L., Zhao, H., Shen, Q., Zhang, F.,... Zhang, B. 2019. Estimation ofchlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based modis land-band algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3769-3783. https://doi.org/10.1109/JSTARS.2019.2936403 es_ES
dc.description.references Lynch, A.J., Cooke, S.J., Deines, A.M., Bower, S.D., Bunnell, D.B., Cowx, I.G.,... others 2016. The social, economic, and environmental importance of inland fish and fisheries. Environmental Reviews, 24(2), 115-121. https://doi.org/10.1139/er-2015-0064 es_ES
dc.description.references Masocha, M., Dube, T., Nhiwatiwa, T., & Choruma, D. 2018. Testing utility of landsat 8 for remote assessment of water quality in two subtropical african reservoirs with contrasting trophic states. Geocarto International, 33(7), 667-680. https://doi.org/10.1080/10106049.2017.1289561 es_ES
dc.description.references Moore, T.S., Dowell, M.D., Bradt, S., & Verdu, A.R. 2014. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote sensing of environment, 143, 97-111. https://doi.org/10.1016/j.rse.2013.11.021 es_ES
dc.description.references Moses, W.J., Sterckx, S., Montes, M.J., De Keukelaere, L., & Knaeps, E. 2017. Atmospheric correction for inland waters. En Bio-optical modeling and remote sensing of inland Waters (pp. 69-100). Elsevier. https://doi.org/10.1016/B978-0-12-804644-9.00003-3 es_ES
dc.description.references Moss, B. 2012. Cogs in the endless machine: lakes, climate change and nutrient cycles: a review. Science of the Total Environment, 434, 130-142. https://doi.org/10.1016/j.scitotenv.2011.07.069 es_ES
dc.description.references Muñoz-Marí, J., & Camps-Valls, G. 2013. Simpleclass: Simple classification toolbox [Manual de software informático]. Descargado de https://github.com/IPLUV/simpleClass (accessed October 21, 2020). es_ES
dc.description.references Oliva-Martínez, M.G., Godínez-Ortega, J.L., & ZuñigaRamos, C.A. 2014. Biodiversidad del fitoplancton de aguas continentales en México. Revista mexicana de biodiversidad, 85, 54-61. https://doi.org/10.7550/rmb.32706 es_ES
dc.description.references Paerl, H.W., & Millie, D.F. 1996. Physiological ecology of toxic aquatic cyanobacteria. Phycologia, 35(sup6), 160-167. https://doi.org/10.2216/i0031-8884-35-6S-160.1 es_ES
dc.description.references Pal, M. 2005. Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1), 217-222. https://doi.org/10.2216/i0031-8884-35-6S-160.1 es_ES
dc.description.references PiSHAROTY, P. 1973. Space technology and oceanography. MBAI Special Publication dedicated to Dr. NK Panikkar (1), 46-51. es_ES
dc.description.references Pizzolon, L. 1996. Importancia de las cianobacterias como factor de toxicidad en las aguas continentales. Interciencia, 21(6), 239-245. es_ES
dc.description.references Raileanu, L.E., & Stoffel, K. 2004. Theoretical comparison between the gini index and information gain criteria. Annals of Mathematics and Artificial Intelligence, 41(1), 77-93. https://doi.org/10.1023/B:AMAI.0000018580.96245.c6 es_ES
dc.description.references Roodschild, M., Gotay Sardiñas, J., Will, A.E., & Rodriguez, S.A. 2019. Optimización de scaled conjugate gradient para froog neural networks. En XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48(SALTA). es_ES
dc.description.references Salazar-Alcaraz, I. 2018. Identificación y aislamiento de cianobacterias de un lago cráter tropical (MATHESIS). Universidad Autónoma de Nayarit. es_ES
dc.description.references Salazar-Alcaraz, I., Ochoa-Zamora, G.G., HernándezAlmeida, O.U., Palomino-Hermosillo, Y.A., LeyvaValencia, I., Romero-Bañuelos, C.A., & CepedaMorales, J. 2021. Polyphasic assessment of thebloom-forming cyanobacterial species Limnoraphis robusta (oscillatoriaceae) and Microcystis aeruginosa (microcystaceae) in a mexican subtropical crater lake. Revista mexicana de biodiversidad, 92. https://doi.org/10.22201/ib.20078706e.2021.92.3485 es_ES
dc.description.references Serrano, D., Filonov, A., & Tereshchenko, I. 2002. Dynamic response to valley breeze circulation in santa maria del oro, a volcanic lake in Mexico. Geophysical Research Letters, 29(13), 1-4. https://doi.org/10.1029/2001GL014142 es_ES
dc.description.references Shaik, A.B., & Srinivasan, S. 2019. A brief survey on random forest ensembles in classification model. En International conference on innovative computing and communications: Proceedings of ICICC 2018, volume 2(pp. 253-260). https://doi.org/10.1007/978-981-13-2354-6_27 es_ES
dc.description.references Shi, K., Li, Y., Li, L., Lu, H., Song, K., Liu, Z.,... Li, Z. 2013. Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. Science of the Total Environment, 444, 1-15. https://doi.org/10.1016/j.scitotenv.2012.11.058 es_ES
