Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores
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| Data de Publicação: | 2019 |
| Tipo de documento: | Dissertação |
| Idioma: | por |
| Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRRJ |
| Texto Completo: | https://tede.ufrrj.br/jspui/handle/jspui/5830 |
Resumo: | Soils are a natural resource of great relevance, mainly due to their importance in the production of food, the support of biomes and in the storage of water, assuring the replacement of springs and water sources, as well as other environmental services. Thus, the knowledge about the soil properties and their distribution in the landscape is important for their management and for the territorial planning. The principal way to obtain information about the soils and its distribution is through soil surveys. The main hypothesis of the study is that from the techniques of digital mapping by using reference areas it is possible to predict the spatial distribution of soil units. Also, through the selection of predictor variables and the evaluation of quantitative predictive methods, the soil survey can be improved, reducing the subjective character of the interpretation, besides giving a quantitative character to the final product. The objective of this study was to evaluate the efficiency of tree-based predictive methods, Random Forest and Decision Tree, for the extrapolation of soil mapping unities in the municipalities of Nova Alvorada do Sul and Rio Brilhante, from 46 profiles described in Sidrol?ndia and Campo Grande, both located in Mato Grosso do Sul state. The approach through tree-based models enabled a quantitative evaluation of the factors involved in pedogenesis, which contributed to a better understanding of each factor and its direct contribution to soil formation. The use of the reference area proved to be adequate for the process of learning the morphometric patterns, as well as for the extrapolation of the mapping units to the entire area. Both predictive models tested proved to be quite efficient in the extrapolation of the mapping units, and the Random Forest model presented the best predictive performance, in all statistical indices evaluated, in relation to the Decision Tree model. The models based on trees can contribute to the knowledge about the soil formation factors and to qualify their contribution in the pedogenesis, as well as in the understanding of the pedoambientes and distribution of the classes of soil in the landscape; also, to support the mapping of similar areas not yet surveyed. |
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Anjos, L?cia Helena Cunha dos660.519.407-15Pinheiro, Helena Saraiva Koenow063.451.836-44Pinheiro, Helena Saraiva KoenowChagas, C?sar da SilvaCeddia, Marcos Bacis117.203.157-60http://lattes.cnpq.br/8202058126084086Xavier, Pedro Armentano Mudado2022-07-29T16:40:35Z2019-02-15XAVIER, Pedro Armentano Mudado. Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores. 77 f. Disserta??o (Mestrado em Agronomia, Ci?ncia do Solo) - Instituto de Agronomia, Universidade Federal Rural do Rio de Janeiro, Serop?dica, RJ, 2019.https://tede.ufrrj.br/jspui/handle/jspui/5830Soils are a natural resource of great relevance, mainly due to their importance in the production of food, the support of biomes and in the storage of water, assuring the replacement of springs and water sources, as well as other environmental services. Thus, the knowledge about the soil properties and their distribution in the landscape is important for their management and for the territorial planning. The principal way to obtain information about the soils and its distribution is through soil surveys. The main hypothesis of the study is that from the techniques of digital mapping by using reference areas it is possible to predict the spatial distribution of soil units. Also, through the selection of predictor variables and the evaluation of quantitative predictive methods, the soil survey can be improved, reducing the subjective character of the interpretation, besides giving a quantitative character to the final product. The objective of this study was to evaluate the efficiency of tree-based predictive methods, Random Forest and Decision Tree, for the extrapolation of soil mapping unities in the municipalities of Nova Alvorada do Sul and Rio Brilhante, from 46 profiles described in Sidrol?ndia and Campo Grande, both located in Mato Grosso do Sul state. The approach through tree-based models enabled a quantitative evaluation of the factors involved in pedogenesis, which contributed to a better understanding of each factor and its direct contribution to soil formation. The use of the reference area proved to be adequate for the process of learning the morphometric patterns, as well as for the extrapolation of the mapping units to the entire area. Both predictive models tested proved to be quite efficient in the extrapolation of the mapping units, and the Random Forest model presented the best predictive performance, in all statistical indices evaluated, in relation to the Decision Tree model. The models based on trees can contribute to the knowledge about the soil formation factors and to qualify their contribution in the pedogenesis, as well as in the understanding of the pedoambientes and distribution of the classes of soil in the landscape; also, to support the mapping of similar areas not yet surveyed.Os solos constituem um recurso natural de grande relev?ncia, sobretudo por sua import?ncia na produ??o de alimentos, na sustenta??o dos biomas e no armazenamento de ?gua, garantindo a reposi??o das nascentes e mananciais, al?m de outros servi?os ambientais. Assim, o conhecimento sobre as propriedades dos solos e sua distribui??o na paisagem ? importante para o seu manejo e para o planejamento territorial. A principal forma de se obter informa??es sobre os solos e sua distribui??o ? atrav?s de levantamentos de solos. A hip?tese principal do estudo ? que a partir das t?cnicas de mapeamento digital por ?reas de refer?ncia ? poss?vel predizer a distribui??o espacial das unidades de solos. Ainda, atrav?s da sele??o de vari?veis preditoras e da avalia??o de m?todos preditivos quantitativos pode-se aperfei?oar o mapeamento, reduzindo o car?ter subjetivo da interpreta??o, conferindo um car?ter quantitativo ao produto final. Sendo assim, o estudo teve como objetivo avaliar a efici?ncia dos m?todos preditivos baseados em ?rvores, Random Forest (RF) e ?rvores de Decis?o (AD), para a extrapola??o de unidades de mapeamento de solo nos munic?pios de Nova Alvorada do Sul e Rio Brilhante, a partir de 46 perfis descritos em Sidrol?ndia e Campo Grande, ambos no Mato Grosso do Sul. A abordagem atrav?s de modelos baseados em ?rvores possibilitou uma avalia??o quantitativa dos fatores envolvidos na pedog?nese, o que contribuiu para a melhor compreens?o sobre cada fator e sua contribui??o direta na forma??o dos solos. A utiliza??o dos dados legados mostrou-se promissora para o processo de aprendizado dos padr?es morfom?tricos, bem como para a extrapola??o das unidades de mapeamento para toda a ?rea. Ambos os modelos preditivos testados se mostraram bastante eficientes na extra??o das informa??es a partir dos dados de entrada e possibilitaram extrapola??o para ?reas semelhantes ainda n?o mapeadas, sendo o modelo RF o que apresentou o melhor desempenho preditivo, em todos os ?ndices estat?sticos avaliados, em rela??o ao modelo ?D. Os modelos baseados em ?rvores (AD e RF) podem contribuir para o conhecimento sobre os fatores de forma??o e para qualificar sua contribui??o na pedog?nese, bem como na compreens?o dos pedoambientes e distribui??o das classes de solo na paisagem, al?m de subsidiar o levantamento de ?reas semelhantes ainda n?o mapeadas.Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2022-07-29T16:40:35Z No. of bitstreams: 1 2019 - Pedro Armentano Mudado Xavier.pdf: 6245888 bytes, checksum: 26cfb4f884591283017810c0b70cb483 (MD5)Made available in DSpace on 2022-07-29T16:40:35Z (GMT). No. of bitstreams: 1 2019 - Pedro Armentano Mudado Xavier.pdf: 6245888 bytes, checksum: 26cfb4f884591283017810c0b70cb483 (MD5) Previous issue date: 2019-02-15CAPES - Coordena??o de Aperfei?oamento de Pessoal de N?vel Superiorapplication/pdfhttps://tede.ufrrj.br/retrieve/70091/2019%20-%20Pedro%20Armentano%20Mudado%20Xavier.pdf.jpgporUniversidade Federal Rural do Rio de JaneiroPrograma de P?s-Gradua??o em Agronomia - Ci?ncia do SoloUFRRJBrasilInstituto de AgronomiaALFONSI, R. R.; PINTO, H. S.; ZULLO J?NIOR, J.; CORAL, G.; ASSAD, E. D.; EVANGELISTA, B. A.; LOPES, T. S. S.; MARRA, E.; BEZERRA, H. S.; HISSA, H. R.; FIGUEIREDO, A. F.; SILVA, G. G.; SUCHAROV, E. C.; ALVES, J.; MARTORANO, L. G.; BOUHID, A.; ROM?SIO, G.; BASTOS ANDRADE, W. E. Zoneamento Clim?tico da Cultura do Caf? (Coffea ar?bica) no Estado de Mato Grosso do Sul. Campinas. Campinas: IAC: UNICAMP; Bras?lia: Embrapa Cerrados; Niter?i: Pesagro-Rio; Rio de Janeiro: SIMERJ: Embrapa Solos. 2002. ARROUAYS, D., GRUNDY, M.G., HARTEMINK, A.E., HEMPEL, J.W., HEUVELINK, G.B.M., HONG, S.Y., LAGACHERIE, P., LELYK, G., MCBRATNEY, A.B., MCKENZIE, N.J., MENDONCA-SANTOS, M.D.L., MINASNY, B., MONTANARELLA, L., ODEH, I.O.A., SANCHEZ, P.A., THOMPSON, J.A., ZHANG, G.-L., GlobalSoilMap: toward a fine-resolution global grid of soil properties. Advances in Agronomy, pp. 93?134. 2014. ANTUNES, M. A. H., DEBIASI, P., COSTA, A. R., GLERIANI, J. M., Corre??o atmosf?rica de imagens ALOS/AVNIR-2 utilizando o modelo 6S. Revista Brasileira de Cartografia, v. 64, p. 531-539, 2012. BAGATINI, T.; GIASSON, E.; TESKE, R. Expans?o de mapas pedol?gicos para ?reas fisiograficamente semelhantes por meio de mapeamento digital de solos. Pesquisa Agropecu?ria Brasileira. v.51. 2016 BIVAND, R.; KEITT, T.; ROWLINGSON, B. rgdal: Bindings for the geospatial data abstraction library, 2017. Dispon?vel em: <https://cran.r-project.org/package=rgdal> BEHRENS, T.; FOSTER, H.; SCHOLTEN, T.; STEINRUCKEN, U.; SPIES, E. D.; GOLDSCHMITT, M. Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science 168: 21-33. 2005. BEHRENS, T., ZHU, A.-X., SCHMIDT, K., SCHOLTEN, T., Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155, 175?185. 2010. BRAGA, A. de P.; CARVALHO L. F. de; LUDEMIR., T. B. Redes Neurais Artificiais: Teoria e Aplica??es 2.ed. Rio de Janeiro: LTC ? Livros T?cnicos e Cient?ficos Editora. 226p. 2007 BRASIL. Minist?rio de Minas e Energia. Secretaria Geral. Projeto RADAMBRASIL. Folha SF. 21, Campo Grande: geologia, geomorfologia, pedologia, vegeta??o e uso potencial da terra, v. 28. Rio de Janeiro: 416p. il. 1982. BREIMAN, L., Random forests. Machine Learning 45, 5?32. 2001. BREIMAN, L.; FRIEDMAN, J.; OLSHEN, R.; STONE, C. Classification and regression trees. The Wadsworth statistics/Probability Series. Wadsworth International Group, Belmont, CA. 358p. 1984. BRUS, D. J.; KEMPEN, B.; HEUVELINK, G. B. M. Sampling for validation of digital soil maps. Eur J Soil Sci. 62:394-407. 2011. CARVALHO J?NIOR, W. de. Classifica??o supervisionada de pedopaisagens no dom?nio dos mares de morros utilizando redes neurais artificiais. Tese de doutorado. UFV, MG. 160p. 2005. CRESSIE, N., Statistics for Spatial Data. Wiley, New York, 900 pp. 1991. CHAGAS, C. da S. Mapeamento digital de solos por correla??o ambiental e redes neurais em uma bacia hidrogr?fica de dom?nio de mar de morros. Tese de doutorado. UFV, MG. 239p. 2006. CHAGAS, C. S.; FERNANDES FILHO, E. I.; VIEIRA, C. A. O.; SCHAEFER, C. E. G. R.; CARVALHO J?NIOR, W. Topographic attributes and Landsat 7 data in the digital soil mapping using neural networks. Pesquisa Agropecu?ria Brasileira 45: 497-507 (in Portuguese, with abstract in English). 2010. CHAGAS, C. S.; FERNANDES FILHO, E. I.; VIEIRA, C. A. O. Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Revista Brasileira de Ci?ncia do Solo 37: 339-351. 2013. CHASMER, L.; HOPKINSON, C.; VENESS, T.; QUINTON, W.; BALTZER, J. A decisiontree classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sensing of Environment 143: 73?84. 2014. COELHO, F. F. Compara??o de m?todos de mapeamento digital de solos atrav?s de vari?veis geomorfom?tricas e sistemas de informa??es geogr?ficas. Disserta??o de Mestrado em Sensoriamento Remoto e Meteorologia. Universidade Federal do Rio Grande do Sul. Porto Alegre, 80 f. 2010. CONGALTON, R.G., GREEN, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, New York (160 pp.). 1999. CONRAD, O., BECHTEL, B., BOCK, M., DIETRICH, H., FISCHER, E., GERLITZ, L., WEHBERG, J., WICHMANN, V., and BOEHNER, J. (2018): System for Automated Geoscientific Analyses (SAGA) v. 7.0.0. Geosci. Model Dev., 8, 1991-2007. 2018. DEUTSCH, C. V., JOUMEL, A. J. Geostatistical Software Library and User?s Guide. Oxford Univ. Press, 340 pp. 1992. DOKUCHAEV, V. V., 1883/1948/1967. Russian Chernozem, in, Selected works of V. V. Dokuchaev, Moscow, 1948, 1, 14?419. Jerusalem: Israel Program for Scientific Translations Ltd. (for USDA-NSF), Publ. by S. Monson, 1967. (Transl. into English by N.Kaner). DRAY, S., SIBERCHICOT, M. A., Package ?ADE4?. https://cran.rproject. org/web/packages/ade4/ade4.pdf. 409p. 2018 ELKAN, C. Evaluating classifiers. San Diego: University of California; 2012. EMBRAPA. Centro Nacional de Pesquisa de Solos. Mapeamento digital de classes e atributos de solos: m?todos, paradigmas e novas t?cnicas. Rio de Janeiro, RJ: Embrapa Solos. 19p. (Embrapa Solos. Documentos; n. 55) 2003. EMBRAPA. Centro Nacional de Pesquisa de Solos. Embrapa Solos e suas origens. Rio de Janeiro, RJ: Embrapa Solos. Dispon?vel no site: https://www.embrapa.br/solos/historia. (acesso 03/04/2019). EMBRAPA. Centro Nacional de Pesquisa de Solos. Levantamento de reconhecimento de baixa intensidade dos solos do munic?pio de Campo Grande - Zoneamento agroecol?gico do Estado do Mato Grosso do Sul. Rio de Janeiro: Embrapa Solos, (Embrapa Solos. Relat?rio T?cnico). 