Ghent University Academic Bibliography

Advanced

Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types

Azam Jafarisirizi, Peter Finke UGent, Johan Van de Wauw, Shamsollah Ayoubi and Hossein Khademi (2012) EUROPEAN JOURNAL OF SOIL SCIENCE. 63(2). p.284-298
abstract
The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy. The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
digital soil mapping, geostatistics, GEOMORPHOLOGY, VALIDATION, MODELS, VALLEY, MAP
journal title
EUROPEAN JOURNAL OF SOIL SCIENCE
Eur. J. Soil Sci.
volume
63
issue
2
pages
284 - 298
Web of Science type
Article
Web of Science id
000301534700014
JCR category
SOIL SCIENCE
JCR impact factor
2.651 (2012)
JCR rank
3/34 (2012)
JCR quartile
1 (2012)
ISSN
1351-0754
DOI
10.1111/j.1365-2389.2012.01425.x
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2057864
handle
http://hdl.handle.net/1854/LU-2057864
date created
2012-03-03 09:05:22
date last changed
2016-12-19 15:45:16
@article{2057864,
  abstract     = {The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy.
The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.},
  author       = {Jafarisirizi, Azam and Finke, Peter and Van de Wauw, Johan and Ayoubi, Shamsollah and Khademi, Hossein},
  issn         = {1351-0754},
  journal      = {EUROPEAN JOURNAL OF SOIL SCIENCE},
  keyword      = {digital soil mapping,geostatistics,GEOMORPHOLOGY,VALIDATION,MODELS,VALLEY,MAP},
  language     = {eng},
  number       = {2},
  pages        = {284--298},
  title        = {Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types},
  url          = {http://dx.doi.org/10.1111/j.1365-2389.2012.01425.x},
  volume       = {63},
  year         = {2012},
}

Chicago
Jafarisirizi, Azam, Peter Finke, Johan Van de Wauw, Shamsollah Ayoubi, and Hossein Khademi. 2012. “Spatial Prediction of USDA-great Soil Groups in the Arid Zarand Region, Iran: Comparing Logistic Regression Approaches to Predict Diagnostic Horizons and Soil Types.” European Journal of Soil Science 63 (2): 284–298.
APA
Jafarisirizi, A., Finke, P., Van de Wauw, J., Ayoubi, S., & Khademi, H. (2012). Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. EUROPEAN JOURNAL OF SOIL SCIENCE, 63(2), 284–298.
Vancouver
1.
Jafarisirizi A, Finke P, Van de Wauw J, Ayoubi S, Khademi H. Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. EUROPEAN JOURNAL OF SOIL SCIENCE. 2012;63(2):284–98.
MLA
Jafarisirizi, Azam, Peter Finke, Johan Van de Wauw, et al. “Spatial Prediction of USDA-great Soil Groups in the Arid Zarand Region, Iran: Comparing Logistic Regression Approaches to Predict Diagnostic Horizons and Soil Types.” EUROPEAN JOURNAL OF SOIL SCIENCE 63.2 (2012): 284–298. Print.