Trend Analysis of Total Suspended Solids in Inland Waters Using the BFAST Algorithm on MOD09GA Products in Porto Primavera Reservoir – Brazil

Vista sencilla de ítem
dc.creatorMoncayo Eraso, Ricardo Javier
dc.creatorEraso-Checa, Francisco
dc.date2024-08-30
dc.date.accessioned2025-10-01T23:53:12Z
dc.descriptionSatellite remote sensing, particularly using the MODIS orbital platform, is crucial for large-scale lake monitoring, allowing the observation of optically active components with suitable spatial and temporal resolution for lakes with surfaces greater than 40 hectares. In this context, the objective of this article was to propose a methodology that improves the retrieval and monitoring of data related to Total Suspended Solids (TSS) in large lakes. The methodology employed involved defining a baseline to map the spatiotemporal dynamics of TSS using MODIS band 1, which generates information in the infrared spectrum and is centered at 645 nm. The method was tested in the Porto Primavera Reservoir (PPR), Brazil. To validate the model, two fieldwork campaigns were conducted in the PPR, where radiometric and water quality data were collected. An empirical model was fitted between reflectance and the TSS data set (r = 0.93, R2 = 0.85, p < 0.01, n = 25). This empirical model was applied to a time series of MODIS images from May 2000 to April 2020. Using the spatial distribution maps, a time series was created from an average pixel of the sampling stations, and then this time series was analyzed to separate the trend and seasonality. The results showed that the average TSS values observed in the time series were 5.79 mg/L. The seasonality of the time series revealed that the highest concentration is recorded in the austral summer (December-February), the rainiest season. The trend component indicates that variations in TSS concentration coincide with exceptional events of increased precipitation and with a homogenization interval of the waters following the reservoir's construction.en-US
dc.descriptionLa teledetección satelital es crucial para monitorear lagos a gran escala, permitiendo la observación de componentes ópticamente activos con una resolución espacial y temporal adecuada para lagos con superficies mayores a 40 hectáreas. El objetivo de este artículo fue proponer una metodología que mejore la recuperación y el monitoreo de los datos correspondientes a los Sólidos en Suspensión Totales (SST) en lagos de grandes dimensiones. La metodología empleada consistió en definir una línea base en la que se puede mapear la dinámica espacio temporal de los SST a partir de la banda 1 de MODIS que genera información en el espectro infrarrojo y está centrada en los 645 nm. El método fue probado en el Embalse de Porto Primavera (EPP), Brasil. Para comprobar la validez del modelo se realizaron dos trabajos de campo en el EPP, en los que se recolectaron datos radiométricos y de calidad del agua. Un modelo empírico fue ajustado entre la reflectancia y el conjunto de datos de SST (r = 0.93, R2 = 0.85, p < 0.01, n = 25). Este modelo empírico fue aplicado a una serie temporal de imágenes MODIS desde mayo de 2000 hasta abril de 2020. Usando los mapas de distribución especial, se creó una serie del tiempo a partir de un píxel promedio de las estaciones de muestreo, esto se analizó para separar la tendencia y la estacionalidad. Los resultados mostraron que los valores promedio de SST observados en la serie temporal son de 5.79 mg/L. La estacionalidad de las series temporales reveló que la mayor concentración se registra en el verano austral (diciembre-febrero), la estación más lluviosa. La componente de tendencia indica que las variaciones en la concentración de SST coinciden con eventos excepcionales de aumento de precipitaciones y con un intervalo de homogeneización de las aguas posterior a la construcción del embalse.es-ES
