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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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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

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Título: Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020)
Otro titulo: Spatio-temporal analysis of algal blooms in tropical crater-lake from MODIS data (2003-2020)
Autor: Cortés-Macías, Lizette Zareh Rivera-Caicedo, Juan Pablo Cepeda-Morales, Jushiro Hernández-Almeida, Óscar Ubisha García-Morales, Ricardo Velarde-Alvarado, Pablo
Fecha difusión:
Resumen:
[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 ...[+]


[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 ...[+]
Palabras clave: MODIS , Harmful algal bloom , Turquoise lake , Algoritmos de clasificación de aprendizaje automático , Florecimientos algales nocivos , Lago turquesa , Machine Learning classification algorithms
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2023.19673
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/raet.2023.19673
Tipo: Artículo

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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

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

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 [+]
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

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

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

Breiman, L. 2001. Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

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

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

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

Congalton, R.G., & Green, K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC press https://doi.org/10.1201/9780429052729

Cortés-Macías, L.Z. 2018. Validación y calibración del algoritmo OC2 para Landsat 8 aplicado al lago cráter de Santa María del Oro, Nayarit.

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

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

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

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

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

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

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

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

Hamilton, J.D. 2020. Time series analysis. Princeton university press. https://doi.org/10.2307/j.ctv14jx6sm

Hovis, W.A., & Leung, K. 1977. Remote sensing of ocean color. Optical Engineering, 16(2),158-166. https://doi.org/10.1117/12.7972093

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

Goodfellow, I., Bengio, Y., & Courville, A. 2016. Deep learning. MIT press.

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

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

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

Klima, E.F., & Roe, R.B. 1972. Report of the national marine fisheries service southeast fisheries center, pascagoula laboratory, fiscal years 1970 and 1971.

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

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

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

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

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

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

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

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).

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

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

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

PiSHAROTY, P. 1973. Space technology and oceanography. MBAI Special Publication dedicated to Dr. NK Panikkar (1), 46-51.

Pizzolon, L. 1996. Importancia de las cianobacterias como factor de toxicidad en las aguas continentales. Interciencia, 21(6), 239-245.

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

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).

Salazar-Alcaraz, I. 2018. Identificación y aislamiento de cianobacterias de un lago cráter tropical (MATHESIS). Universidad Autónoma de Nayarit.

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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.

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

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

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

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).

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

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

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