Métodos de geoestadística e interpolación espacial aplicados a datos climáticos

La predicción de una propiedad sobre la base de un conjunto de mediciones puntuales en una región es necesaria si se va a elaborar un mapa de la propiedad para la región. De las técnicas de predicción e interpolación espacial, Kriging es óptimo entre todos los procedimientos lineales, ya que es impa...

Full description

Autores:: Montegranario Riascos, Hebert

Tipo de recurso:

Fecha de publicación:: 2001

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:

id	UNAB2_abbfa561c70801723c4bd20a0ffe05e1
oai_identifier_str	oai:repository.unab.edu.co:20.500.12749/25468
network_acronym_str	UNAB2
network_name_str	Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
dc.title.translated.spa.fl_str_mv	Geostatistics and spatial interpolation methods applied to climate data
title	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
spellingShingle	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos Computer sciences Systems engineer Weather forecast Math Geology Statistical methods Geophysical predictions Meteorology Interpolation spaces Ciencias computacionales Ingeniería de sistemas Predicciones geofísicas Meteorología Espacios de interpolación Pronóstico del tiempo Matemáticas Geología Métodos estadísticos
title_short	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
title_full	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
title_fullStr	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
title_full_unstemmed	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
title_sort	Métodos de geoestadística e interpolación espacial aplicados a datos climáticos
dc.creator.fl_str_mv	Montegranario Riascos, Hebert
dc.contributor.advisor.none.fl_str_mv	Leclerc, Gregoire
dc.contributor.author.none.fl_str_mv	Montegranario Riascos, Hebert
dc.contributor.cvlac.spa.fl_str_mv	Montegranario Riascos, Hebert [0000381918]
dc.subject.keywords.spa.fl_str_mv	Computer sciences Systems engineer Weather forecast Math Geology Statistical methods Geophysical predictions Meteorology Interpolation spaces
topic	Computer sciences Systems engineer Weather forecast Math Geology Statistical methods Geophysical predictions Meteorology Interpolation spaces Ciencias computacionales Ingeniería de sistemas Predicciones geofísicas Meteorología Espacios de interpolación Pronóstico del tiempo Matemáticas Geología Métodos estadísticos
dc.subject.lemb.spa.fl_str_mv	Ciencias computacionales Ingeniería de sistemas Predicciones geofísicas Meteorología Espacios de interpolación
dc.subject.proposal.spa.fl_str_mv	Pronóstico del tiempo Matemáticas Geología Métodos estadísticos
description	La predicción de una propiedad sobre la base de un conjunto de mediciones puntuales en una región es necesaria si se va a elaborar un mapa de la propiedad para la región. De las técnicas de predicción e interpolación espacial, Kriging es óptimo entre todos los procedimientos lineales, ya que es imparcial y tiene una variación mínima del error de predicción. En Cokriging, que tiene esta misma propiedad atractiva, se utilizan observaciones adicionales de una o más covariables, lo que puede contribuir a una mayor precisión de las predicciones. Un procedimiento más reciente son los Splines de píate fino (TPS), de los cuales el famoso spline cúbico para una variable es un caso particular. Este método no requiere análisis de correlación espacial y es más fácil de implementar en computadora. En este estudio intentamos mejorar la comprensión de estos tres métodos, proporcionando los fundamentos matemáticos y comparando su desempeño utilizando valores medios mensuales de variables clonadas como temperatura y precipitación para Colombia; Implementando los principales algoritmos con Delphi Pascal Lenguaje.
