Geoestadística en datos circulares

ilustraciones, diagramas

Autores:: Niño Chaparro, Alejandro

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Geoestadística en datos circulares
dc.title.translated.eng.fl_str_mv	Geostatistics in circular data
title	Geoestadística en datos circulares
spellingShingle	Geoestadística en datos circulares 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Datos circulares Geoestadística no estacionaria Pulimento de medianas Redes neuronales Kriging circular Circular kriging Nonstationary geostatistics Neural networks Median polish Directional data geoestadística geoprocesamiento red neuronal artificial geostatistics geoprocessing artificial neural network
title_short	Geoestadística en datos circulares
title_full	Geoestadística en datos circulares
title_fullStr	Geoestadística en datos circulares
title_full_unstemmed	Geoestadística en datos circulares
title_sort	Geoestadística en datos circulares
dc.creator.fl_str_mv	Niño Chaparro, Alejandro
dc.contributor.advisor.spa.fl_str_mv	Giraldo Henao, Ramón
dc.contributor.author.spa.fl_str_mv	Niño Chaparro, Alejandro
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Datos circulares Geoestadística no estacionaria Pulimento de medianas Redes neuronales Kriging circular Circular kriging Nonstationary geostatistics Neural networks Median polish Directional data geoestadística geoprocesamiento red neuronal artificial geostatistics geoprocessing artificial neural network
dc.subject.proposal.spa.fl_str_mv	Datos circulares Geoestadística no estacionaria Pulimento de medianas Redes neuronales
dc.subject.proposal.eng.fl_str_mv	Kriging circular Circular kriging Nonstationary geostatistics Neural networks Median polish Directional data
dc.subject.wikidata.spa.fl_str_mv	geoestadística geoprocesamiento red neuronal artificial
dc.subject.wikidata.eng.fl_str_mv	geostatistics geoprocessing artificial neural network
description	ilustraciones, diagramas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023
dc.date.accessioned.none.fl_str_mv	2024-05-27T22:58:41Z
dc.date.available.none.fl_str_mv	2024-05-27T22:58:41Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86169 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Albawi, S.; Mohammed, T.; Al-Zawi, S: Understanding of a convolutional neural network. En: 2017 international conference on engineering and technology (ICET) Ieee, 2017, p. 1–6. Arroyo, L: Estudio de la variabilidad espacio-temporal de la precipitación, el viento y la humedad en la región del Urabá antioqueño a través de resultados de WRF. (2023). Atkinson, P.; Lloyd, C: geoENV VII–geostatistics for environmental applications. Vol. 16. Springer Science & Business Media, 2010. Breckling, J: The analysis of directional time series applications to wind speed and direction. Springer, 1989. Breckling, J: The analysis of directional time series: applications to wind speed and direction. Vol. 61. Springer Science & Business Media, 2012. Carrat, F.; Valleron, A: Epidemiologic mapping using the “kriging” method: application to an influenza-like epidemic in France. En: American journal of epidemiology 135 (1992), Nr. 11, p. 1293–1300. Carrera, J.; Alcolea, A.; Medina, A.; Hidalgo, J.; Slooten, L: Inverse problem in hydrogeology. En: Hydrogeology Journal 13 (2005), p. 206–222. Coble, K.; Mishra, A.; Ferrell, S.; Griffin, T: Big data in agriculture: A challenge for the future. En: Applied Economic Perspectives and Policy 40 (2018), Nr. 1, p. 79–96. Cressie, N: Geostatistical analysis of spatial data. En: Spatial statistics and digital image analysis 1991 (1991), p. 87–108. Cressie, N: Statistics for spatial data. John Wiley & Sons, 2015. Cuador-Gil, J.; Quintero-Silverio, A: Simulación condicional de variables regionalizadas y su aplicación al comportamiento de la porosidad efectiva en un yacimiento fracturado-poroso. En: Boletín de la Sociedad Geológica Mexicana 54 (2001), Nr. 1, p. 19–27. Demyanov, V.; Kanevsky, M.; Chernov, S.; Savelieva, E.; Timonin, V: Neural network residual kriging application for climatic data. En: Journal of Geographic Information and