2009. EMBRAPA. Centro Nacional de Pesquisa de Solos. Levantamento de reconhecimento de baixa intensidade dos solos do munic?pio de Sidrol?ndia - Zoneamento agroecol?gico do Estado do Mato Grosso do Sul. Rio de Janeiro: Embrapa Solos, (Embrapa Solos. Relat?rio T?cnico). 2010. ESRI. Environmental Systems Research Institute Inc. Redlands. CA. Software: ArcGIS e ArcINFO ? v.10. 2010. FAVROT, J. C. A strategy for large scale soil mapping: the reference areas method. Science du Sol 27: 351-368 (in French, with abstract in English). 1989. GIASSON, E.; SARMENTO, E. C.; WEBER, E.; FLORES, C. A. e HASENACK, H. Decision trees for digital soil mapping on subtropical basaltic steeplands. Sci. Agr?., 68:167- 174, 2011. GINI, C. Variabilit? e Mutabilit?, Bologna: Universit? di Cagliari. 158p. 1912. GRINAND, C.; ARROUAYS, D.; LAROCHE, B.; MARTIN, M. P. Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma 143: 180-190, 2008. HANSEN, M. K., BROWN, D. J., DENNISON, P. E., GRAVES, S. A., BRICKLEMYER, R. S., Inductively mapping expert-derived soil-landscape units within dambo wetland catenae using multispectral and topographic data. Geoderma 150 (1?2), 72?84. http://dx.doi.org/10.1016/j.geoderma.2009.01.013. 2009. HENGL, T. E HEUVELINK, G. B. M. New Challenges for Predictive Soil Mapping. Anais. Global Workshop on Digital Soil Mapping. September 14-17, Montpellier AGROM/ INRA Campus. 2004. HENGL, T., DE JESUS, J. M., MACMILLAN, R. A., BATJES, N. H., HEUVELINK, G. B. M., RIBEIRO, E., SAMUEL-ROSA, A., KEMPEN, B., LEENAARS, J. G. B., WALSH, M. G., GONZALEZ, M. R., SoilGrids1km ? global soil information based on automated mapping. 2014. HENGL, T., HEUVELINK, G. B. M., KEMPEN, B., LEENAARS, J. G. B., TAMENE, L., TONDOH, J. E., Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. 2015. H?FIG, P.; GIASSON, E.; VENDRAME, P. R. S. Mapeamento digital de solos com base na extrapola??o de mapas entre ?reas fisiograficamente semelhantes. Pesquisa Agropecu?ria Brasileira, v.49, p.958-966, 2014. DOI: 10.1590/S0100-204X2014001200006. IBGE. Instituto Brasileiro de Geografia e Estat?stica. Mapas Tem?ticos do Brasil 1:250.000. Dispon?vel em <https://mapas.ibge.gov.br/tematicos.html>. Consulta em: 10/04/2017. INPE, Manuais; Tutorial de geoprocessamento; Opera??es aritm?ticas, dispon?vel em: http://www.dpi.inpe.br/spring/portugues/tutorial/operacao.html. Acesso em 09/09/2018b. ISAAKS, E .H., SRIVASTAVA, R. M., An Introduction to Applied Geostatistics. Oxford, New York, 561 pp. 1989. JAFARI, A., KHADEMI, H., FINKE, P. A., VAN DE WAUW, J., AYOUBI, S. Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma 232-234, 148?163. doi:10.1016/j.geoderma.2014.04.029. 2014. JENNY, H. Factors of soil formation: a system of quantitative pedology. New York: Mcgraw-Hill. 281p. 1941. JENSEN, J.R. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. S?o Jos? dos Campos: Par?ntese, p 604, 2009. JEONG, G.; OEVERDIECK, H.; PARK, S. J.; HUWE, B.; LIESS, M. Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain. Catena, v. 154, p. 73?84, 2017. JEUNE, W.; FRANCELINO, M. R.; SOUZA, E. DE; FERNANDES FILHO, E. I.; ROCHA, G. C. Multinomial logistic regression and random forest classifiers in digital mapping of soil classes in western Haiti. Revista Brasileira de Ciencia do Solo, v. 42, p. 1?20, 2018. JOURNEL, A.J., HUIJBREGTS, C.H. J. Mining Geostatistics. Academic Press. New York. 1978. KEMPEN, B; BRUS, D. J.; HEUVELINK, G. B. M.; STOORVOGEL, J. J. Updating the 1:50,000 Dutch soil map using legacy soil data: a multinomial logistic regression approach. Geoderma 151: 311-326. 2009. KINCAID, T., OLSEN, T., STEVENS, D., PLATT, C., WHITE, C.,REMINGTON, R. Spsurvey package: Spatial Survey Design and Analysis, CRAN r-project. 193p. 2014. KOMSTA, L. Package ?outliers?. https://cran.rproject. org/web/packages/outliers/outliers.pdf [20 de Fevereiro, 2015] 15p. 2015 K?PPEN, W. Climatologia: con um est?dio de los climas de la Tierra. M?xico: Fondo de Cultura Economica, 478 p. 1948. LACERDA FILHO, J. W.; BRITO, R. S.C.; SILVA, M.G.; OLIVEIRA, C.C. DE; MORETON, L.C.; MARTINS, E.G.; LOPES, R.C.; LIMA, T.M.; LARIZZATTI, J.H.; VALENTE, C.R. Geologia e Recursos Minerais do Estado de Mato Grosso do Sul. Programa Integra??o, Atualiza??o e Difus?o de Dados de Geologia do Brasil. Conv?nio CPRM/SICME-MS, MME, 121 p. 2006. LAGACHERIE, P.; LEGROS, J. P.; BURROUGH, P. A. A soil survey procedure using the knowledge on soil pattern of a previously mapped reference area. Geoderma 65: 283-301, 1995. LAGACHERIE, P. e MCBRATNEY, A. B. Spatial soil information systems and spatial soil inference systems: Perspectives for digital soil mapping. In: LAGACHERIE, P.; MCBRATNEY, A. e VOLTZ, M., eds. Digital soil mapping: An introductory perspective. Amsterdam, Elsevier, p.3-22. 2007. LASTORIA, G. Hidrogeologia da Forma??o Serra Geral no Estado de Mato Grosso do Sul. Rio Claro, 2002. 133 p. Tese (Doutoramento em Geoci?ncias e Meio Ambiente). Instituto de Geoci?ncias e Ci?ncias Exatas ? IGCE ? UNESP ? Rio Claro, 2002. LEMERCIER, B.; LACOSTE, M.; LOUM, M; WALTER, C. Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach, Geoderma, In Press, Corrected Proof. 2011. LEPSCH, I. F. Forma??o e conserva??o dos solos. S?o Paulo, Oficina de Textos, 178p. 2002. LIAW, A.; WIENER, M. Random Forest: Breiman and Cutler?s Random Forests for Classification and Regression. URL http://cran.rproject. org/web/packages/randomForest/index.html. 2009. MACHADO, F. B., NARDY, A. J. R., ROCHA-J?NIOR, E. R. V., MARQUES, L. S., and OLIVEIRA, M. A. F.: Geologia e Litogeoqu?mica da Forma??o Serra Geral nos Estados de Mato Grosso e Mato Grosso do Sul, Geoci?ncias (S?o Paulo. Impresso), 28, 523?540, 2009. MATHERON, G., The theory of regionalised variables and its applications. Les Cahicrs du centre de morphologie mathematique de Fontainbleu. Ecole Nationale Superieur des Mines de Paris. 1971. MATO GROSSO DO SUL. SECRETARIA DE PLANEJAMENTO E COORDENA??O GERAL - SEPLAN-MS. Macrozoneamento geoambiental do Estado de Mato Grosso do Sul. Campo Grande, 242 p. 1989. McBRATNEY, A.B.; MENDON?A-SANTOS, M.L.; MINASNY, B. On digital soil mapping. Geoderma, v.117, p. 3-52. 2003. MEIER, M.; SOUZA, E.; FRANCELINO, M. R.; FERNANDES FILHO, E. I.; SCHAEFER, C. E. G. Digital soil mapping using machine learning algorithms in a tropical mountainous area. Revista Brasileira de Ci?ncia do Solo, v. 42, p. 1?23, 2018. MILANI, E. J.; FRANCA, A.; SCHNEIDER, R. Bacia do Paran?. Boletim de Geoci?ncias da Petrobr?s 8 (1), 69-82. 1994. MILANI E. D.; MELO J. H. G.; SOUZA P. A.; FERNANDES L. A.; FRAN?A A. B. Bacia do Paran?. Boletim de Geoci?ncias da Petrobras, 15(2): 265-287. 