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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
19/01/2011 |
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Data da última atualização: |
13/03/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
DART, R. de O.; COELHO, M. R.; MENDONÇA-SANTOS, M. de L.; PARES, J. G.; BERBARA, R. L. L. |
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Afiliação: |
RICARDO DE OLIVEIRA DART, CNPS; MAURICIO RIZZATO COELHO, CNPS; MARIA DE LOURDES MENDONÇA SANTOS BREFIN, CNPS; J. G. PARES; RICARDO LUIZ LOURO BERBARA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO. |
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Título: |
Digital soil mapping at Parque Estadual da Mata Seca, Minas Gerais state, Brazil: applying regression tree to predict soil classes. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: 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. |
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Idioma: |
Inglês |
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Conteúdo: |
The 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. |
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Palavras-Chave: |
Digital Soil Mapping; Soil classes; Traditional techniques. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/25741/1/Dart-et-al.pdf
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Marc: |
LEADER 02020nam a2200193 a 4500
001 1873578
005 2023-03-13
008 2010 bl uuuu u00u1 u #d
100 1 $aDART, R. de O.
245 $aDigital soil mapping at Parque Estadual da Mata Seca, Minas Gerais state, Brazil$bapplying regression tree to predict soil classes.$h[electronic resource]
260 $aIn: INTERNATIONAL WORKSHOP ON DIGITAL SOIL MAPPING, 4., 2010, Rome. From digital soil mapping to digital soil assessment: identifying key gaps from fields to continents: proceedings... Rome: IUSS$c2010
520 $aThe 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.
653 $aDigital Soil Mapping
653 $aSoil classes
653 $aTraditional techniques
700 1 $aCOELHO, M. R.
700 1 $aMENDONÇA-SANTOS, M. de L.
700 1 $aPARES, J. G.
700 1 $aBERBARA, R. L. L.
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Embrapa Solos (CNPS) |
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| 1. |  | DART, R. de O.; COELHO, M. R.; MENDONÇA-SANTOS, M. de L.; PARES, J. G.; BERBARA, R. L. L. Digital soil mapping at Parque Estadual da Mata Seca, Minas Gerais state, Brazil: applying regression tree to predict soil classes. In: 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.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Solos. |
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