Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil
Autor(a) principal: | |
---|---|
Data de Publicação: | 2006 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162006000300008 |
Resumo: | Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution. |
id |
USP-18_439b67f64ea8a2db2323dad626aa9085 |
---|---|
oai_identifier_str |
oai:scielo:S0103-90162006000300008 |
network_acronym_str |
USP-18 |
network_name_str |
Scientia Agrícola (Online) |
repository_id_str |
|
spelling |
Digital soil mapping using multiple logistic regression on terrain parameters in southern BrazilGISDEMsoil surveyterrain analysisSoil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.Escola Superior de Agricultura "Luiz de Queiroz"2006-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162006000300008Scientia Agricola v.63 n.3 2006reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162006000300008info:eu-repo/semantics/openAccessGiasson,ElvioClarke,Robin ThomasInda Junior,Alberto VasconcellosMerten,Gustavo HenriqueTornquist,Carlos Gustavoeng2006-06-26T00:00:00Zoai:scielo:S0103-90162006000300008Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2006-06-26T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
title |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
spellingShingle |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil Giasson,Elvio GIS DEM soil survey terrain analysis |
title_short |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
title_full |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
title_fullStr |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
title_full_unstemmed |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
title_sort |
Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil |
author |
Giasson,Elvio |
author_facet |
Giasson,Elvio Clarke,Robin Thomas Inda Junior,Alberto Vasconcellos Merten,Gustavo Henrique Tornquist,Carlos Gustavo |
author_role |
author |
author2 |
Clarke,Robin Thomas Inda Junior,Alberto Vasconcellos Merten,Gustavo Henrique Tornquist,Carlos Gustavo |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Giasson,Elvio Clarke,Robin Thomas Inda Junior,Alberto Vasconcellos Merten,Gustavo Henrique Tornquist,Carlos Gustavo |
dc.subject.por.fl_str_mv |
GIS DEM soil survey terrain analysis |
topic |
GIS DEM soil survey terrain analysis |
description |
Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162006000300008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162006000300008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162006000300008 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.63 n.3 2006 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936460134776832 |