2007. MINASNY, B.; McBRATNEY, A.B.; SANTOS, M. de L.M.; SANTOS H.G. dos. Revis?o sobre Fun??es de Pedotransfer?ncia (PTFs) e Novos M?todos de Predi??o de Classes de Solos e Atributos do Solo. Rio de Janeiro: Embrapa Solos, 50 p. (Embrapa Solos. Documentos n. 45). 2003. MINASNY, B. e McBRATNEY, A. B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers e Geosciences, v.32. p.1378- 1388. 2006. MINASNY, B.; McBRATNEY, A. B.; LARK, M. Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures. In: Digital Soil Mapping With Limited Data. HARTEMINK, A. E.; McBRATNEY, A. B.; MENDON?A-SANTOS, M. de L. (Eds.), Springer. p. 15-30. 2008. MINASNY, B.; MCBRATNEY, A. B. 2015. Digital soil mapping: a brief history and some lessons. Geoderma. In press. MORAN C. J.; BUI E. N.; Spatial data mining for enhanced soil map modelling. Int J Geogr Inf Sci.;16:533-49. 2002. MORA-VALLEJO, A., CLAESSENS, L., STOORVOGEL, J., HEUVELINK, G. B. M. Small scaledigital soil mapping in Southeastern Kenya. Catena, v. 76, p. 44-53. 2008. OLAYA, V. A gentle introduction to SAGA GIS. 2004. 202p. Dispon?vel em: http://downloads.sourceforge.net/saga-gis/SagaManual.pdf. Acesso em 04/07/18. PEATE, D. W.; HAWKESWORTH, C. J.; MANTOVANI, M. SM. Chemical stratigraphy of the Paran? lavas (South Am?rica): classification of magma types and their spatial distribution. Bulletin of Volcanology, Berlin, v.55, n. 1, p. 119-139. 1992. PEBESMA, E., BIVAND, R., ROWLINGSON, B., RUBIO, V. G., HIJMANS, R., SUMNER, M., MACQUENN, D., LEMON, J., O?BRIEN, J., O?ROURKE, J., Package ?SP?. https://cran.r-project.org/web/packages/sp/sp.pdf 118p. 2018 PEDREGOSA, F.; VAROQUAUX, G.; GRAMFORT, A.; MICHEL, V.; THIRION, B.; GRISEL, O.; BLONDEL, M.; PRETTENHOFER, P.; WEISS, R.; DUBOURG, V.; VANDERPLAS, J.; PASSOS, A.; COURNAPEAU, D.; BRUCHER, M.; PERROT, M.; DUCHESNAY, E. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, v. 12, p. 2825-2830, 2011. PINHEIRO, H.S.K. Mapeamento digital de solos por redes neurais artificiais da Bacia Hidrogr?fica do Rio Guapi-Macacu, RJ. Disserta??o de Mestrado. Universidade Federal Rural do Rio de Janeiro. 151p. 2012. PINHEIRO, H. S. K. M?todos de mapeamento digital aplicados na predi??o de classes e atributos dos solos da Bacia Hidrogr?fica do Rio Guapi-Macacu, RJ. Tese de Doutorado. Universidade Federal Rural do Rio de Janeiro, 180f. 2015. PINHEIRO, H. S. K.; OWENS, P. R.; ANJOS, L. H. C.; CARVALHO J?NIOR, W.; CHAGAS, C. S. Tree-based techniques to predict soil units, Soil Research 55(8) 788-798. 2017. PRONASOLOS - Programa Nacional de Solos do Brasil / Jos? Carlos Polidoro ... [et al]. ? Dados eletr?nicos. ? Rio de Janeiro: Embrapa Solos, 53 p. ? (Documentos / Embrapa Solos, ISSN 1517-2627; 183). 2016. R CORE TEAM, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2017. R CORE TEAM, Package ?FOREIGN?. https://cran.rproject. org/web/packages/foreign/foreign.pdf. 25p. 2018 RAD, M. R. P.; TOOMANIAN, N.; KHORMALI, F.; BRUNGARD, C. W.; KOMAKI, C. B.; BOGAERT, P. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, v. 232?234, p. 97?106, 2014. ROKACH, L.; MAIMON, O. Data mining with decision trees. Theory and applications. World Scientific Publishing. 2008. SAMUEL-ROSA, A.; HEUVELINK, G. B. M.; VASQUES, G. M.; ANJOS, L. H. C. Do more detailed environmental covariates deliver more accurate soil maps? Geoderma, v. 243?244, p.214?227. 2015 SANTOS, H. G., JACOMINE, P. K. T, ANJOS, L. H. C., OLIVEIRA, V. A., LUMBRERAS, J.F, COELHO, M. Z., ALMEIDA, J. A., ARAUJO FILHO, J. C., OLIVEIRA, J. B., CUNHA, T. J. F. Sistema brasileiro de classifica??o de solos. 5. ed.Bras?lia: Embrapa, 356 p. 2018. SANTOS, M. de L. M., SANTOS, H. G. dos, Mapeamento digital de classes e atributos de solos. Embrapa solos. Documentos 55. 2003. SANTOS, M. L. M., SANTOS, H. G., DART, R.de O, PARES, J. G. Mapeamento Digital de Classes de Solos no Estado do Rio de Janeiro. 28p. (Boletim de Pesquisa e Desenvolvimento 119) Dezembro, 2007 SARMENTO, E. C.; GIASSON, E.; WEBER, E. J.; FLORES, C. A.; ROSSITER, D. G.; HASENACK, H. Caracteriza??o de mapas legados de solos: uso de indicadores em mapas com diferentes escalas no Rio Grande do Sul. Revista Brasileira de Ci?ncia do Solo, v.38, p.1672-1680, 2014. DOI: 10.1590/S0100-06832014000600002. SCHMIDT, K. BEHRENS, T. SCHOLTEN, T. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma, v.146, p.138-146, 2008. SILVA, V. A., CURI, N., MARQUES, J. J. G., CARVALHO, L. M. T. D., SANTOS, W. J. R., Soil maps, field knowledge, forest inventory and ecological-economic zoning as a basis for agricultural suitability of lands in Minas Gerais elaborated in GIS. Ci?nc. Agrotecnologia 37, 538?549. 2013. SILVA, S. H. G., OWENS, P. R., DUARTE DE MENEZES, M., REIS SANTOS, W. J., CURI, N., A technique for low cost soil mapping and validation using expert knowledge on a watershed in Minas Gerais, Brazil. Soil Sci. Soc. Am. J. 78, 1310?1319. 2014. SINDAYIHEBURA, A.; OTTOY, S.; DONDEYNE, S.; MEIRVENNE, M. V.; ORSHOVEN, J.V. Comparing digital soil mapping techniques for organic carbon and claycontent: Case study in Burundi's central plateaus. Catena, v. 156, p. 161?175. 2017. SINGH, A.N. e DWIVEDI, R. S. The utility of LANDSAT imagery as an integral part of the data base for small-scale soil mapping. International Journal of Remote Sensing, v. 7, n. 9, p.1099-1108, 1986. STEYERBERG, E. W. Validation of prediction models. In: Steyerberg EW, editor. Clinical prediction models. New York: Springer p.299-311. 2009. STROBL, C.; BOULESTEIX, A. L.; ZEILEIS, A.; HOTHORN, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, v. 8, n. 1, p.25, 2007. STUMPF, F.; SCHMIDT, K.; BEHRENS, T.; SCH?NBRODT-STITT, S.; BUZZO, G.; DUMPERTH, C.; WADOUX, A.; XIANG, W.; SCHOLTEN, T. Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping. Journal of Plant Nutrition and Soil Science, v. 000, p. 1?11, 2016. TAGHIZADEH-MEHRJARDI, R., NABIOLLAHI, K., MINASNY, B., TRIANTAFILIS, J., Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma 253-254, 67?77. 2015 TEN CATEN, A. Mapeamento digital de solos: metodologias para atender a demanda por informa??o espacial em solos. Tese de Doutorado. Universidade Federal de Santa Maria. 108p. 2011. TEN CATEN, A.; DALMOLIN, R. S. D.; PEDRON, F. de A.; RUIZ, L. F. C.; SILVA, C. A. da. An apropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil. Revista Brasileira de Ci?ncia do Solo, v.37, p.359-366, 2013. DOI: 10.1590/S0100- 06832013000200007. THERNEAU, T.; ATKINSON, B.; RIPLEY, B. RPART: Recursive Partitioning and Regression Trees. (Author of initial R port) 2015-02-24. 2015. TESKE, R.; GIASSON, E.; BAGATINI, T. Compara??o de esquemas de amostragem para treinamento de modelos preditores no mapeamento digital de classes de solos, Rio Grande do Sul, Brazil. Revista Brasileira de Ci?ncia do Solo, v.39, p.14-20, 2015. USGS GLOBAL VISUALIZATION VIEWER (Usa). Frequently Asked Questions about the Landsat Missions. 