dc.formatapplication/pdf
dc.formattext/xml
dc.formatapplication/zip
dc.formattext/html
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2985
dc.identifier10.22430/22565337.2985
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7898
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2985/3328
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2985/3422
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2985/3522
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2985/3523
dc.relation/*ref*/M. J. Butt, and M. Nazeer, “Landsat ETM+ Secchi Disc Transparency (SDT) retrievals for Rawal Lake, Pakistan,” ScienceDirect, vol. 56, no. 7, pp. 1428-1440, Oct. 2015. https://doi.org/10.1016/j.asr.2015.06.041
dc.relation/*ref*/Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sensing of Environment, vol. 192, pp. 98-113, Apr. 2017. https://doi.org/10.1016/j.rse.2017.02.007
dc.relation/*ref*/M. H. Gholizadeh, A. M. Melesse, and L. Reddi, “Comprehensive review on water quality parameters. Estimation using Remote Sensing Techniques,” Sensors, vol. 16, no. 8, p. 1298, Aug. 2016. https://doi.org/10.3390/s16081298
dc.relation/*ref*/C. Giardino, M. Pepe, P. A. Brivio, P. Ghezzi, and E. Zilioli, “Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery,” The Science of the Total Environment, vol. 268, no. 1-3, pp. 19-29, Mar. 2001. https://doi.org/10.1016/S0048-9697(00)00692-6
dc.relation/*ref*/X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sensing of Environment, vol. 190, pp. 107-121, Mar. 2017. https://doi.org/10.1016/j.rse.2016.12.006
dc.relation/*ref*/E. M. Ruzycki, R. P. Axler, G. E. Host, J. R. Henneck, and N. R. Will, “Estimating sediment and nutrient loads in four western Lake Superior streams,” Journal of the American Water Resources Association, vol. 50, no. 5, pp. 1138-1154, Oct. 2014. https://doi.org/10.1111/jawr.12175
dc.relation/*ref*/J. Hui, L. Yao, and Z. Wen-bin, “Retrieval and Analysis of Total Suspended Solid Concentration by MODIS Terra 500m Imagery during Flood Period in Poyang Lake, China,” in 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, Changsha, China, 2011, pp. 985-988. https://doi.org/10.1109/CDCIEM.2011.480
dc.relation/*ref*/M. Wang, S. Son, Y. Zhang, and W. Shi, “Remote sensing of water optical property for China’s Inland Lake Taihu using the SWIR Atmospheric correction with 1640 and 2130nm Bands,” IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 6, no. 6, pp. 2505-2516, Dec. 2013. https://doi.org/10.1109/JSTARS.2013.2243820
dc.relation/*ref*/Z. Wang, K. Kawamura, Y. Sakuno, X. Fan, Z. Gong, and J. Lim, “Retrieval of chlorophyll-a and total suspended solids using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression based on field hyperspectral measurements in irrigation ponds in Higashihiroshima, Japan,” Remote Sensing, vol. 9, no. 3, p. 264, Mar. 2017. https://doi.org/10.3390/rs9030264
dc.relation/*ref*/G. S. Bilotta et al., “Developing environment-specific water quality guidelines for suspended particulate matter,” Water Res., vol. 46, no. 7, pp. 2324–2332, May. 2012. https://doi.org/10.1016/j.watres.2012.01.055
dc.relation/*ref*/G. Bilotta, and R. Brazier, “Understanding the influence of suspended solids on water quality and aquatic biota,” Water Research, vol. 42, no. 12, pp. 2849-2861, Jun. 2008. https://doi.org/10.1016/j.watres.2008.03.018
dc.relation/*ref*/F. L. Hellweger, P. Schlosser, U. Lall, and J. K. Weissel, “Use of satellite imagery for water quality studies in New York Harbor,” Estuar. Coast. Shelf Sci., vol. 61, no. 3, pp. 437–448, 2004. https://doi.org/10.1016/j.ecss.2004.06.019
