Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/873578
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dc.contributor.authorDART, R. de O.eng
dc.contributor.authorCOELHO, M. R.eng
dc.contributor.authorMENDONÇA-SANTOS, M. de L.eng
dc.contributor.authorPARES, J. G.eng
dc.contributor.authorBERBARA, R. L. L.eng
dc.date.accessioned2020-03-05T18:11:39Z-
dc.date.available2020-03-05T18:11:39Z-
dc.date.created2011-01-19
dc.date.issued2010
dc.identifier.citationIn: INTERNATIONAL WORKSHOP ON DIGITAL SOIL MAPPING, 4., 2010, Rome. From digital soil mapping to digital soil assessment: identifying key gaps from fields to continents: proceedings... Rome: IUSS, 2010.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/873578-
dc.descriptionThe use of Digital Soil Mapping (DSM) to predict soil classes is an important issue to decrease costs and subjectivity of soil maps. The main objective of this study was to use DSM to produce soil maps of a relatively small area (about 100 km2) and compare it to a preliminary soil map made by traditional techniques. The study area is located at north of Minas Gerais State, southwest of Brazil. In this study we used decision tree classifier, See5, and 278 soil samples to predict soil class at order level of the Brazilian System of Soil Classification. We also did use ancillary data as Landsat ratios and variables of the topography. DSM didn?t show a good performance of soil prediction because basically three factors: (a) taxonomic similarity between Argissolos and Latossolos, (b) great spatial and attributes variability of Cambissolos that occurred in different landscapes types, and (c) low accuracy of soil prediction to Gleissolos, Neossolos and Cambissolos of the river plain domain because its shows great environment complexity. Following works will make a better selection of environmental covariates, predict the soil classes in higher categorical level and assessment of quality of digital soil maps.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectDigital Soil Mappingeng
dc.subjectSoil classeseng
dc.subjectTraditional techniqueseng
dc.titleDigital soil mapping at Parque Estadual da Mata Seca, Minas Gerais state, Brazil: applying regression tree to predict soil classes.eng
dc.typeArtigo em anais e proceedingseng
dc.date.updated2020-03-05T18:11:39Z
riaa.ainfo.id873578eng
riaa.ainfo.lastupdate2020-03-05
dc.contributor.institutionRICARDO DE OLIVEIRA DART, CNPSeng
dc.contributor.institutionMAURICIO RIZZATO COELHO, CNPSeng
dc.contributor.institutionMARIA DE LOURDES MENDONÇA SANTOS BREFIN, CNPSeng
dc.contributor.institutionJ. G. PARESeng
dc.contributor.institutionRICARDO LUIZ LOURO BERBARA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO.eng
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