2013. Dispon?vel em: <http://landsat.usgs.gov/L8_band_combos.php>. Acesso em: 22 de novembro. 2017. VERMOTE, E. F., TANRE, D., DEUZE, J. L., HERMAM, M., MORCRETTE, J. J., Second Simulation of Satellite Signal in the Solar Spectrum, 6S: An overview., IEEE Transactionson Geoscience and Remote Sensing v 35, n 3, p 675-686, 1997. WEBSTER, R., OLIVER, M. A., Statistical Methods in Soil and Land Resources Survey. Oxford Univ. Press. 1990. WERE, K.; BUI, D. T.; DICK, ?. B.; SINGH, B. R. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, v. 52, p. 394?403, 2015. YANG, R. M.; ZHANG, G. L.; LIU, F.; LU, Y. Y.; YANG, F.; YANG, F.; YANG, M.; ZHAO, Y. G.; LI, D. C. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, v. 60, p. 870?878, 2016. YANG, W.; YANG, L.; MERCHANT, J.W. An assessment of AVHRR/NDVIecoclimatological relations in Nebraska. USA. International Journal of Remote Sensing, v.10. p. 2161-2180. 1997. YIGINI, Y.; PANAGOS, P., Reference area method for mapping soil organic carbon content at regional scale. Procedia Earth and Planetary Science 10: 330-338, 2014. YOUNG, P.; PARKINSON, S.; LEES, M. Simplicity out of complexity in environmental modelling: Occam's razor revisited. 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| dc.title.por.fl_str_mv |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| dc.title.alternative.eng.fl_str_mv |
Digital mapping of soils in the Mato Grosso do Sul state by reference areas and tree-based predictive models |
| title |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| spellingShingle |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores Xavier, Pedro Armentano Mudado Minera??o de dados Pedometria Dados legados Levantamento de Solos Landsat 8 Data mining Pedometrcs Legacy data Soil survey Agronomia |
| title_short |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| title_full |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| title_fullStr |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| title_full_unstemmed |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| title_sort |
Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores |
| author |
Xavier, Pedro Armentano Mudado |
| author_facet |
Xavier, Pedro Armentano Mudado |
| author_role |
author |
| dc.contributor.advisor1.fl_str_mv |
Anjos, L?cia Helena Cunha dos |
| dc.contributor.advisor1ID.fl_str_mv |
660.519.407-15 |
| dc.contributor.advisor-co1.fl_str_mv |
Pinheiro, Helena Saraiva Koenow |
| dc.contributor.advisor-co1ID.fl_str_mv |
063.451.836-44 |
| dc.contributor.referee1.fl_str_mv |
Pinheiro, Helena Saraiva Koenow |
| dc.contributor.referee2.fl_str_mv |
Chagas, C?sar da Silva |
| dc.contributor.referee3.fl_str_mv |
Ceddia, Marcos Bacis |
| dc.contributor.authorID.fl_str_mv |
117.203.157-60 |
| dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8202058126084086 |
| dc.contributor.author.fl_str_mv |
Xavier, Pedro Armentano Mudado |
| contributor_str_mv |
Anjos, L?cia Helena Cunha dos Pinheiro, Helena Saraiva Koenow Pinheiro, Helena Saraiva Koenow Chagas, C?sar da Silva Ceddia, Marcos Bacis |
| dc.subject.por.fl_str_mv |
Minera??o de dados Pedometria Dados legados Levantamento de Solos Landsat 8 |
| topic |
Minera??o de dados Pedometria Dados legados Levantamento de Solos Landsat 8 Data mining Pedometrcs Legacy data Soil survey Agronomia |
| dc.subject.eng.fl_str_mv |
Data mining Pedometrcs Legacy data Soil survey |
| dc.subject.cnpq.fl_str_mv |
Agronomia |
| description |
Soils are a natural resource of great relevance, mainly due to their importance in the production of food, the support of biomes and in the storage of water, assuring the replacement of springs and water sources, as well as other environmental services. Thus, the knowledge about the soil properties and their distribution in the landscape is important for their management and for the territorial planning. The principal way to obtain information about the soils and its distribution is through soil surveys. The main hypothesis of the study is that from the techniques of digital mapping by using reference areas it is possible to predict the spatial distribution of soil units. Also, through the selection of predictor variables and the evaluation of quantitative predictive methods, the soil survey can be improved, reducing the subjective character of the interpretation, besides giving a quantitative character to the final product. The objective of this study was to evaluate the efficiency of tree-based predictive methods, Random Forest and Decision Tree, for the extrapolation of soil mapping unities in the municipalities of Nova Alvorada do Sul and Rio Brilhante, from 46 profiles described in Sidrol?ndia and Campo Grande, both located in Mato Grosso do Sul state. The approach through tree-based models enabled a quantitative evaluation of the factors involved in pedogenesis, which contributed to a better understanding of each factor and its direct contribution to soil formation. The use of the reference area proved to be adequate for the process of learning the morphometric patterns, as well as for the extrapolation of the mapping units to the entire area. Both predictive models tested proved to be quite efficient in the extrapolation of the mapping units, and the Random Forest model presented the best predictive performance, in all statistical indices evaluated, in relation to the Decision Tree model. The models based on trees can contribute to the knowledge about the soil formation factors and to qualify their contribution in the pedogenesis, as well as in the understanding of the pedoambientes and distribution of the classes of soil in the landscape; also, to support the mapping of similar areas not yet surveyed. |
| publishDate |
2019 |
| dc.date.issued.fl_str_mv |
2019-02-15 |
| dc.date.accessioned.fl_str_mv |
2022-07-29T16:40:35Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| format |
masterThesis |
| status_str |
publishedVersion |
| dc.identifier.citation.fl_str_mv |
XAVIER, Pedro Armentano Mudado. Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores. 77 f. Disserta??o (Mestrado em Agronomia, Ci?ncia do Solo) - Instituto de Agronomia, Universidade Federal Rural do Rio de Janeiro, Serop?dica, RJ, 2019. |
| dc.identifier.uri.fl_str_mv |
https://tede.ufrrj.br/jspui/handle/jspui/5830 |
| identifier_str_mv |
XAVIER, Pedro Armentano Mudado. Mapeamento digital de solos no estado do Mato Grosso do Sul a partir de dados legados e modelos preditivos baseados em ?rvores. 77 f. Disserta??o (Mestrado em Agronomia, Ci?ncia do Solo) - Instituto de Agronomia, Universidade Federal Rural do Rio de Janeiro, Serop?dica, RJ, 2019. |
| url |
https://tede.ufrrj.br/jspui/handle/jspui/5830 |
| dc.language.iso.fl_str_mv |
por |
| language |
por |
| dc.relation.references.por.fl_str_mv |