dc.relation/*ref*/Y. Liu, A. Islam, and J. Gao, “Quantification of shallow water quality parameters by means of remote sensing,” Progress in Physical Geography, vol. 27, no. 1, pp. 24-43, Mar. 2003. https://doi.org/10.1191/0309133303pp357ra
dc.relation/*ref*/N. L. Lailia, F. Arafah, A. Jaelani, and A. D. Pamungkas, “Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-a concentration using Landsat-8 imagery at Poteran island water,” Remote Sensing and Spatial Information Sciences, vol. II, no. 2, Mar. 2015. http://eprints.itn.ac.id/2852/1/isprsannals-II-2-W2-55-2015.pdf
dc.relation/*ref*/Y. Zhang, R. Ma, H. Duan, S. Loiselle, M. Zhang, and J. Xu, “A novel MODIS algorithm to estimate chlorophyll a concentration in eutrophic turbid lakes,” Ecological Indicators, vol. 69, pp. 138-151, Oct. 2016. https://doi.org/10.1016/j.ecolind.2016.04.020
dc.relation/*ref*/C. Östlund, P. Flink, N. Strömbeck, D. Pierson, and T Lindell, “Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper,” The Science of the Total Environment, vol. 268, no. 1-3, pp. 139-154, Mar. 2001. https://doi.org/10.1016/S0048-9697(00)00683-5
dc.relation/*ref*/P Dorji, P. Fearns, and M. Broomhall, “A semi-analytic model for estimating Total Suspended Sediment concentration in turbid coastal waters of Northern Western Australia Using MODIS-Aqua 250m Data,” Remote Sensing, vol. 8, no. 7, p. 556, Jun. 2016. https://doi.org/10.3390/rs8070556
dc.relation/*ref*/D. Doxaran, J. M. Froidefond, P. Castaing, and M. Babin, “Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data,” Estuarine, Coastal and Shelf Science, vol. 81, no.3, pp. 321-332, Feb. 2009. https://doi.org/10.1016/j.ecss.2008.11.013
dc.relation/*ref*/A. R. M. Amin, K. Abdullah, H. S. Lim, M. F. Embong, F. Ahmad, and R. Yaacob, “Development of regional TSS algorithm over Penang using Modis Terra (250 M) surface reflectance product,” Ekológia (Bratislava), vol. 35, no. 3, pp. 289–294, Sep. 2016. https://doi.org/10.1515/eko-2016-0023
dc.relation/*ref*/C. Petus, G. Chust, F. Gohin, D. Doxaran, J. M. Froidefond, and Y. Sagarminaga, “Estimating turbidity and total suspended matter in the adour river plume (South Bay of Biscay) using MODIS 250 m imagery,” Continental Shelf Research, vol. 30, no, 5, pp. 379-392, Mar. 2010. https://doi.org/10.1016/j.csr.2009.12.007
dc.relation/*ref*/E. Kaba, W. Philpot, and T. Steenhuis, T, “Evaluating suitability of MODIS-Terra images for reproducing historic sediment concentrations in water bodies: Lake Tana, Ethiopia,” International Journal of Applied Earth Observation and Geoinformation, vol. 26, pp. 286-297, Feb. 2014. https://doi.org/10.1016/j.jag.2013.08.001
dc.relation/*ref*/N. Bi, Z. Yang, H. Wang, D. Fan, X. Sun, and K. Lei, “Seasonal variation of suspended-sediment transport through the southern Bohai Strait,” Estuarine Coastal and Shelf Science, vol. 93, no. 3, pp. 239-247, Jul. 2011. https://doi.org/10.1016/j.ecss.2011.03.007
dc.relation/*ref*/Z. Chen, C. Hu, and F. Muller-Karger, “Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery,” Remote Sensing of Environment, vol. 109, no. 2, pp. 207-220, Jul. 2007. https://doi.org/10.1016/j.rse.2006.12.019
dc.relation/*ref*/J. E. Min, J. H. Ryu, S. Lee, and S. H. Son, “Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea,” Marine Pollution Bulletin, vol. 64, no. 2, pp. 382-390, Feb. 2012. https://doi.org/10.1016/j.marpolbul.2011.10.025
dc.relation/*ref*/M. Hasan, and L. Benninger, “Resiliency of the western Chesapeake Bay to total suspended solid concentrations following storms and accounting for land-cover,” Estuarine, Coastal and Shelf Science, vol. 191, pp. 136-149, May. 2017. https://doi.org/10.1016/j.ecss.2017.04.002