Decision Analysis 2 (1998), Nr. 2, p. 215–232. Dowd, P.; Sarac, C: A neural network approach to geostatistical simulation. En: Mathematical Geology 26 (1994), p. 491–503. Emery, X.; Séguret, S: Geostatistics for the Mining Industry: Applications to Porphyry Copper Deposits. CRC Press, 2020. Eslava, J: Climatología y diversidad climática de Colombia. En: Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales 18 (1993), Nr. 71, p. 507–538. Fernholz, L: Von Mises calculus for statistical functionals. Vol. 19. Springer Science & Business Media, 2012. Gill, J.; Hangartner, D: Circular data in political science and how to handle it. En: Political Analysis 18 (2010), Nr. 3, p. 316–336. Grancher, D.; Bar-Hen, A.; Paris, R.; Lavigne, F.; Brunstein, D: Spatial interpolation of circular data: application to tsunami of December 2004. En: Advances and Applications in Statistics 30 (2012), Nr. 1, p. 19–29. Gribov, A.; Krivoruchko, K: Empirical Bayesian kriging implementation and usage. En: Science of the Total Environment 722 (2020), p. 137290. Handcock, M.; Wallis, J: An approach to statistical spatial-temporal modeling of meteorological fields. En: Journal of the American Statistical Association 89 (1994), Nr. 426, p. 368–378. Jona, L.; Santoro, M.; Mastrantonio, G: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data. En: Journal of Statistical Computation and Simulation 90 (2020), Nr. 7, p. 1315–1345. Kanevski, M.; Maignan, M: Analysis and modelling of spatial environmental data. Vol. 6501. EPFL press, 2004. Kanevski, M.; Timonin, V.; Pozdnukhov, A: Machine learning for spatial environmental data: theory, applications, and software. EPFL press, 2009. Kovacs-Győri, A.; Ristea, A.; Havas, C.; Mehaffy, M.; Hochmair, H.; Resch, B.; Juhasz, L.; Lehner, A.; Ramasubramanian, L.; Blaschke, T: Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning. En: ISPRS International Journal of Geo-Information 9 (2020), Nr. 12, p. 752. Lamamra, A.; Neguritsa, D.; Mazari, M: Geostatistical modeling by the Ordinary Kriging in the estimation of mineral resources on the Kieselguhr mine, Algeria. En: IOP Conference Series: Earth and Environmental Science Vol. 362 IOP Publishing, 2019, p. 012051. Langevin, P: Magnétisme et théorie des électrons. En: Ann. chim. et phys. (1905), Nr. 8, p. 203. Lantuéjoul, C: Geostatistical simulation: models and algorithms. Springer Science & Business Media, 2001 (1139). Mardia, K.; Jupp, P: Directional statistics. Wiley, 2000. Martínez, F: Modelización de la función de covarianza en procesos espacio-temporales: análisis y aplicaciones. Universitat de Valencia (Spain), 2008. Martínez, W.; Melo, C.; Melo, O: Median polish kriging for space–time analysis of precipitation. En: Spatial statistics 19 (2017), p. 1–20. Matías, J.; Vaamonde, A.; Taboada, J.; González-Manteiga, W: Comparison of kriging and neural networks with application to the exploitation of a slate mine. En: Mathematical geology 36 (2004), p. 463–486. McNeill, L: Interpolation and smoothing of mapped circular data. En: South African Statistical Journal 27 (1993), Nr. 1, p. 23–49. Morphet, W: Simulation, kriging, and visualization of circular-spatial data. Utah State University, 2009. Oliver, A.; Webster, R: A tutorial guide to geostatistics: Computing and modelling variograms and kriging. En: Catena 113 (2014), p. 56–69. Oliver, M.; Webster, R: Basic steps in geostatistics: The Variogram and Kriging. Springer, 2015. Padarian, J.; Pérez-Quezada, J.; Seguel, O: Modelling the distribution of organic carbon in the soils of Chile. En: Proceeding of the fifth global workshop on digital soil mapping, Digital Soil assessments and beyond, Sydney, 2012, p. 329–333. Pewsey, A.; Neuhäuser, M.; Ruxton, G: Circular statistics in R. OUP Oxford, 2013. Playfair, W: Playfair’s commercial and political atlas and statistical breviary. Cambridge University Press, 2005. Rao, T: Spatial statistics and spatio-temporal data. En: Journal of Time Series Analysis 34 (2013), Nr. 2, p. 280–280. Rodriguez-Rubio, E.; Stuardo, J: Variability of photosynthetic pigments in the Colombian Pacific Ocean and its relationship with the wind field using ADEOS-I data. En: Journal of Earth System Science 111 (2002), p. 227–236. Rueda, J.; Rodríguez, E.; Ortiz, J: Caracterización espacio temporal del campo de vientos superficiales del Pacífico colombiano y el Golfo de Panamá a partir de sensores remotos y datos in situ. (2007). Sareen, K.; Panigrahi, B.; Shikhola, T.; Sharma, R: An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction. En: Energy 278 (2023), p. 127799. Seber, G.; Wild, J: Nonlinear Regression. 