ALFONSI, R. R.; PINTO, H. S.; ZULLO J?NIOR, J.; CORAL, G.; ASSAD, E. D.; EVANGELISTA, B. A.; LOPES, T. S. S.; MARRA, E.; BEZERRA, H. S.; HISSA, H. R.; FIGUEIREDO, A. F.; SILVA, G. G.; SUCHAROV, E. C.; ALVES, J.; MARTORANO, L. G.; BOUHID, A.; ROM?SIO, G.; BASTOS ANDRADE, W. E. Zoneamento Clim?tico da Cultura do Caf? (Coffea ar?bica) no Estado de Mato Grosso do Sul. Campinas. Campinas: IAC: UNICAMP; Bras?lia: Embrapa Cerrados; Niter?i: Pesagro-Rio; Rio de Janeiro: SIMERJ: Embrapa Solos. 2002. ARROUAYS, D., GRUNDY, M.G., HARTEMINK, A.E., HEMPEL, J.W., HEUVELINK, G.B.M., HONG, S.Y., LAGACHERIE, P., LELYK, G., MCBRATNEY, A.B., MCKENZIE, N.J., MENDONCA-SANTOS, M.D.L., MINASNY, B., MONTANARELLA, L., ODEH, I.O.A., SANCHEZ, P.A., THOMPSON, J.A., ZHANG, G.-L., GlobalSoilMap: toward a fine-resolution global grid of soil properties. Advances in Agronomy, pp. 93?134. 2014. ANTUNES, M. A. H., DEBIASI, P., COSTA, A. R., GLERIANI, J. M., Corre??o atmosf?rica de imagens ALOS/AVNIR-2 utilizando o modelo 6S. Revista Brasileira de Cartografia, v. 64, p. 531-539, 2012. BAGATINI, T.; GIASSON, E.; TESKE, R. Expans?o de mapas pedol?gicos para ?reas fisiograficamente semelhantes por meio de mapeamento digital de solos. Pesquisa Agropecu?ria Brasileira. v.51. 2016 BIVAND, R.; KEITT, T.; ROWLINGSON, B. rgdal: Bindings for the geospatial data abstraction library, 2017. Dispon?vel em: <https://cran.r-project.org/package=rgdal> BEHRENS, T.; FOSTER, H.; SCHOLTEN, T.; STEINRUCKEN, U.; SPIES, E. D.; GOLDSCHMITT, M. Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science 168: 21-33. 2005. BEHRENS, T., ZHU, A.-X., SCHMIDT, K., SCHOLTEN, T., Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155, 175?185. 2010. BRAGA, A. de P.; CARVALHO L. F. de; LUDEMIR., T. B. Redes Neurais Artificiais: Teoria e Aplica??es 2.ed. Rio de Janeiro: LTC ? Livros T?cnicos e Cient?ficos Editora. 226p. 2007 BRASIL. Minist?rio de Minas e Energia. Secretaria Geral. Projeto RADAMBRASIL. Folha SF. 21, Campo Grande: geologia, geomorfologia, pedologia, vegeta??o e uso potencial da terra, v. 28. Rio de Janeiro: 416p. il. 1982. BREIMAN, L., Random forests. Machine Learning 45, 5?32. 2001. BREIMAN, L.; FRIEDMAN, J.; OLSHEN, R.; STONE, C. Classification and regression trees. The Wadsworth statistics/Probability Series. Wadsworth International Group, Belmont, CA. 358p. 1984. BRUS, D. J.; KEMPEN, B.; HEUVELINK, G. B. M. Sampling for validation of digital soil maps. Eur J Soil Sci. 62:394-407. 2011. CARVALHO J?NIOR, W. de. Classifica??o supervisionada de pedopaisagens no dom?nio dos mares de morros utilizando redes neurais artificiais. Tese de doutorado. UFV, MG. 160p. 2005. CRESSIE, N., Statistics for Spatial Data. Wiley, New York, 900 pp. 1991. CHAGAS, C. da S. Mapeamento digital de solos por correla??o ambiental e redes neurais em uma bacia hidrogr?fica de dom?nio de mar de morros. Tese de doutorado. UFV, MG. 239p. 2006. CHAGAS, C. S.; FERNANDES FILHO, E. I.; VIEIRA, C. A. O.; SCHAEFER, C. E. G. R.; CARVALHO J?NIOR, W. Topographic attributes and Landsat 7 data in the digital soil mapping using neural networks. Pesquisa Agropecu?ria Brasileira 45: 497-507 (in Portuguese, with abstract in English). 2010. CHAGAS, C. S.; FERNANDES FILHO, E. I.; VIEIRA, C. A. O. Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Revista Brasileira de Ci?ncia do Solo 37: 339-351. 2013. CHASMER, L.; HOPKINSON, C.; VENESS, T.; QUINTON, W.; BALTZER, J. A decisiontree classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sensing of Environment 143: 73?84. 2014. COELHO, F. F. Compara??o de m?todos de mapeamento digital de solos atrav?s de vari?veis geomorfom?tricas e sistemas de informa??es geogr?ficas. Disserta??o de Mestrado em Sensoriamento Remoto e Meteorologia. Universidade Federal do Rio Grande do Sul. Porto Alegre, 80 f. 2010. CONGALTON, R.G., GREEN, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, New York (160 pp.). 1999. CONRAD, O., BECHTEL, B., BOCK, M., DIETRICH, H., FISCHER, E., GERLITZ, L., WEHBERG, J., WICHMANN, V., and BOEHNER, J. (2018): System for Automated Geoscientific Analyses (SAGA) v. 7.0.0. Geosci. Model Dev., 8, 1991-2007. 2018. DEUTSCH, C. V., JOUMEL, A. J. Geostatistical Software Library and User?s Guide. Oxford Univ. Press, 340 pp. 1992. DOKUCHAEV, V. V., 1883/1948/1967. Russian Chernozem, in, Selected works of V. V. Dokuchaev, Moscow, 1948, 1, 14?419. Jerusalem: Israel Program for Scientific Translations Ltd. (for USDA-NSF), Publ. by S. Monson, 1967. (Transl. into English by N.Kaner). DRAY, S., SIBERCHICOT, M. A., Package ?ADE4?. https://cran.rproject. org/web/packages/ade4/ade4.pdf. 409p. 2018 ELKAN, C. Evaluating classifiers. San Diego: University of California; 2012. EMBRAPA. Centro Nacional de Pesquisa de Solos. Mapeamento digital de classes e atributos de solos: m?todos, paradigmas e novas t?cnicas. Rio de Janeiro, RJ: Embrapa Solos. 19p. (Embrapa Solos. Documentos; n. 55) 2003. EMBRAPA. Centro Nacional de Pesquisa de Solos. Embrapa Solos e suas origens. Rio de Janeiro, RJ: Embrapa Solos. Dispon?vel no site: https://www.embrapa.br/solos/historia. (acesso 03/04/2019). EMBRAPA. Centro Nacional de Pesquisa de Solos. Levantamento de reconhecimento de baixa intensidade dos solos do munic?pio de Campo Grande - Zoneamento agroecol?gico do Estado do Mato Grosso do Sul. Rio de Janeiro: Embrapa Solos, (Embrapa Solos. Relat?rio T?cnico). 2009. EMBRAPA. Centro Nacional de Pesquisa de Solos. Levantamento de reconhecimento de baixa intensidade dos solos do munic?pio de Sidrol?ndia - Zoneamento agroecol?gico do Estado do Mato Grosso do Sul. Rio de Janeiro: Embrapa Solos, (Embrapa Solos. Relat?rio T?cnico). 2010. ESRI. Environmental Systems Research Institute Inc. Redlands. CA. Software: ArcGIS e ArcINFO ? v.10. 2010. FAVROT, J. C. A strategy for large scale soil mapping: the reference areas method. Science du Sol 27: 351-368 (in French, with abstract in English). 1989. GIASSON, E.; SARMENTO, E. C.; WEBER, E.; FLORES, C. A. e HASENACK, H. Decision trees for digital soil mapping on subtropical basaltic steeplands. Sci. Agr?., 68:167- 174, 2011. GINI, C. Variabilit? e Mutabilit?, Bologna: Universit? di Cagliari. 158p. 1912. GRINAND, C.; ARROUAYS, D.; LAROCHE, B.; MARTIN, M. P. Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma 143: 180-190, 2008. HANSEN, M. K., BROWN, D. J., DENNISON, P. E., GRAVES, S. A., BRICKLEMYER, R. S., Inductively mapping expert-derived soil-landscape units within dambo wetland catenae using multispectral and topographic data. Geoderma 150 (1?2), 72?84. http://dx.doi.org/10.1016/j.geoderma.2009.01.013. 2009. HENGL, T. E HEUVELINK, G. B. M. New Challenges for Predictive Soil Mapping. Anais. Global Workshop on Digital Soil Mapping. September 14-17, Montpellier AGROM/ INRA Campus. 2004. HENGL, T., DE JESUS, J. M., MACMILLAN, R. A., BATJES, N. H., HEUVELINK, G. B. M., RIBEIRO, E., SAMUEL-ROSA, A., KEMPEN, B., LEENAARS, J. G. B., WALSH, M. G., GONZALEZ, M. R., SoilGrids1km ? global soil information based on automated mapping. 