publishDate	2001
dc.date.issued.none.fl_str_mv	2001
dc.date.accessioned.none.fl_str_mv	2024-07-12T19:41:56Z
dc.date.available.none.fl_str_mv	2024-07-12T19:41:56Z
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv	Tesis
dc.type.hasversion.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/25468
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/25468
identifier_str_mv	reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co
dc.relation.references.spa.fl_str_mv	Burges T.M. , and Webster R. 1980a. "Optimal interpolation and Isarithmic Mapping of Soil Properties". Journal of Soil Science 31, 315- 331. Burges T.M. , and Webster R. 1980b. "Optimal interpolation and Isaritlnnic Mapping of Soil Properties". II Block Kriging. Journal of Soil Science 31, 333-341. Burges T.M. , and Webster R. 1980c. "Optimal interpolation and Isarithmic Mapping of Soil Properties". III Changing Drift and Universal Kriging. Journal of Soil Science 31, 505-524. Cárter J. and Roberts, S. A., 1996, An Investigation into the Use of Median Indicator Kriging to Assist in Post Accident Radiation Assessment, In Proceedings of the Seventh Symposium of Spatial Data Handling , Delft, Netherlands, August 1996, Vol. 2, pp. 9B27-9B40. Taylor and Francis. Cárter J., McLaren F. and Higgins N. A., 1997, An Investigation into the Applicability of Gcostatistical Techniques for Estimating Contamination Levels Following an Accidental Release of Radioactivity. Journal of Radiation Protection (in press). Chui, C. (1988), Multivariate Splines, Society for Industrial and Applied Mathematics, Philadelphia.PA. Clark I., 1979, Practical Geostatistics. Applied Science Publishers Ltd., London. Cressie N. A. and Hawkins D. M., 1980, Robust Estimation of the Variogram. I. Mathematical Geology, Vol. 12, pp. 115-125. Cressie N. A. and Ver Hoef J. M., 1993, Spatial Statistical Analysis of Environmental and Ecological Data. pp. 404-413, in Environmental Modeling with GIS, Goodchild M. F., Parks B. O. and Steyaert L. T (Eds.), Oxford University Press, N.Y. Cressie N. A., 1991, Statistics for Spatial Data. John Wiley and Sons Inc. Cressie, N. A., 1985, Fitting Variogram Models by Weighted Least Squares. Mathematical Geology, Vol. 17, pp. 563-578. Cressie, N. A., 1993, Geostatistics: A Tool for Environmental Modelers. pp. 414-421, in Environmental Modeling with GIS, Goodchild M. F., Parks B. O. and Steyaert L. T. (Eds.), Oxford University Press, N.Y. Cressie, N. The Origins of Kriging. Mathematical Geology, Vol.22, No. 3, 1990. Cressie, Noel A.C., 1993, “Statistics for Spatial Data”, John Wiley & Sons, Deutsch, C. V. and A. G. Journel. 1992. GSLIB. Geostatistical software library and user's guide. Oxford Univ. Press, Oxford. Dowd P. A., 1992, A Review of Recent Developments in Geostatistics. Computers and Geosciences, Vol. 17, No. 10, pp. 1481-1500. Dubrule O., 1984, Comparing Splines and Kriging. Computers and Geosciences, Vol. 10, No. 2-3, pp. 327-338. Dubrule, O. (1983). Two methods with different objectives: Splines and Kriging. Mathematical Geology 15, 245-257. Duchon, J. (1977), Splines minimizing rotation-invariant semi-norms in Sobolev spaces, in Constructive Theory of Functions of Several Variables, Springer-Verlag, Berlín, pp.85-100. Fotheringham S. and Rogerson P. A., 1994, Spatial Analysis and GIS, Taylor Francis Ltd., London. Geostatistics, 1996 http://java.ei.jrc.it/rem/gregoire/#2 Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY. Gillison, A. , Brewer, K. The use of Gradient Directed Transects or Gradsects in Natural Resourse Survey. Journal of Environmental Management (1985) 20, 103-127. Haining R., 1993, Spatial Data Analysis in the Social ancl Environmental Sciences. Cambridge University Press. Hóck, B.K T. W. Payn, J. W. Shirley (1993). Using a Geographic Information System ancl Geostatistics to estímate Site Index of Pinus Radiata for Kaingaroa Forest, New Zealancl. New Zealand Journal of Forestry Sciencc 23(3); 264-277 (1993). Hutchinson M.F. (1991). Climatic Analysis in Data Sparse Regions. In : R.C. Muchow and J.A. Bellamy (eds), 1991. Climatic Risk in Crop Production, CAB International, Wallingford, pp 55-71. Hutchinson,M.F. 1995a. Interpolation of mean rainfall using thin píate smoothing splines. International Journal Geographic Information Systems 9: 385-403. Hutchinson,M.F. and Gessler.P.E. 1994. Splines - more than just a smooth interpolator. Geoderma 62: 45-67. Isaaks, E. H. and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics. Oxford Univ. Press, New York, Oxford. Journel, A. G. ,and Ch. J. Huijhregts. 1978. Mining Geostatistics. Academic Press London. Journel, A.G. (1984). New Ways of Assessing Spatial Distribution of Pollutants. In G. Schweitzer (ed.) . Environmental Sampling for Hazardous Wastes, Washington DC: American Chemical Society, pp. 109- 118. Journel, A.G. (1984). Nonparametric Geostatistics for Risk and Additional sampling assessment. In L. H. Kieth (ed). Principies of Enviromental Sampling, Washington DC: American Chemical Society, pp. 45-72. Voltz M. & R. Webster. 