dc.description.references Shi, K., Zhang, Y., Xu, H., Zhu, G., Qin, B., Huang, C.,... Lv, H. 2015. Long-term satellite observations of microcystin concentrations in lake taihu during cyanobacterial bloom periods. Environmental Science & Technology, 49(11), 6448-6456. https://doi.org/10.1021/es505901a es_ES
dc.description.references Shi, K., Zhang, Y., Zhang, Y., Li, N., Qin, B., Zhu, G., & Zhou, Y. 2019. Phenology of phytoplankton blooms in a trophic lake observed from long-term modis data. Environmental science & technology, 53(5), 2324-2331. https://doi.org/10.1021/acs.est.8b06887 es_ES
dc.description.references Shi, K., Zhang, Y., Zhang, Y., Qin, B., & Zhu, G. 2020. Understanding the long-term tren of particulate phosphorus in a cyanobacteria-dominated lake using modis-aqua observations. Science of The Total Environment, 737, 139736. https://doi.org/10.1016/j.scitotenv.2020.139736 es_ES
dc.description.references Shi, K., Zhang, Y., Zhou, Y., Liu, X., Zhu, G., Qin, B., & Gao, G. 2017. Long-term modis observations of cyanobacterial dynamics in lake taihu: Responses to nutrient enrichment and meteorological factors. Scientific reports, 7(1), 1-16. https://doi.org/10.1038/srep40326 es_ES
dc.description.references Sosa-Nájera, S., Lozano-García, S., Roy, P.D., & Caballero, M. 2010. Registro de sequías históricas en el occidente de México con base en el análisis elemental de sedimentos lacustres: El caso del lago de Santa María del Oro. Boletín de la Sociedad Geológica Mexicana, 62(3), 437-451. https://doi.org/10.18268/BSGM2010v62n3a8 es_ES
dc.description.references Spyrakos, E., O'donnell, R., Hunter, P.D., Miller, C., Scott, M., Simis, S.G.,... others 2018. Optical types of inland and coastal waters. Limnology and Oceanography, 63(2), 846-870. https://doi.org/10.1002/lno.10674 es_ES
dc.description.references Tett, P. 1987. The ecophysiology of exceptional blooms. Rapport et Proces-verbaux des Reunions. Conseil international pour l'Exploration de la Mer, 187, 47-60. es_ES
dc.description.references Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A.E. 2017. Linear discriminant analysis: A detailed tutorial. AI communications, 30(2), 169-190. https://doi.org/10.3233/AIC-170729 es_ES
dc.description.references The MathWorks, I. 2010. Deep learning toolbox [Manual de software informático]. Natick, Massachusetts, United State. Descargado de https://www.mathworks.com/help/deeplearning/ref/patternnet.html es_ES
dc.description.references Tomaselli, L., & cols. 2004. The microalgal cell. Handbook of microalgal culture: Biotechnology and applied phycology, 1, 3-19 https://doi.org/10.1002/9780470995280.ch1 es_ES
dc.description.references Verhoef, W. 1996. Application of harmonic analysis of ndvi time series (hants). Fourier análisis of temporal NDVI in the Southern African and American continents, 108, 19-24. es_ES
dc.description.references Vermote, Eric, y Wolfe, Robert. 2015. MOD09GQ MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Descargado 2021-07-22, de: https://lpdaac.usgs.gov/products/mod09gqv006/(Type: dataset) doi:10.5067/MODIS/MOD09GQ.006 es_ES
dc.description.references Wang, Q., Ma, Y., Zhao, K., & Tian, Y. 2022. A comprehensive survey of loss functions in machine learning. Annals of Data Science, 9(2), 187-212. https://doi.org/10.1007/s40745-020-00253-5 es_ES
dc.description.references Wang, S., Li, J., Zhang, B., Spyrakos, E., Tyler, A.N., Shen, Q., Zhang, F., Kuster, T., Lehmann, M.K., Wu, Y., Peng, D. 2018. Trophic state assessment of global inland waters using a modis-derived forel-ule index. Remote Sensing of Environment, 217, 444-460. https://doi.org/10.1016/j.rse.2018.08.026 es_ES
dc.description.references Wolter, K., & Timlin, M.S. 1993. Monitoring enso in coads with a seasonally adjusted principal component index. En Proceedings of the 17th Climate Diagnostics Workshop, Norman, OK, NOAA/NMC/CAC, NSSL, Oklahoma Clim. Survey, Cimms And The School Of Meteor., univ. Of Oklahoma (Vol. 52). es_ES
dc.description.references Xiang, S., Nie, F., & Zhang, C. 2008. Learning a mahalanobis distance metric for data clustering and classification. Pattern recognition, 41(12), 3600-3612. https://doi.org/10.1016/j.patcog.2008.05.018 es_ES
dc.description.references Xing, X.-G., Zhao, D.-Z., Liu, Y.-G., Yang, J.-H., Xiu, P., & Wang, L. 2007. An overview of508 remote sensing of chlorophyll fluorescence. Ocean Science Journal, 42, 49-59. https://doi.org/10.1007/BF03020910 es_ES


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