2014. HENGL, T., HEUVELINK, G. B. M., KEMPEN, B., LEENAARS, J. G. B., TAMENE, L., TONDOH, J. E., Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. 2015. H?FIG, P.; GIASSON, E.; VENDRAME, P. R. S. Mapeamento digital de solos com base na extrapola??o de mapas entre ?reas fisiograficamente semelhantes. Pesquisa Agropecu?ria Brasileira, v.49, p.958-966, 2014. DOI: 10.1590/S0100-204X2014001200006. IBGE. Instituto Brasileiro de Geografia e Estat?stica. Mapas Tem?ticos do Brasil 1:250.000. Dispon?vel em <https://mapas.ibge.gov.br/tematicos.html>. Consulta em: 10/04/2017. INPE, Manuais; Tutorial de geoprocessamento; Opera??es aritm?ticas, dispon?vel em: http://www.dpi.inpe.br/spring/portugues/tutorial/operacao.html. Acesso em 09/09/2018b. ISAAKS, E .H., SRIVASTAVA, R. M., An Introduction to Applied Geostatistics. Oxford, New York, 561 pp. 1989. JAFARI, A., KHADEMI, H., FINKE, P. A., VAN DE WAUW, J., AYOUBI, S. Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma 232-234, 148?163. doi:10.1016/j.geoderma.2014.04.029. 2014. JENNY, H. Factors of soil formation: a system of quantitative pedology. New York: Mcgraw-Hill. 281p. 1941. JENSEN, J.R. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. S?o Jos? dos Campos: Par?ntese, p 604, 2009. JEONG, G.; OEVERDIECK, H.; PARK, S. J.; HUWE, B.; LIESS, M. Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain. Catena, v. 154, p. 73?84, 2017. JEUNE, W.; FRANCELINO, M. R.; SOUZA, E. DE; FERNANDES FILHO, E. I.; ROCHA, G. C. Multinomial logistic regression and random forest classifiers in digital mapping of soil classes in western Haiti. Revista Brasileira de Ciencia do Solo, v. 42, p. 1?20, 2018. JOURNEL, A.J., HUIJBREGTS, C.H. J. Mining Geostatistics. Academic Press. New York. 1978. KEMPEN, B; BRUS, D. J.; HEUVELINK, G. B. M.; STOORVOGEL, J. J. Updating the 1:50,000 Dutch soil map using legacy soil data: a multinomial logistic regression approach. Geoderma 151: 311-326. 2009. KINCAID, T., OLSEN, T., STEVENS, D., PLATT, C., WHITE, C.,REMINGTON, R. Spsurvey package: Spatial Survey Design and Analysis, CRAN r-project. 193p. 2014. KOMSTA, L. Package ?outliers?. https://cran.rproject. org/web/packages/outliers/outliers.pdf [20 de Fevereiro, 2015] 15p. 2015 K?PPEN, W. Climatologia: con um est?dio de los climas de la Tierra. M?xico: Fondo de Cultura Economica, 478 p. 1948. LACERDA FILHO, J. W.; BRITO, R. S.C.; SILVA, M.G.; OLIVEIRA, C.C. DE; MORETON, L.C.; MARTINS, E.G.; LOPES, R.C.; LIMA, T.M.; LARIZZATTI, J.H.; VALENTE, C.R. Geologia e Recursos Minerais do Estado de Mato Grosso do Sul. Programa Integra??o, Atualiza??o e Difus?o de Dados de Geologia do Brasil. Conv?nio CPRM/SICME-MS, MME, 121 p. 2006. LAGACHERIE, P.; LEGROS, J. P.; BURROUGH, P. A. A soil survey procedure using the knowledge on soil pattern of a previously mapped reference area. Geoderma 65: 283-301, 1995. LAGACHERIE, P. e MCBRATNEY, A. B. Spatial soil information systems and spatial soil inference systems: Perspectives for digital soil mapping. In: LAGACHERIE, P.; MCBRATNEY, A. e VOLTZ, M., eds. Digital soil mapping: An introductory perspective. Amsterdam, Elsevier, p.3-22. 2007. LASTORIA, G. Hidrogeologia da Forma??o Serra Geral no Estado de Mato Grosso do Sul. Rio Claro, 2002. 133 p. Tese (Doutoramento em Geoci?ncias e Meio Ambiente). Instituto de Geoci?ncias e Ci?ncias Exatas ? IGCE ? UNESP ? Rio Claro, 2002. LEMERCIER, B.; LACOSTE, M.; LOUM, M; WALTER, C. Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach, Geoderma, In Press, Corrected Proof. 2011. LEPSCH, I. F. Forma??o e conserva??o dos solos. S?o Paulo, Oficina de Textos, 178p. 2002. LIAW, A.; WIENER, M. Random Forest: Breiman and Cutler?s Random Forests for Classification and Regression. URL http://cran.rproject. org/web/packages/randomForest/index.html. 2009. MACHADO, F. B., NARDY, A. J. R., ROCHA-J?NIOR, E. R. V., MARQUES, L. S., and OLIVEIRA, M. A. F.: Geologia e Litogeoqu?mica da Forma??o Serra Geral nos Estados de Mato Grosso e Mato Grosso do Sul, Geoci?ncias (S?o Paulo. Impresso), 28, 523?540, 2009. MATHERON, G., The theory of regionalised variables and its applications. Les Cahicrs du centre de morphologie mathematique de Fontainbleu. Ecole Nationale Superieur des Mines de Paris. 1971. MATO GROSSO DO SUL. SECRETARIA DE PLANEJAMENTO E COORDENA??O GERAL - SEPLAN-MS. Macrozoneamento geoambiental do Estado de Mato Grosso do Sul. Campo Grande, 242 p. 1989. McBRATNEY, A.B.; MENDON?A-SANTOS, M.L.; MINASNY, B. On digital soil mapping. Geoderma, v.117, p. 3-52. 2003. MEIER, M.; SOUZA, E.; FRANCELINO, M. R.; FERNANDES FILHO, E. I.; SCHAEFER, C. E. G. Digital soil mapping using machine learning algorithms in a tropical mountainous area. Revista Brasileira de Ci?ncia do Solo, v. 42, p. 1?23, 2018. MILANI, E. J.; FRANCA, A.; SCHNEIDER, R. Bacia do Paran?. Boletim de Geoci?ncias da Petrobr?s 8 (1), 69-82. 1994. MILANI E. D.; MELO J. H. G.; SOUZA P. A.; FERNANDES L. A.; FRAN?A A. B. Bacia do Paran?. Boletim de Geoci?ncias da Petrobras, 15(2): 265-287. 2007. MINASNY, B.; McBRATNEY, A.B.; SANTOS, M. de L.M.; SANTOS H.G. dos. Revis?o sobre Fun??es de Pedotransfer?ncia (PTFs) e Novos M?todos de Predi??o de Classes de Solos e Atributos do Solo. Rio de Janeiro: Embrapa Solos, 50 p. (Embrapa Solos. Documentos n. 45). 2003. MINASNY, B. e McBRATNEY, A. B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers e Geosciences, v.32. p.1378- 1388. 2006. MINASNY, B.; McBRATNEY, A. B.; LARK, M. Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures. In: Digital Soil Mapping With Limited Data. HARTEMINK, A. E.; McBRATNEY, A. B.; MENDON?A-SANTOS, M. de L. (Eds.), Springer. p. 15-30. 2008. MINASNY, B.; MCBRATNEY, A. B. 2015. Digital soil mapping: a brief history and some lessons. Geoderma. In press. MORAN C. J.; BUI E. N.; Spatial data mining for enhanced soil map modelling. Int J Geogr Inf Sci.;16:533-49. 2002. MORA-VALLEJO, A., CLAESSENS, L., STOORVOGEL, J., HEUVELINK, G. B. M. Small scaledigital soil mapping in Southeastern Kenya. Catena, v. 76, p. 44-53. 2008. OLAYA, V. A gentle introduction to SAGA GIS. 2004. 202p. Dispon?vel em: http://downloads.sourceforge.net/saga-gis/SagaManual.pdf. Acesso em 04/07/18. PEATE, D. W.; HAWKESWORTH, C. J.; MANTOVANI, M. SM. Chemical stratigraphy of the Paran? lavas (South Am?rica): classification of magma types and their spatial distribution. Bulletin of Volcanology, Berlin, v.55, n. 1, p. 119-139. 1992. PEBESMA, E., BIVAND, R., ROWLINGSON, B., RUBIO, V. G., HIJMANS, R., SUMNER, M., MACQUENN, D., LEMON, J., O?BRIEN, J., O?ROURKE, J., Package ?SP?. https://cran.r-project.org/web/packages/sp/sp.pdf 118p. 2018 PEDREGOSA, F.; VAROQUAUX, G.; GRAMFORT, A.; MICHEL, V.; THIRION, B.; GRISEL, O.; BLONDEL, M.; PRETTENHOFER, P.; WEISS, R.; DUBOURG, V.; VANDERPLAS, J.; PASSOS, A.; COURNAPEAU, D.; BRUCHER, M.; PERROT, M.; DUCHESNAY, E. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, v. 12, p. 2825-2830, 2011. PINHEIRO, H.S.K. Mapeamento digital de solos por redes neurais artificiais da Bacia Hidrogr?fica do Rio Guapi-Macacu, RJ. Disserta??o de Mestrado. Universidade Federal Rural do Rio de Janeiro. 151p. 2012. PINHEIRO, H. S. K. M?todos de mapeamento digital aplicados na predi??o de classes e atributos dos solos da Bacia Hidrogr?fica do Rio Guapi-Macacu, RJ. Tese de Doutorado. Universidade Federal Rural do Rio de Janeiro, 180f. 2015. PINHEIRO, H. S. K.; OWENS, P. R.; ANJOS, L. H. C.; CARVALHO J?NIOR, W.; CHAGAS, C. S. Tree-based techniques to predict soil units, Soil Research 55(8) 788-798. 2017. PRONASOLOS - Programa Nacional de Solos do Brasil / Jos? Carlos Polidoro ... [et al]. ? Dados eletr?nicos. ? Rio de Janeiro: Embrapa Solos, 53 p. ? (Documentos / Embrapa Solos, ISSN 1517-2627; 183). 2016. R CORE TEAM, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2017. R CORE TEAM, Package ?FOREIGN?. https://cran.rproject. org/web/packages/foreign/foreign.pdf. 25p. 2018 RAD, M. R. P.; TOOMANIAN, N.; KHORMALI, F.; BRUNGARD, C. W.; KOMAKI, C. B.; BOGAERT, P. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, v. 232?234, p. 97?106, 2014. ROKACH, L.; MAIMON, O. Data mining with decision trees. Theory and applications. World Scientific Publishing. 2008. SAMUEL-ROSA, A.; HEUVELINK, G. B. M.; VASQUES, G. M.; ANJOS, L. H. C. Do more detailed environmental covariates deliver more accurate soil maps? Geoderma, v. 243?244, p.214?227. 2015 SANTOS, H. G., JACOMINE, P. K. T, ANJOS, L. H. C., OLIVEIRA, V. A., LUMBRERAS, J.F, COELHO, M. Z., ALMEIDA, J. A., ARAUJO FILHO, J. C., OLIVEIRA, J. B., CUNHA, T. J. F. Sistema brasileiro de classifica??o de solos. 5. ed.Bras?lia: Embrapa, 356 p. 2018. SANTOS, M. de L. M., SANTOS, H. G. dos, Mapeamento digital de classes e atributos de solos. Embrapa solos. Documentos 55. 2003. SANTOS, M. L. M., SANTOS, H. G., DART, R.de O, PARES, J. G. Mapeamento Digital de Classes de Solos no Estado do Rio de Janeiro. 28p. (Boletim de Pesquisa e Desenvolvimento 119) Dezembro, 2007 SARMENTO, E. C.; GIASSON, E.; WEBER, E. J.; FLORES, C. A.; ROSSITER, D. G.; HASENACK, H. Caracteriza??o de mapas legados de solos: uso de indicadores em mapas com diferentes escalas no Rio Grande do Sul. Revista Brasileira de Ci?ncia do Solo, v.38, p.1672-1680, 2014. DOI: 10.1590/S0100-06832014000600002. SCHMIDT, K. BEHRENS, T. SCHOLTEN, T. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma, v.146, p.138-146, 2008. SILVA, V. A., CURI, N., MARQUES, J. J. G., CARVALHO, L. M. T. D., SANTOS, W. J. R., Soil maps, field knowledge, forest inventory and ecological-economic zoning as a basis for agricultural suitability of lands in Minas Gerais elaborated in GIS. Ci?nc. Agrotecnologia 37, 538?549. 2013. SILVA, S. H. G., OWENS, P. R., DUARTE DE MENEZES, M., REIS SANTOS, W. J., CURI, N., A technique for low cost soil mapping and validation using expert knowledge on a watershed in Minas Gerais, Brazil. Soil Sci. Soc. Am. J. 78, 1310?1319. 2014. SINDAYIHEBURA, A.; OTTOY, S.; DONDEYNE, S.; MEIRVENNE, M. V.; ORSHOVEN, J.V. Comparing digital soil mapping techniques for organic carbon and claycontent: Case study in Burundi's central plateaus. Catena, v. 156, p. 161?175. 2017. SINGH, A.N. e DWIVEDI, R. S. The utility of LANDSAT imagery as an integral part of the data base for small-scale soil mapping. International Journal of Remote Sensing, v. 7, n. 9, p.1099-1108, 1986. STEYERBERG, E. W. Validation of prediction models. In: Steyerberg EW, editor. Clinical prediction models. New York: Springer p.299-311. 2009. STROBL, C.; BOULESTEIX, A. L.; ZEILEIS, A.; HOTHORN, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, v. 8, n. 1, p.25, 2007. STUMPF, F.; SCHMIDT, K.; BEHRENS, T.; SCH?NBRODT-STITT, S.; BUZZO, G.; DUMPERTH, C.; WADOUX, A.; XIANG, W.; SCHOLTEN, T. Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping. Journal of Plant Nutrition and Soil Science, v. 000, p. 1?11, 2016. TAGHIZADEH-MEHRJARDI, R., NABIOLLAHI, K., MINASNY, B., TRIANTAFILIS, J., Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma 253-254, 67?77. 2015 TEN CATEN, A. Mapeamento digital de solos: metodologias para atender a demanda por informa??o espacial em solos. Tese de Doutorado. Universidade Federal de Santa Maria. 108p. 2011. TEN CATEN, A.; DALMOLIN, R. S. D.; PEDRON, F. de A.; RUIZ, L. F. C.; SILVA, C. A. da. An apropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil. Revista Brasileira de Ci?ncia do Solo, v.37, p.359-366, 2013. DOI: 10.1590/S0100- 06832013000200007. THERNEAU, T.; ATKINSON, B.; RIPLEY, B. RPART: Recursive Partitioning and Regression Trees. (Author of initial R port) 2015-02-24. 2015. TESKE, R.; GIASSON, E.; BAGATINI, T. Compara??o de esquemas de amostragem para treinamento de modelos preditores no mapeamento digital de classes de solos, Rio Grande do Sul, Brazil. Revista Brasileira de Ci?ncia do Solo, v.39, p.14-20, 2015. USGS GLOBAL VISUALIZATION VIEWER (Usa). Frequently Asked Questions about the Landsat Missions. 2013. Dispon?vel em: <http://landsat.usgs.gov/L8_band_combos.php>. Acesso em: 22 de novembro. 2017. VERMOTE, E. F., TANRE, D., DEUZE, J. L., HERMAM, M., MORCRETTE, J. J., Second Simulation of Satellite Signal in the Solar Spectrum, 6S: An overview., IEEE Transactionson Geoscience and Remote Sensing v 35, n 3, p 675-686, 1997. WEBSTER, R., OLIVER, M. A., Statistical Methods in Soil and Land Resources Survey. Oxford Univ. Press. 1990. WERE, K.; BUI, D. T.; DICK, ?. B.; SINGH, B. R. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, v. 52, p. 394?403, 2015. YANG, R. M.; ZHANG, G. L.; LIU, F.; LU, Y. Y.; YANG, F.; YANG, F.; YANG, M.; ZHAO, Y. G.; LI, D. C. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, v. 60, p. 870?878, 2016. YANG, W.; YANG, L.; MERCHANT, J.W. An assessment of AVHRR/NDVIecoclimatological relations in Nebraska. USA. International Journal of Remote Sensing, v.10. p. 2161-2180. 1997. YIGINI, Y.; PANAGOS, P., Reference area method for mapping soil organic carbon content at regional scale. Procedia Earth and Planetary Science 10: 330-338, 2014. YOUNG, P.; PARKINSON, S.; LEES, M. Simplicity out of complexity in environmental modelling: Occam's razor revisited. Journal of Applied Statistics, 23(2-3), 165-210. 1996. |
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Biblioteca Digital de Teses e Dissertações da UFRRJ |
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Biblioteca Digital de Teses e Dissertações da UFRRJ |
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Biblioteca Digital de Teses e Dissertações da UFRRJ - Universidade Federal Rural do Rio de Janeiro (UFRRJ) |
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bibliot@ufrrj.br||bibliot@ufrrj.br |
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1800313553117249536 |