TY - JOUR
T1 - Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil
AU - Giasson, Elvio
AU - Clarke, Robin Thomas
AU - Inda, Alberto Vasconcellos
AU - Merten, Gustavo Henrique
AU - Tornquist, Carlos Gustavo
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - DEM
KW - GIS
KW - Soil survey
KW - Terrain analysis
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U2 - 10.1590/S0103-90162006000300008
DO - 10.1590/S0103-90162006000300008
M3 - Article
AN - SCOPUS:33745929335
SN - 0103-9016
VL - 63
SP - 262
EP - 268
JO - Scientia Agricola
JF - Scientia Agricola
IS - 3
ER -