1990. A comparison of Kriging, cubic splines and classification for predicting soil properties from sample information. Journal of Soil Science, 1990, 41, 473-490. Matheron, G. (1971). The Theory of Regionalizaecl Variables and its Applications. Les Cahiers du Centre de Morphologie Mathématique , No. 5. París: Ecole de Mines de París. Matheron, G. 1963. Principies of Geostatistics. Economic Geology 58, 1246-1266. Matheron, G.(1979). Recherche de Simplification dans un probléme de Cokrigeage. Fontainebleau. Centre de Géostatistique. Matheron, G., (1963), Principies of geostatistics, Economic Geology, 58, p. 1246-1266. Olea, R. A. 1975. Optimal mapping techniques using regionalized variable theory. Series on Spatial Analysis, No. 2. Kansas Geological Survey, Lawrence. Powell, M.J.D. The theory of Radial basis function approximation. In W.A. Light, editor, Advances in Numérica! Analysis II: Wavelets, Subdivisión Algorithms and Radial Functions. Pages 105-210. Oxford University Press, Oxford,UK, 1992. Schoenberg, I. (1964a), Spline functions and the problem of graduation, Proc.. Nat. Acad. Sci. U.S.A., 52, pp. 947-950. Schoenberg, I. (1964b), On interpolation by spline functions and its mínimum properties, Internat. Ser. Numer. Anal., 5, pp. 109-129. Schumaker, L. (1981), Spline Functions, John Wiley, New York. Sharov et. al. (1996) Spatial Variation Among Counts of Gypsy Moths. Enviromental Entomology. Vol. 25, no. 6. Vieira S.R. , Hatfield J.L., Nielsen D.R. and Biggar J. W. Geostatistical Theory and Application to Varibility of Some Agronomical Properties. HILGARDIA Vol 51. No. 3 June 1983. W.A. Light. Some aspects of radial basis function approximation. In S.P. Singh, editor, Approximation Theory, Spline Functions and Applications, pages 163-190. Kluwer Academic Publishers (Dortrecht), 1992. Wahba, G. 1990. Spline Models for Observational Data. CBMSNSF. Regional Conference Series in Mathematics 59. SIAM, Philadelphia.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rights.local.spa.fl_str_mv	Abierto (Texto Completo)
dc.rights.creativecommons.*.fl_str_mv	Atribución-NoComercial-SinDerivadas 2.5 Colombia
rights_invalid_str_mv	http://creativecommons.org/licenses/by-nc-nd/2.5/co/ Abierto (Texto Completo) Atribución-NoComercial-SinDerivadas 2.5 Colombia http://purl.org/coar/access_right/c_abf2
dc.coverage.spatial.spa.fl_str_mv	Bucaramanga (Santander, Colombia)
dc.coverage.campus.spa.fl_str_mv	UNAB Campus Bucaramanga
dc.publisher.grantor.spa.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv	Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv	Maestría en Ciencias Computacionales
institution	Universidad Autónoma de Bucaramanga - UNAB
bitstream.url.fl_str_mv	https://repository.unab.edu.co/bitstream/20.500.12749/25468/1/2001_Tesis_Hebert_Montegranario_OCR.pdf https://repository.unab.edu.co/bitstream/20.500.12749/25468/2/license.txt https://repository.unab.edu.co/bitstream/20.500.12749/25468/3/2001_Tesis_Hebert_Montegranario_OCR.pdf.jpg
bitstream.checksum.fl_str_mv	4d16825a17cd263f0afdf3e1063c8451 3755c0cfdb77e29f2b9125d7a45dd316 82ce8ba75c56fa2dc402b5853d178196
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional \| Universidad Autónoma de Bucaramanga - UNAB
repository.mail.fl_str_mv	repositorio@unab.edu.co
_version_	1831930970331152384
spelling	Leclerc, Gregoirec2dba4e0-abb9-492c-9e29-a1ceb7e1c4e5Montegranario Riascos, Hebert691593f0-c239-4968-aa24-25afc14e39dbMontegranario Riascos, Hebert [0000381918]Bucaramanga (Santander, Colombia)UNAB Campus Bucaramanga2024-07-12T19:41:56Z2024-07-12T19:41:56Z2001http://hdl.handle.net/20.500.12749/25468reponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLa predicción de una propiedad sobre la base de un conjunto de mediciones puntuales en una región es necesaria si se va a elaborar un mapa de la propiedad para la región. De las técnicas de predicción e interpolación espacial, Kriging es óptimo entre todos los procedimientos lineales, ya que es imparcial y tiene una variación mínima del error de predicción. En Cokriging, que tiene esta misma propiedad atractiva, se utilizan observaciones adicionales de una o más covariables, lo que puede contribuir a una mayor precisión de las predicciones. Un procedimiento más reciente son los Splines de píate fino (TPS), de los cuales el famoso spline cúbico para una variable es un caso particular. Este método no requiere análisis de correlación espacial y es más fácil de implementar en computadora. En este estudio intentamos mejorar la comprensión de estos tres métodos, proporcionando los fundamentos matemáticos y comparando su desempeño utilizando valores medios mensuales de variables clonadas como temperatura y precipitación para Colombia; Implementando los principales algoritmos con Delphi Pascal Lenguaje.Instituto Tecnológico de Estudios Superiores de Monterrey (ITESM)Universidad Autónoma de Occidentecapitulo.................................................................................................................................................................................................................i introducción......................................................................................................................................................................................................... 