Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway

被引:78
作者
Debella-Gilo, Misganu [1 ]
Etzelmuller, Bernd [1 ]
机构
[1] Univ Oslo, Inst Geosci, N-0371 Oslo, Norway
关键词
Digital soil mapping; Terrain analysis; GIS; Logistic regression; DEM;
D O I
10.1016/j.catena.2008.12.001
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The main objectives of this study were to model the relationship between WRB-1998 soil groups and terrain attributes and predict the spatial distribution of the soil groups using digital terrain analysis and multinomial logistic regression integrated in GIS in the Vestfold County of south-eastern Norway. A digital elevation model of 25 meter grid resolution was used to derive fifteen terrain attributes. A digitized soil map of thirteen WRB soil groups at the scale of 1:25,000 was used to obtain the reference soil data for model building and validation. First, the relationships between the soil groups and the terrain attributes were modeled using multinomial logistic regression. Then. the probability that a given soil type is present at a given pixel was determined from the logit models in ARCGIS to continuously map each soil group's spatial distribution. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes correlating with the spatial distribution of the soil groups. The prediction showed higher mean probability values for each soil group in the areas actually covered by that soil group compared to other areas, indicating the reliability of the prediction. However, the prediction performed poorly for soil groups that are not greatly influenced by topography but by other factors such as human activities. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:8 / 18
页数:11
相关论文
共 36 条
[1]  
[Anonymous], 2007, OFFICE OFFICIAL PUBL
[2]  
[Anonymous], 1998, WORLD REF BAS SOIL R
[3]  
BRUS DJ, 2007, 3 APPROACHES STOCHAS, V58
[4]  
Dobos E, 2006, 22123 EUR EN, P68
[5]   Multinomial goodness-of-fit tests for logistic regression models [J].
Fagerland, Morten W. ;
Hosmer, David W. ;
Bofin, Anna M. .
STATISTICS IN MEDICINE, 2008, 27 (21) :4238-4253
[6]  
Gallant J.C., 2000, TERRAIN ANAL PRINCIP, P51
[7]   A goodness-of-fit test for multinomial logistic regression [J].
Goeman, Jelle J. ;
le Cessie, Saskia .
BIOMETRICS, 2006, 62 (04) :980-985
[8]   Soil Map Density and a Nation's Wealth and Income [J].
Hartemink, Alfred E. .
DIGITAL SOIL MAPPING WITH LIMITED DATA, 2008, :53-66
[9]   Methods to interpolate soil categorical variables from profile observations: Lessons from Iran [J].
Hengl, Tomislav ;
Toomanian, Norair ;
Reuter, Hannes I. ;
Malakouti, Mohammad J. .
GEODERMA, 2007, 140 (04) :417-427
[10]   Goodness-of-fit processes for logistic regression: simulation results [J].
Hosmer, DW ;
Hjort, NL .
STATISTICS IN MEDICINE, 2002, 21 (18) :2723-2738