dc.relation/*ref*/E. K. Ayana, A. W. Worqlul, and T. S. Steenhuis, “Evaluation of stream water quality data generated from MODIS images in modeling total suspended solid emission to a freshwater lake,” Science of the total Environment, vol. 523, pp. 170-177, Aug. 2015. https://doi.org/10.1016/j.scitotenv.2015.03.132
dc.relation/*ref*/E. M. L. de Moraes Novo, C. C. Faria Barbosa, J. M. Melack, R. Moraes de Freitas, F. Titonelli, and Y. Shimabukuro, “Comparing MODIS and etm+ image data for inland water studies: spatial resolution constraints,” Revista Brasileira de Cartografia, vol. 58, no. 2 pp. 109-118, Aug. 2006. https://scholar.archive.org/work/caab3zgplndzzlqy7py6osltju/access/wayback/http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/download/44916/23926/
dc.relation/*ref*/J. Knight, and M. L. Voth, “Application of MODIS imagery for intra-annual water clarity assessment of Minnesota Lakes,” Journal of remote sensing of environment, vol. 4, no. 7, pp. 2181-2198, Jul. 2012. https://doi.org/10.3390/rs4072181
dc.relation/*ref*/I. M. McCullough, C. S. Loftin, and S. A. Sader, “High-frequency remote monitoring of large lakes with MODIS 500m imagery,” Remote Sens. Environ., vol. 124, pp. 234–241, Sep. 2012. https://doi.org/10.1016/j.rse.2012.05.018
dc.relation/*ref*/R. Moncayo, “Mapeo de la dinámica regional de la transparencia en aguas continentales usando productos de reflectancia MOD09GA,” Entramado, vol. 13, no. 2, pp. 270-276, Jun. 2017. https://doi.org/10.18041/entramado.2017v13n2.26233
dc.relation/*ref*/E. E. Souza Filho, “The Porto Primavera dam and the fluvial transport on the Porto são José section, Parana river” Mercator, vol. 15, no. 4, pp. 65–81, 2016. https://doi.org/10.4215/RM2016.1504.0005
dc.relation/*ref*/J. Dias, “A construção da paisagem na raia divisória são paulo-paraná-mato grosso do sul: um estudo por teledetecção” (Tese de Doutorado), Departamento de Geografía, Universidade Estadual Paulista – UNESP - Presidente Prudente, Brasil, 2003. https://repositorio.unesp.br/server/api/core/bitstreams/05b51ab9-eb4c-48c1-80c7-f3feb70b1740/content
dc.relation/*ref*/Z. Cao et al., “MODIS observations reveal decrease in lake suspended particulate matter across China over the past two decades,” Remote Sens. Environ., vol. 295, no. 113724, p. 113724, Sep. 2023. https://doi.org/10.1016/j.rse.2023.113724
dc.relation/*ref*/E. Ghaderpour, and T. Vujadinovic, “Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis,” Remote Sensing, vol. 12, no. 23, p. 4001, Dec. 2020. https://doi.org/10.3390/rs12234001
dc.relation/*ref*/D. Masiliūnas, T. Nandin-Erdene, M. Herold, and J. Verbesselt, “BFAST Lite: A lightweight break detection method for time series analysis,” Remote Sens. (Basel), vol. 13, no. 16, p. 3308, Aug. 2021. https://doi.org/10.3390/rs13163308
dc.relation/*ref*/C. A. Tassinari, S. H. Bonilla, F. Agostinho, C. M. V. B. Almeida, and B. F. Giannetti, “Evaluation of two hydropower plants in Brazil: using emergy for exploring regional possibilities,” J. Clean. Prod., vol. 122, pp. 78–86, 2016. https://doi.org/10.1016/j.jclepro.2016.01.077
dc.relation/*ref*/L. Sabo Boschi, M. L. B. T. Galo, L. H. S. Rotta, and F. S. Y. Watanabe, Mapeamento do biovolume de plantas aquáticas submersas a partir de dados hidroacústicos e imagem multiespectral de alta resolução,” Planta Daninha, vol. 30, no. 3, pp. 525–539, Sep. 2012. https://www.scielo.br/j/pd/a/3gX3NnzbVjw3N7LZMCRvpvG/#
dc.relation/*ref*/R. L. Miller, B. A. Mckee, “Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters,” Remote Sensing of Environment, vol. 93, no. 1-3, pp. 259-266, Oct. 2004. https://doi.org/10.1016/j.rse.2004.07.012
dc.relation/*ref*/R. G. Wetzel, and G. E. Likens, Limnological analyses, New York, USA: Springer, 1991.