1.1 naturaleza del problema................................................................................................................................................................................6 1.2 enunciado del problema................................................................................................................................................................................7 1.3 objetivo general...............................................................................................................................................................................................9 1.3.1 objetivos específicos...................................................................................................................................................................................10 1.4 antecedentes...................................................................................................................................................................................................11 1.4.1 métodos de interpolación espacial...........................................................................................................................................................12 1.4.2 métodos geoestadísticos............................................................................................................................................................................14 1.4.3 métodos determinísticos (splines).............................................................................................................................................................17 capitulo ii...................................................................................................................................................................................................................... métodos geoestadísticos................................................................................................................................................................................................ 2.1 la teoría de variables regionalizadas............................................................................................................................................................21 2.1.1 dependencia espacial y semivariograma ...............................................................................................................................................22 2.1.2 propiedades generales del semivariograma...........................................................................................................................................23 2.1.3 suposiciones de estacionalidad................................................................................................................................................................28 2.1.4 relación entre covarianza y semivariograma..........................................................................................................................................29 2.1.5 semivariograma .........................................................................................................................................................................................31 2.1.6 semivariograma direccionales...................................................................................................................................................................32 2.2 kriging puntual................................................................................................................................................................................................34 2.2.1 deducción de las ecuaciones de kriging puntual..................................................................................................................................35 2.2.2 el sistema de kriging utilizando el semivariograma..................................................................................................................................36 2.3 cokriging........................................................................................................................................................................................................39 2.3.1 el semivariograma cruzado.......................................................................................................................................................................40 2.3.2 las ecuaciones de cokriging........................................................................................................................................................................40 2.3.3 el sistema de ecuaciones para cokriging..................................................................................................................................................44 capitulo iii................................................................................................................................................................................................................. métodos determinísticos....................................................................................................................................................................................... 