MineraÃ§Ã£o de dados para inferÃªncia da relaÃ§Ã£o solo ... - IAC

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CRIVELENTI, Rafael Castro. Data mining about to inference from relation soil -landscape in digital mapping soil. Dissertação (Mestrado em Gestão de RecursosAgroambientais) – Pós-Graduação – IAC.ABSTRACTDigital soil mapping can be a feasible option for less expensive and less timeconsumingsoil surveys. This research aims to develop a digital soil mapping techniqueusing relief descriptors as well as geologic mapping units in order to produce digital soilmaps of two topographic sheets to the scale of 1:50.000, Sao Pedro (SF-23-Y-A-IV-1) eDois Corregos (SF-22-Z-B-III-3). A digital database was built with topographic,geological and soil survey sheets. A digital elevation model (DEM), constructed fromthe 1:50.000 topo sheets, was used to create the relief morphometrical parameters slopegradient, slope profile, contour profile, basin contribution area, and diagonal distance tothe drainage of the two study areas. These parameters together with the geological mapwere georeferred, rasterized, and spatially associated to the existing 1:100.000-scale soilmaps, what allowed building a matrix of landscape parameters and soil mapping units.These matrixes were used in the Weka machine-learning environment to generate aprediction model by means of decision trees. The rules developed by the models wereapplied in geographical information systems to produce digital soil maps. Geology wasthe element with the greatest ability to predict soil mapping units, followed by slopegradient, at the Sao Pedro sheet, and by diagonal distance to the drainage, at the DoisCorregos sheet. Decision trees performed well to produce the models, with a spatialaccuracy of 55 % for the Sao Pedro and 61 % for the Dois Corregos sheets. Balancingclasses for training the data increased precision of mapping units with the leastextensive occurrences, associated to little reduction in the precision of those that occurin greater extensions, what improved usefulness of the produced digital soil maps.XIII
Keywords: soil survey, relief descriptors, decision trees.XIV
Page 1 and 2: INSTITUTO AGRONÔMICOCURSO DE PÓS-
Page 4 and 5: Primeiramente a Deus, pela vida,Ao
Page 6 and 7: - A toda turma do geoprocessamento
Page 8 and 9: ÍNDICE DE TABELASTabela 1 - Distri
Page 10 and 11: ÍNDICE DE FIGURASFigura 1 - Repres
Page 12 and 13: CRIVELENTI, Rafael Castro. Mineraç
Page 16 and 17: 1 INTRODUÇÃOA distribuição espa
Page 18 and 19: 2 REVISÃO DE LITERATURA2.1 Histór
Page 20 and 21: 2.2 Levantamento pedológico por m
Page 22 and 23: corretivas ou compensatórias de ca
Page 24 and 25: Nesta perspectiva, é oportuno dese
Page 28 and 29: irregulares (MONTGOMERY, 2003). As
Page 30 and 31: o objetivo de diminuir o erro por e
Page 32 and 33: Recentemente, SIRTOLI et al. (2008)
Page 34 and 35: intermediário, é a unidade de tom
Page 36 and 37: −∑H x / y)= p .ln( p / p )[3](i
Page 38 and 39: Estes sistemas manipulam dados geor
Page 40 and 41: 2.9 Comparação entre resultados d
Page 42 and 43: mapeamento, considerando as classes
Page 44 and 45: correlacionaram com a distribuiçã
Page 46 and 47: 3 MATERIAL E MÉTODOS3.1 Descriçã
Page 48 and 49: (a)120Extrato do Balanço Hídrico
Page 50 and 51: (a)(b)Figura 5 - Mapas de geologia
Page 52 and 53: Conforme PONÇANO (1981), o ambient
Page 54 and 55: h) Cambissolo: Solos constituídos
Page 56 and 57: 1:1.000.000 (IPT, 1981), e evidenci
Page 58 and 59: ) Curvatura em perfil (VALERIANO, 2
Page 60 and 61: dados estratificados retirados ante
Page 62 and 63: 4 RESULTADOS E DISCUSSÃO4.1 MDE, G
Page 64 and 65:
(a)(b)(c)Figura 7 - Mapas de variá
Page 66 and 67:
(a)(b)(c)Figura 8 - Mapas de variá
Page 68 and 69:
4.2 Análise dos dados e elaboraç
Page 70 and 71:
BalanceamentosFigura 10 - Distribui
Page 72 and 73:
ocorrência deixe de ser amostrada
Page 74 and 75:
unidades de mapeamento de solos fos
Page 76 and 77:
devido a sua grande representativid
Page 78 and 79:
Tabela 12 - Atualização da legend
Page 80 and 81:
A análise do mapa permite verifica
Page 82 and 83:
Tabela 15 - Ordenamento das variáv
Page 84 and 85:
A partir da sobreposição dos mapa
Page 86 and 87:
Tabela 16 - Matiz de confusão das
Page 88 and 89:
Tabela 17 - Porcentagem de acerto e
Page 90 and 91:
Como pode ser observado na figura 1
Page 92 and 93:
unidades de mapeamento. Por exemplo
Page 94 and 95:
O latossolo vermelho amarelo textur
Page 96 and 97:
mapeamento de solos fosse classific
Page 98 and 99:
predominância de latossolos (64 %)
Page 100 and 101:
um banco de dados digitais para ess
Page 102 and 103:
6 SUGESTÕESComo trabalhos futuros
Page 104 and 105:
mapa de solos na região de Três P
Page 106 and 107:
COSTA CABRAL, M. & BURGES, S.J. Dig
Page 108 and 109:
IBGE - INSTITUTO BRASILEIRO DE GEOG
Page 110 and 111:
McBRATNEY, A.B.; MENDONÇA SANTOS,
Page 112 and 113:
PISSARA, T.C.T.; POLITANO, W.; FERR
Page 114 and 115:
TURCOTTE, R.; FORTIN, J.P.; ROUSSEA
Page 116 and 117:
8 ANEXO(S)Anexo I………………
Page 118 and 119:
65,9 0,9 3,6 6,8 LVdf text. argilos
Page 120 and 121:
Balanceamento de classes = 1a b c d
Page 122:
Anexo VI - Matriz de confusão para
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MineraÃ§Ã£o de dados para inferÃªncia da relaÃ§Ã£o solo ... - IAC

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