dc.relation/*ref*/E. F. Vermote, N. Z. El Saleous, and C. O. Justice, “Atmospheric correction of MODIS data in the visible to middle infrared: first results,” Remote Sensing of Environment, vol. 83, no. 1-2, pp. 97-111, Nov. 2002. https://doi.org/10.1016/S0034-4257(02)00089-5
dc.relation/*ref*/Y. Zhang et al., “Temporal and spatial variability of chlorophyll a concentration in Lake Taihu using MODIS time-series data,” Hydrobiologia, vol. 661, no. 1, pp. 235–250, Feb. 2011. https://doi.org/10.1007/s10750-010-0528-9
dc.relation/*ref*/L. Zhu, S. Wang, Y. Zhou and F. Yan, “Estimation of Suspended Sediment Concentration Changes in Taihu Lake Based on Multi-temporal MODIS Image Data,” in 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA, 2006, pp. 3023-3026. https://doi.org/10.1109/IGARSS.2006.776
dc.relation/*ref*/B. F. Rudorff, “aplicações de MODIS em estudos epidemiológicos” in Sensor Modis e Suas Aplicações Ambientas no Brasil, Editora Parêntese, 2007, pp. 207-209. https://books.google.com/books?id=4MxI4hZQeOEC&printsec=frontcover&dq=inauthor:%22BERNARDO+F.+T.+RUDORFF+Rudorff%22&hl=es&newbks=1&newbks_redir=1&sa=X&ved=2ahUKEwiiyKSKipiIAxWNSjABHUtVDY8Q6AF6BAgHEAI
dc.relation/*ref*/D. C. Hatchell, Analytical Spectral Devices, Technical Guide, New York – USA, (1999). Accessed: Jan. 5, 2023. [Online]. Available: https://www.gep.uchile.cl/Biblioteca/radiometr%C3%ADa%20de%20campo/TechGuide.pdf
dc.relation/*ref*/C. D. Mobley, “Estimation of the remote-sensing reflectance from above-surface measurements,” Appl. Opt., vol. 38, no. 36, p. 7442, Dec. 1999. https://doi.org/10.1364/ao.38.007442
dc.relation/*ref*/S. Chen, L. Han, X. Chen, D. Li, and Y. Li, “Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: An improved method.,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 99, pp. 58-69, Jan. 2015. https://doi.org/10.1016/j.isprsjprs.2014.10.006
dc.relation/*ref*/G. Dall'olmo, A. A. Gitelson, D. C. Rundsquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of Sea WIFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red near-infrared bands,” Remote Sensing of Environment, vol. 96, no. 2, pp. 176-187, May. 2005. https://doi.org/10.1016/j.rse.2005.02.007
dc.relation/*ref*/MODIS ProtoFlight Model (PFM) Relative Spectral Response (RSR), Science Team - MODIS web – NASA, 1999, Available at: ftp://mcst.ssaihq.com/pub/permanent/MCST/PFM_L1B_LUT_4-30-99/L1B_RSR_LUT/
dc.relation/*ref*/M. Matthews, “Current review of empirical procedures of remote sensing in inland and near-coastal transitional waters,” International Journal of Remote Sensing, vol. 32, pp. 6855-6899, Aug. 2011. http://dx.doi.org/10.1080/01431161.2010.512947
dc.relation/*ref*/J. J. Wang, and X. X. Lu, “Estimation of suspended sediment concentrations using Terra MODIS: an example from the Lower Yangtze River, China,” Science of The Total Environment, vol. 408, no. 5, pp. 1131–1138, Feb. 2010. https://doi.org/10.1016/j.scitotenv.2009.11.057
dc.relation/*ref*/K. Shi et al., “Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250m MODIS-Aqua data,” Remote Sens. Environ., vol. 164, pp. 43–56, Jul. 2015. https://doi.org/10.1016/j.rse.2015.02.029