2003. Seo, Y.; Kim, S.; Singh, V: Estimating spatial precipitation using regression kriging and artificial neural network residual kriging (RKNNRK) hybrid approach. En: Water Resources Management 29 (2015), p. 2189–2204. Sparks, A: nasapower: a NASA POWER global meteorology, surface solar energy, and climatology data client for R. En: Journal of Open Source Software 3 (2018), Nr. 30, p. 1035. Stein, M: Interpolation of Spatial Data: Some theory for Kriging. Springer, 2013. Surjotedjo, H.; Widyaningsih, Y.; Nurrohmah, S: Median polish kriging model for circular-spatial data. En: Empowering Science and Mathematics for Global Competitiveness. CRC Press, 2019, p. 377–385. Wackernagel, H: Multivariate geostatistics: an introduction with applications. Springer Science & Business Media, 2003. Wang, L.; Wong, P.; Shibli, S: Modelling porosity distribution in the A’nan Oilfield: Use of geological quantification, neural networks and geostatistics. En: SPE International Oil and Gas Conference and Exhibition in China SPE, 1998, p. SPE–48884. Webster, R.; Oliver, M: Geostatistics for environmental scientists. John Wiley & Sons, 2007. Weisstein, E: Bessel function of the first kind. En: https://mathworld.wolfram.com/ (2002). Xiao, L.; Zhang, Y: Zhang neural network versus gradient neural network for solving time-varying linear inequalities. En: IEEE transactions on neural networks 22 (2011), Nr. 10, p. 1676–1684. Yan, Q.; Wan, Z.; Yang, C: Flight Load Calculation Using Neural Network Residual Kriging. En: Aerospace 10 (2023), Nr. 7, p. 599. Zakeri, F.; Mariethoz, G: A review of geostatistical simulation models applied to satellite remote sensing: Methods and applications. En: Remote Sensing of Environment 259 (2021), p. 112381.
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dc.rights.license.spa.fl_str_mv	Atribución-SinDerivadas 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	ix, 58 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Henao, Ramón69cdaf318604c2b37e003603d498f429Niño Chaparro, Alejandro243a8557ddbe3ca6d94be796a5549ec52024-05-27T22:58:41Z2024-05-27T22:58:41Z2023https://repositorio.unal.edu.co/handle/unal/86169Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasSe propone una nueva metodología en el contexto de geostadística no estacionaria que permite hacer predicción de datos circulares empleando kriging circular residual cuando la tendencia espacial es modelada a través de redes neuronales. Usando datos simulados y reales (tomados del proyecto NASA power) se hace comparación de la técnica propuesta con pulimento de medianas. Los resultados indican que la estrategia considerada mejora las predicciones. (Texto tomado de la fuente).We propose a new methodology in the context of nonstationary geostatistics that allows the prediction of circular data using residual circular kriging when the spatial trend is modeled through neural networks. Using simulated and real data (taken from the NASA power project), the proposed technique is compared with those obtained through median polish. The results indicate that the strategy proposed improves the predictionsMaestríaMagíster en Ciencias - Estadísticaix, 58 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasDatos circularesGeoestadística no estacionariaPulimento de medianasRedes neuronalesKriging circularCircular krigingNonstationary geostatisticsNeural networksMedian polishDirectional datageoestadísticageoprocesamientored neuronal artificialgeostatisticsgeoprocessingartificial neural networkGeoestadística en datos circularesGeostatistics in circular dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlbawi, S.; Mohammed, T.; Al-Zawi, S: Understanding of a convolutional neural network. En: 2017 international conference on engineering and technology (ICET) Ieee, 2017, p. 1–6.Arroyo, L: Estudio de la variabilidad espacio-temporal de la precipitación, el viento y la humedad en la región del Urabá antioqueño a través de resultados de WRF. (2023).Atkinson, P.; Lloyd, C: geoENV VII–geostatistics for environmental applications. Vol. 16. Springer Science & Business Media, 2010.Breckling, J: The analysis of directional time series applications to wind speed and direction. Springer, 1989.Breckling, J: The analysis of directional time series: applications to wind speed and direction. Vol. 61. Springer Science & Business Media, 2012.Carrat, F.; Valleron, A: Epidemiologic mapping using the “kriging” method: application to an influenza-like epidemic in France. En: American journal of epidemiology 135 (1992), Nr. 11, p. 1293–1300.Carrera, J.; Alcolea, A.; Medina, A.; Hidalgo, J.; Slooten, L: Inverse problem in hydrogeology. En: Hydrogeology Journal 13 (2005), p. 206–222.Coble, K.; Mishra, A.; Ferrell, S.; Griffin, T: Big data in agriculture: A challenge for the future. En: Applied Economic Perspectives and Policy 40 (2018), Nr. 1, p. 79–96.Cressie, N: Geostatistical analysis of spatial data. En: Spatial statistics and digital image analysis 1991 (1991), p. 87–108.Cressie, N: Statistics for spatial data. John Wiley & Sons, 2015.Cuador-Gil, J.; Quintero-Silverio, A: Simulación condicional de variables regionalizadas y su aplicación al comportamiento de la porosidad efectiva en un yacimiento fracturado-poroso. En: Boletín de la Sociedad Geológica Mexicana 54 (2001), Nr. 1, p. 19–27.Demyanov, V.; Kanevsky, M.; Chernov, S.; Savelieva, E.; Timonin, V: Neural network residual kriging application for climatic data. En: Journal of Geographic Information and Decision Analysis 2 (1998), Nr. 2, p. 215–232.Dowd, P.; Sarac, C: A neural network approach to geostatistical simulation. En: Mathematical Geology 26 (1994), p. 491–503.Emery, X.; Séguret, S: Geostatistics for the Mining Industry: Applications to Porphyry Copper Deposits. CRC Press, 2020.Eslava, J: Climatología y diversidad climática de Colombia. En: Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales 18 (1993), Nr. 71, p. 507–538.Fernholz, L: Von Mises calculus for statistical functionals. Vol. 19. Springer Science & Business Media, 2012.Gill, J.; Hangartner, D: Circular data in political science and how to handle it. En: Political Analysis 18 (2010), Nr. 3, p. 316–336.Grancher, D.; Bar-Hen, A.; Paris, R.; Lavigne, F.; Brunstein, D: Spatial interpolation of circular data: application to tsunami of December 2004. En: Advances and Applications in Statistics 30 (2012), Nr. 1, p. 19–29.Gribov, A.; Krivoruchko, K: Empirical Bayesian kriging implementation and usage. En: Science of the Total Environment 722 (2020), p. 137290.Handcock, M.; Wallis, J: An approach to statistical spatial-temporal modeling of meteorological fields. En: Journal of the American Statistical Association 89 (1994), Nr. 426, p. 368–378.Jona, L.; Santoro, M.; Mastrantonio, G: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data. En: Journal of Statistical Computation and Simulation 90 (2020), Nr. 7, p. 1315–1345.Kanevski, M.; Maignan, M: Analysis and modelling of spatial environmental data. Vol. 6501. EPFL press, 2004.Kanevski, M.; Timonin, V.; Pozdnukhov, A: Machine learning for spatial environmental data: theory, applications, and software. EPFL press, 2009.Kovacs-Győri, A.; Ristea, A.; Havas, C.; Mehaffy, M.; Hochmair, H.; Resch, B.; Juhasz, L.; Lehner, A.; Ramasubramanian, L.; Blaschke, T: Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning. En: ISPRS International Journal of Geo-Information 9 (2020), Nr. 12, p. 752.Lamamra, A.; Neguritsa, D.; Mazari, M: Geostatistical modeling by the Ordinary Kriging in the estimation of mineral resources on the Kieselguhr mine, Algeria. En: IOP Conference Series: Earth and Environmental Science Vol. 362 IOP Publishing, 2019, p. 012051.Langevin, P: Magnétisme et théorie des électrons. En: Ann. chim. et phys. (1905), Nr. 8, p. 203.Lantuéjoul, C: Geostatistical simulation: models and algorithms. Springer Science & Business Media, 2001 (1139).Mardia, K.; Jupp, P: Directional statistics. Wiley, 2000.Martínez, F: Modelización de la función de covarianza en procesos espacio-temporales: análisis y aplicaciones. Universitat de Valencia (Spain), 2008.Martínez, W.; Melo, C.; Melo, O: Median polish kriging for space–time analysis of precipitation. En: Spatial statistics 19 (2017), p. 1–20.Matías, J.; Vaamonde, A.; Taboada, J.; González-Manteiga, W: Comparison of kriging and neural networks with application to the exploitation of a slate mine. En: Mathematical geology 36 (2004), p. 463–486.McNeill, L: Interpolation and smoothing of mapped circular data. En: South African Statistical Journal 27 (1993), Nr. 1, p. 23–49.Morphet, W: Simulation, kriging, and visualization of circular-spatial data. Utah State University, 2009.Oliver, A.; Webster, R: A tutorial guide to geostatistics: Computing and modelling variograms and kriging. En: Catena 113 (2014), p. 56–69.Oliver, M.; Webster, R: Basic steps in geostatistics: The Variogram and Kriging. Springer, 2015.Padarian, J.; Pérez-Quezada, J.; Seguel, O: Modelling the distribution of organic carbon in the soils of Chile. En: Proceeding of the fifth global workshop on digital soil mapping, Digital Soil assessments and beyond, Sydney, 2012, p. 329–333.Pewsey, A.; Neuhäuser, M.; Ruxton, G: Circular statistics in R. OUP Oxford, 2013.Playfair, W: Playfair’s commercial and political atlas and statistical breviary. Cambridge University Press, 2005.Rao, T: Spatial statistics and spatio-temporal data. En: Journal of Time Series Analysis 34 (2013), Nr. 2, p. 280–280.Rodriguez-Rubio, E.; Stuardo, J: Variability of photosynthetic pigments in the Colombian Pacific Ocean and its relationship with the wind field using ADEOS-I data. En: Journal of Earth System Science 111 (2002), p. 227–236.Rueda, J.; Rodríguez, E.; Ortiz, J: Caracterización espacio temporal del campo de vientos superficiales del Pacífico colombiano y el Golfo de Panamá a partir de sensores remotos y datos in situ. (2007).Sareen, K.; Panigrahi, B.; Shikhola, T.; Sharma, R: An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction. En: Energy 278 (2023), p. 127799.Seber, G.; Wild, J: Nonlinear Regression. 2003.Seo, Y.; Kim, S.; Singh, V: Estimating spatial precipitation using regression kriging and artificial neural network residual kriging (RKNNRK) hybrid approach. En: Water Resources Management 29 (2015), p. 2189–2204.Sparks, A: nasapower: a NASA POWER global meteorology, surface solar energy, and climatology data client for R. En: Journal of Open Source Software 3 (2018), Nr. 30, p. 1035.Stein, M: Interpolation of Spatial Data: Some theory for Kriging. Springer, 2013.Surjotedjo, H.; Widyaningsih, Y.; Nurrohmah, S: Median polish kriging model for circular-spatial data. En: Empowering Science and Mathematics for Global Competitiveness. CRC Press, 2019, p. 377–385.Wackernagel, H: Multivariate geostatistics: an introduction with applications. Springer Science & Business Media, 2003.Wang, L.; Wong, P.; Shibli, S: Modelling porosity distribution in the A’nan Oilfield: Use of geological quantification, neural networks and geostatistics. En: SPE International Oil and Gas Conference and Exhibition in China SPE, 1998, p. SPE–48884.Webster, R.; Oliver, M: Geostatistics for environmental scientists. John Wiley & Sons, 2007.Weisstein, E: Bessel function of the first kind. En: https://mathworld.wolfram.com/ (2002).Xiao, L.; Zhang, Y: Zhang neural network versus gradient neural network for solving time-varying linear inequalities. En: IEEE transactions on neural networks 22 (2011), Nr. 10, p. 1676–1684.Yan, Q.; Wan, Z.; Yang, C: Flight Load Calculation Using Neural Network Residual Kriging. En: Aerospace 10 (2023), Nr. 7, p. 599.Zakeri, F.; Mariethoz, G: A review of geostatistical simulation models applied to satellite remote sensing: Methods and applications. 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