3.1 interpolación mediante spline...................................................................................................................................................................... 46 3.2 funciones de base radial.................................................................................................................................................................................47 3.3 aplicación del método variacional...............................................................................................................................................................50 3.3.1 formula para el spline tps...........................................................................................................................................................................51 3.4 sistema de ecuaciones para tps....................................................................................................................................................................52 capitulo iv................................................................................................................................................................................................................ resultados............................................................................................................................................................................................................... 4.1 aspectos computacionales............................................................................................................................................................................54 4.2 indicadores para evaluar la precisión de la predicción con los métodos de interpolación.................................................................57 4.3 las variables climáticas con los métodos de interpolación con los métodos de interpolación.........................................................58 4.4 exploración de los datos con los métodos de interpolación........................................................................................................................60 4.4.1 la región de estudio.................................................................................................................................................................................61 4.5 variables que se analizaron......................................................................................................................................................................62 4.5.1 transformación de variables...................................................................................................................................................................68 4.6 análisis estructural...........................................................................................................................................................................................69 4.6.1 modelación del semivariograma................................................................................................................................................................71 4.6.2 precipitación.................................................................................................................................................................................................73 4.6.3 temperatura.................................................................................................................................................................................................75 4.6.4 radiación solar...............................................................................................................................................................................................77 4.7 resultados de las .............................................................................................................................................................................................79 4.7.1 características generales del clima colombiano......................................................................................................................................80 4.7.2 resultados utilizando kriging puntual........................................................................................................................................................81 4.7.3 resultados utilizando cokriging..................................................................................................................................................................88 