dc.relation/*ref*/A. G. Dekker, R. J. Vos, and S. W. M. Peters, “Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data,” Int. J. Remote Sens., vol. 23, no. 1, pp. 15–35, 2002. https://doi.org/10.1080/01431160010006917
dc.relation/*ref*/E. Park, and E. M. Latrubesse, “Modeling suspended sediment distribution patterns of the Amazon River using MODIS data,” Remote Sens. Environ., vol. 147, pp. 232–242, May. 2014. https://doi.org/10.1016/j.rse.2014.03.013
dc.relation/*ref*/V. Rodríguez-Guzmán, and F. Gilbes-Santaella, “Using MODIS 250 m imagery to estimate total suspended sediment in a tropical open bay,” Int. J. Syst. Appl. Eng. Dev, vol. 3, no, 1, pp. 36–44, 2009. https://www.uprm.edu/gerslab/wp-content/uploads/sites/214/2023/07/rodriguez_gilbes_09b.pdf
dc.relation/*ref*/B. Demir, F. Bolovo, and L. Bruzzone, “Classification of time series of multispectral images with limited training data,” IEEE Transactions on Image Processing, vol. 22, no. 8, pp. 3219-3233, Aug. 2013. https://doi.org/10.1109/TIP.2013.2259838
dc.relation/*ref*/J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, “Detecting trend and seasonal changes in satellite image time series,” Remote Sensing of Environment, vol. 114, no. 1, pp. 106-115, Jan. 2010. https://doi.org/10.1016/j.rse.2009.08.014
dc.relation/*ref*/M. Lu, E. Pebesma, A. Sánchez, and J. Verbesselt, “Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 117, pp. 227-236, 2016. https://doi.org/10.1016/j.isprsjprs.2016.03.007
dc.relation/*ref*/J. Lambert, A. Jacquin, J. Denux, and V. Chéret, “Comparison of two remote sensing time series analysis methods for monitoring forest decline,” Multi temp. 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-temp), Trento, Italy, 2009, pp. 93-96. https://doi.org/10.1016/j.isprsjprs.2016.03.007
dc.relation/*ref*/P. A. Permatasari, A. Fatikhunnada, Liyantono, Y. Setiawan, Syartinilia, and A. Nurdiana, “Analysis of agricultural land use changes in jombang regency, east java, Indonesia using BFAST method,” Procedia Environ. Sci., vol. 33, pp. 27–35, 2016. https://doi.org/10.1016/j.proenv.2016.03.053
dc.relation/*ref*/L. Feng, C. Hu, X. Han, X. Chen, and L. Qi, “Long-Term Distribution Patterns of Chlorophyll-a Concentration in China’s Largest Freshwater Lake: MERIS Full-Resolution Observations with a Practical Approach,” Remote sensing, vol. 7, no. 1, pp. 275-299, Dec. 2015. https://doi.org/10.3390/rs70100275
dc.relation/*ref*/A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll- a concentration in case 2 waters,” Environ. Res. Lett., vol. 4, no. 4, p. 045003, 2009. https://doi.org/10.1088/1748-9326/4/4/045003
dc.relation/*ref*/C. Le et al., “Towards a long-term chlorophyll-a data record in a turbid estuary using MODIS observations,” Prog. Oceanogr., vol. 109, pp. 90–103, Feb. 2013. https://doi.org/10.1016/j.pocean.2012.10.002
dc.relation/*ref*/Y. Zhang, S. Lin, J. Liu, X. Qian, and Y. Ge, “Time-series MODIS image-based retrieval and distribution analysis of total suspended matter concentrations in Lake Taihu (China),” Int. J. Environ. Res. Public Health, vol. 7, no. 9, pp. 3545–3560, 2010. https://doi.org/10.3390/ijerph7093545
dc.relation/*ref*/J. Zhao et al., “Remote sensing evaluation of total suspended solids dynamic with Markov model: A case study of inland reservoir across administrative boundary in South China,” Sensors (Basel), vol. 20, no. 23, p. 6911, Dec. 2020. https://doi.org/10.3390/s20236911
dc.relation/*ref*/Y. Yang, and Y. Wang, “Using the BFAST Algorithm and Multitemporal AIRS Data to Investigate Variation of Atmospheric Methane Concentration over Zoige Wetland of China,” Remote Sensing, vol. 12, no. 19, pp. 1-17, 2020. https://doi.org/10.3390/rs12193199
dc.relation/*ref*/J. C. Stevaux, D. P. Martins, and M. Meurer, “Changes in a large regulated tropical river: The Paraná River downstream from the Porto Primavera Dam, Brazil,” Geomorphology (Amst.), vol. 113, no. 3–4, pp. 230–238, Dec. 2009. https://doi.org/10.1016/j.geomorph.2009.03.015
dc.relation/*ref*/E. Ciancia et al., “Modeling and multi-temporal characterization of total suspended matter by the combined use of sentinel 2-MSI and Landsat 8-OLI data: The Pertusillo Lake case study (Italy),” Remote Sens. (Basel), vol. 12, no. 13, p. 2147, Jul. 2020. https://doi.org/10.3390/rs12132147
dc.relation/*ref*/G. B. Chelotti, J. M. Martinez, H. L. Roig, and D. Olivietti, “Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing,” RBRH, vol. 24, p. e17, 2019. https://www.scielo.br/j/rbrh/a/rcbdD6j8VZVD5pVBqRtDZgR/?lang=en
dc.relation/*ref*/Z. Tan, Z. Cao, M. Shen, J. Chen, Q. Song, and H. Duan, “Remote estimation of water clarity and suspended particulate matter in Qinghai Lake from 2001 to 2020 using MODIS images,” Remote Sens. (Basel), vol. 14, no. 13, p. 3094, 2022. https://doi.org/10.3390/rs14133094
dc.relation/*ref*/
dc.rightsDerechos de autor 2024 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 27 No. 60 (2024); e2985en-US
dc.sourceTecnoLógicas; Vol. 27 Núm. 60 (2024); e2985es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectalgoritmo BFASTes-ES
dc.subjectreflectancia de superficiees-ES
dc.subjectseries temporaleses-ES
dc.subjectteledetecciónes-ES
dc.subjectvariable ópticamente activaes-ES
dc.subjectBFAST algorithmen-US
dc.subjectsurface reflectanceen-US
dc.subjecttemporal seriesen-US
dc.subjectremote sensingen-US
dc.subjectoptical active componentsen-US
dc.titleTrend Analysis of Total Suspended Solids in Inland Waters Using the BFAST Algorithm on MOD09GA Products in Porto Primavera Reservoir – Brazilen-US
dc.titleAnálisis de tendencia del total de sólidos en suspensión en aguas interiores aplicando el algoritmo BFAST a productos MOD09GA en el embalse de Porto Primavera-Brasiles-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

Archivos

Bloque original

Mostrando 1 - 4 de 4
Miniatura
Nombre:
2985-MPU-VF-v3.pdf
Tamaño:
969.91 KB
Formato:
Adobe Portable Document Format
Descargar
Miniatura
Nombre:
2256-5337-teclo-27-60-e201.xml
Tamaño:
150.68 KB
Formato:
Extensible Markup Language
Descargar
Miniatura
Nombre:
344277854013.epub
Tamaño:
1.06 MB
Formato:
Electronic publishing
Descargar
Miniatura
Nombre:
3523.html
Tamaño:
139.22 KB
Formato:
Hypertext Markup Language
Descargar

Colecciones

Revista TecnoLógicas - Cosecha Portal de Revistas