4.7.4 resultados utilizando spline tps................................................................................................................................................................100 4.8 conclusiones y futura investigación.............................................................................................................................................................105 apéndices.............................................................................................................................................................................................................117 bibliografía...........................................................................................................................................................................................................128MaestríaPredicting a property based on a set of point measurements in a region is necessary if a property map is to be constructed for the region. Of the spatial interpolation and prediction techniques, Kriging is optimal among all linear procedures as it is unbiased and has minimal prediction error variation. In Cokriging, which has this same attractive property, additional observations of one or more covariates are used, which can contribute to greater prediction accuracy. A more recent procedure is Fine Piate Splines (TPS), of which the famous cubic spline for a variable is a particular case. This method does not require spatial correlation analysis and is easier to implement on a computer. In this study we attempt to improve the understanding of these three methods, providing the mathematical foundations and comparing their performance using monthly mean values of cloned variables such as temperature and precipitation for Colombia; Implementing the main algorithms with Delphi Pascal Language.Modalidad Presencialhttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Métodos de geoestadística e interpolación espacial aplicados a datos climáticosGeostatistics and spatial interpolation methods applied to climate dataMagíster en en Ciencias ComputacionalesUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaMaestría en Ciencias Computacionalesinfo:eu-repo/semantics/masterThesisTesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TMComputer sciencesSystems engineerWeather forecastMathGeologyStatistical methodsGeophysical predictionsMeteorologyInterpolation spacesCiencias computacionalesIngeniería de sistemasPredicciones geofísicasMeteorologíaEspacios de interpolaciónPronóstico del tiempoMatemáticasGeologíaMétodos estadísticosBurges T.M. , and Webster R. 1980a. "Optimal interpolation and Isarithmic Mapping of Soil Properties". Journal of Soil Science 31, 315- 331.Burges T.M. , and Webster R. 1980b. "Optimal interpolation and Isaritlnnic Mapping of Soil Properties". II Block Kriging. Journal of Soil Science 31, 333-341.Burges T.M. , and Webster R. 1980c. "Optimal interpolation and Isarithmic Mapping of Soil Properties". III Changing Drift and Universal Kriging. Journal of Soil Science 31, 505-524.Cárter J. and Roberts, S. A., 1996, An Investigation into the Use of Median Indicator Kriging to Assist in Post Accident Radiation Assessment, In Proceedings of the Seventh Symposium of Spatial Data Handling , Delft, Netherlands, August 1996, Vol. 2, pp. 9B27-9B40. Taylor and Francis.Cárter J., McLaren F. and Higgins N. A., 1997, An Investigation into the Applicability of Gcostatistical Techniques for Estimating Contamination Levels Following an Accidental Release of Radioactivity. Journal of Radiation Protection (in press).Chui, C. (1988), Multivariate Splines, Society for Industrial and Applied Mathematics, Philadelphia.PA.Clark I., 1979, Practical Geostatistics. Applied Science Publishers Ltd., London.Cressie N. A. and Hawkins D. M., 1980, Robust Estimation of the Variogram. I. Mathematical Geology, Vol. 12, pp. 115-125.Cressie N. A. and Ver Hoef J. M., 1993, Spatial Statistical Analysis of Environmental and Ecological Data. pp. 404-413, in Environmental Modeling with GIS, Goodchild M. F., Parks B. O. and Steyaert L. T (Eds.), Oxford University Press, N.Y.Cressie N. A., 1991, Statistics for Spatial Data. John Wiley and Sons Inc.Cressie, N. A., 1985, Fitting Variogram Models by Weighted Least Squares. Mathematical Geology, Vol. 17, pp. 563-578.Cressie, N. A., 1993, Geostatistics: A Tool for Environmental Modelers. pp. 414-421, in Environmental Modeling with GIS, Goodchild M. F., Parks B. O. and Steyaert L. T. (Eds.), Oxford University Press, N.Y.Cressie, N. The Origins of Kriging. Mathematical Geology, Vol.22, No. 3, 1990.Cressie, Noel A.C., 1993, “Statistics for Spatial Data”, John Wiley & Sons,Deutsch, C. V. and A. G. Journel. 1992. GSLIB. Geostatistical software library and user's guide. Oxford Univ. Press, Oxford.Dowd P. A., 1992, A Review of Recent Developments in Geostatistics. Computers and Geosciences, Vol. 17, No. 10, pp. 1481-1500.Dubrule O., 1984, Comparing Splines and Kriging. Computers and Geosciences, Vol. 10, No. 2-3, pp. 327-338.Dubrule, O. (1983). Two methods with different objectives: Splines and Kriging. Mathematical Geology 15, 245-257.Duchon, J. (1977), Splines minimizing rotation-invariant semi-norms in Sobolev spaces, in Constructive Theory of Functions of Several Variables, Springer-Verlag, Berlín, pp.85-100.Fotheringham S. and Rogerson P. A., 1994, Spatial Analysis and GIS, Taylor Francis Ltd., London.Geostatistics, 1996 http://java.ei.jrc.it/rem/gregoire/#2Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY.Gillison, A. , Brewer, K. The use of Gradient Directed Transects or Gradsects in Natural Resourse Survey. Journal of Environmental Management (1985) 20, 103-127.Haining R., 1993, Spatial Data Analysis in the Social ancl Environmental Sciences. Cambridge University Press.Hóck, B.K T. W. Payn, J. W. Shirley (1993). Using a Geographic Information System ancl Geostatistics to estímate Site Index of Pinus Radiata for Kaingaroa Forest, New Zealancl. New Zealand Journal of Forestry Sciencc 23(3); 264-277 (1993).Hutchinson M.F. (1991). Climatic Analysis in Data Sparse Regions. In : R.C. Muchow and J.A. Bellamy (eds), 1991. Climatic Risk in Crop Production, CAB International, Wallingford, pp 55-71.Hutchinson,M.F. 1995a. Interpolation of mean rainfall using thin píate smoothing splines. International Journal Geographic Information Systems 9: 385-403.Hutchinson,M.F. and Gessler.P.E. 1994. Splines - more than just a smooth interpolator. Geoderma 62: 45-67.Isaaks, E. H. and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics. Oxford Univ. Press, New York, Oxford.Journel, A. G. ,and Ch. J. Huijhregts. 1978. Mining Geostatistics. Academic Press London.Journel, A.G. (1984). New Ways of Assessing Spatial Distribution of Pollutants. In G. Schweitzer (ed.) . Environmental Sampling for Hazardous Wastes, Washington DC: American Chemical Society, pp. 109- 118.Journel, A.G. (1984). Nonparametric Geostatistics for Risk and Additional sampling assessment. In L. H. Kieth (ed). Principies of Enviromental Sampling, Washington DC: American Chemical Society, pp. 45-72.Voltz M. & R. Webster. 1990. A comparison of Kriging, cubic splines and classification for predicting soil properties from sample information. Journal of Soil Science, 1990, 41, 473-490.Matheron, G. (1971). The Theory of Regionalizaecl Variables and its Applications. Les Cahiers du Centre de Morphologie Mathématique , No. 5. París: Ecole de Mines de París.Matheron, G. 1963. Principies of Geostatistics. Economic Geology 58, 1246-1266.Matheron, G.(1979). Recherche de Simplification dans un probléme de Cokrigeage. Fontainebleau. Centre de Géostatistique.Matheron, G., (1963), Principies of geostatistics, Economic Geology, 58, p. 1246-1266.Olea, R. A. 1975. Optimal mapping techniques using regionalized variable theory. Series on Spatial Analysis, No. 2. Kansas Geological Survey, Lawrence.Powell, M.J.D. The theory of Radial basis function approximation. In W.A. Light, editor, Advances in Numérica! Analysis II: Wavelets, Subdivisión Algorithms and Radial Functions. Pages 105-210. Oxford University Press, Oxford,UK, 1992.Schoenberg, I. (1964a), Spline functions and the problem of graduation, Proc.. Nat. Acad. Sci. U.S.A., 52, pp. 947-950.Schoenberg, I. (1964b), On interpolation by spline functions and its mínimum properties, Internat. Ser. Numer. Anal., 5, pp. 109-129.Schumaker, L. (1981), Spline Functions, John Wiley, New York.Sharov et. al. (1996) Spatial Variation Among Counts of Gypsy Moths. Enviromental Entomology. Vol. 25, no. 6.Vieira S.R. , Hatfield J.L., Nielsen D.R. and Biggar J. W. Geostatistical Theory and Application to Varibility of Some Agronomical Properties. HILGARDIA Vol 51. No. 3 June 1983.W.A. Light. Some aspects of radial basis function approximation. In S.P. Singh, editor, Approximation Theory, Spline Functions and Applications, pages 163-190. Kluwer Academic Publishers (Dortrecht), 1992.Wahba, G. 1990. Spline Models for Observational Data. CBMSNSF. Regional Conference Series in Mathematics 59. SIAM, Philadelphia.ORIGINAL2001_Tesis_Hebert_Montegranario_OCR.pdf2001_Tesis_Hebert_Montegranario_OCR.pdfTesisapplication/pdf29482899https://repository.unab.edu.co/bitstream/20.500.12749/25468/1/2001_Tesis_Hebert_Montegranario_OCR.pdf4d16825a17cd263f0afdf3e1063c8451MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/25468/2/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD52open accessTHUMBNAIL2001_Tesis_Hebert_Montegranario_OCR.pdf.jpg2001_Tesis_Hebert_Montegranario_OCR.pdf.jpgIM Thumbnailimage/jpeg6399https://repository.unab.edu.co/bitstream/20.500.12749/25468/3/2001_Tesis_Hebert_Montegranario_OCR.pdf.jpg82ce8ba75c56fa2dc402b5853d178196MD53open access20.500.12749/25468oai:repository.unab.edu.co:20.500.12749/254682024-07-12 22:02:32.587open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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

Métodos de geoestadística e interpolación espacial aplicados a datos climáticos

Publicaciones similares