Maghsodi, Z., Rostaminia, M., Faramarzi, M., Keshavarzi, A., Rahmani, A. (2019). Spatial Prediction of Soil Units Using Geographic Information Systems in Sivan Lands of Ilam Province. , 33(2), 253-267. doi: 10.22092/ijsr.2019.119766
Z. Maghsodi; M. Rostaminia; M. Faramarzi; A. Keshavarzi; A. Rahmani. "Spatial Prediction of Soil Units Using Geographic Information Systems in Sivan Lands of Ilam Province". , 33, 2, 2019, 253-267. doi: 10.22092/ijsr.2019.119766
Maghsodi, Z., Rostaminia, M., Faramarzi, M., Keshavarzi, A., Rahmani, A. (2019). 'Spatial Prediction of Soil Units Using Geographic Information Systems in Sivan Lands of Ilam Province', , 33(2), pp. 253-267. doi: 10.22092/ijsr.2019.119766
Maghsodi, Z., Rostaminia, M., Faramarzi, M., Keshavarzi, A., Rahmani, A. Spatial Prediction of Soil Units Using Geographic Information Systems in Sivan Lands of Ilam Province. , 2019; 33(2): 253-267. doi: 10.22092/ijsr.2019.119766

Spatial Prediction of Soil Units Using Geographic Information Systems in Sivan Lands of Ilam Province

پژوهش های خاک
Article 11, Volume 33, Issue 2, September 2019, Pages 253-267    PDF (856.58 K)
Document Type: Research Paper
DOI: 10.22092/ijsr.2019.119766
Authors
Z. Maghsodi1; M. Rostaminia* 2; M. Faramarzi3; A. Keshavarzi4; A. Rahmani5
1MSc Student, Water and Soil Engineering Group, Faculty of Agricultural, Ilam University
2Assistant Professor, Water and Soil Engineering Group, College of Agriculture, Ilam University
3Associate Professor, Rangeland and Watershed Group, College of Agriculture, Ilam University
4Assistant Professor, Soil Science and Engineering Department, College of Agriculture, Tehran University
5PhD Student, Soil Science and Engineering, Tehran University
Abstract
Limitations in conventional soil identification methods and the advances made in information technology in soil science have attracted more attention to new approaches to soil mapping in order to improve the purity of soil maps. The present study was carried out in a part of Ilam province to identify and prepare soil maps of this region. At first, 46 profiles were identified. Then, based on the morphological characteristics of each profile, soil samples were taken from all genetic horizons and analyzed for chemical and physical properties. Then, the soils were classified based on the Soil Survey Staff keys (2014). A multinomial logistic regression model was used for spatial prediction of soil taxonomic classes. The geomorphometric features were extracted from digital elevation model with a resolution of 30 m2 by SAGAGIS2.2 software. The classification results of each soil control profile in the studied area showed that, in general, the soils were in three order categories: Mollisols, Inceptisols, Entisols, and six classes at the family level. The correlation between the features of digital elevation model showed that the parameters of the mid slope position, spatial solar radiation, index of moisture content, ground roughness index, surface curvature, and profile curvature had the most effect on the formation of soil family classes. The overall accuracy and Kappa index of spatial prediction map from the regression model was 60% and 0.38 at the familial level, respectively. Finally, the results of this study showed that geomorphometric variables had a significant influence on the prediction of soil classes. Therefore, it is suggested that in future studies, other covariates derived from remote sensing data should also be used to improve the quality and accuracy of soil maps.
Keywords
Digital Soil Mapping; Multinomial logistic regression method; Data mining; Covariate
References
  1. افشار، ف.، ایوبی، ش.، جعفری، ا. (1395). نقشه‌برداری رقومی کلاس‌های خاک با استفاده از نقشه خاک قدیمی در منطقه خشک جنوب شرق ایران. نشریه علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی).21 (1): 253-239.
  2. بنایی، م. (1377). نقشه رژیم های رطوبتی و حرارتی ایران. مؤسسه تحقیقات خاک و آب کشور ایران.
  3. پهلوان‌راد، م. خرمالی، ف. تومانیان، ن. کیانی، ف. کمکی، ب. (1393). پهنه‌بندی رقومی واحدهای خاک با استفاده از مدل درختان تصمیم‌گیری تصادفی در استان گلستان.مجله پژوهش‌های حفاظت آب و خاک. 21(6): 93-73.
  4. تقی‌زاده مهرجردی، ر.، سرمدیان، ف.، امید، م.، تومانیان، ن.، روستا، م.، رحیمیان، م. (1393). نقشه‌برداری رقومی کلاس‌های خاک با استفاده از انواع روش‌های داده‌کاوی در منطقه اردکان یزد. مهندسی زراعی (مجله علمی کشاورزی). 37 (2): 115-101.
  5. جعفری، ا.، ایوبی، ش.، خادمی، ح. (1390). کاربرد مدل‌های رگرسیونی در پیش‌بینی کلاس خاک در بخشی از مناطق ایران مرکزی (مطالعه موردی منطقه زرند کرمان). نشریه آب و خاک (علوم و صنایع کشاورزی).25 شماره 6. 1364-1353.
  6. فاتحی، ش.، محمدی، ج.، صالحی، م.، مومنی، ع.، تومانیان، ت.، جعفری، ا. (1394). انبوهش‌زدائی مکانی نقشه‌ی سنتی خاک با استفاده از رگرسیون لاجیستیک چند کلاسه و درختان طبقه‌بندی (مطالعه موردی: زیر حوضه آبخیز مرک در استان کرمانشاه). چهاردهمین کنگره علوم خاک.
  7. Afshar, F. A., Ayoubi, S., & Jafari, A. (2018). The extrapolation of soil great groups using multinomial logistic regression at regional scale in arid regions of Iran. Geoderma, 315, 36-48.
  8. Behrens, T., Zhu, A. X., Schmidt, K., & Scholten, T. (2010). Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma, 155(3-4), 175-185.
  9. Böhner, J., Koethe, R., Conrad, O., Gross, J., Ringeler, A., & Selige, T. (2001). Soil regionalisation by means of terrain analysis and process parameterisation. Soil classification, 2003.
  10. Brus, D. J., Kempen, B., & Heuvelink, G. B. M. (2011). Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3), 394-407.
  11. Caten, A. T., Dalmolin, R. S. D., PEDRON, F. D. A., & MENDONÇA-SANTOS, M. D. L. (2011). Extrapolação das relações solo-paisagem a partir de uma área de referência. Embrapa Solos-Artigo em periódico indexado (ALICE).
  12. Debella-Gilo, M. (2007). The Application of Digital Terrain Analysis for Digital Soil Mapping: Examples from Vestfold County, South-Eastern Norway (Master's thesis).
  13. Debella-Gilo, M., & Etzelmüller, B. (2009). Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. Catena, 77(1), 8-18.
  14. Dietrich, H., & Böhner, J. (2008). Cold air production and flow in a low mountain range landscape in Hessia (Germany). Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, 19, 37-48.
  15. Giasson, E., Figueiredo, S. R., Tornquist, C. G., & Clarke, R. T. (2008). Digital soil mapping using logistic regression on terrain parameters for several ecological regions in Southern Brazil. In Digital soil mapping with limited data (pp. 225-232). Springer, Dordrecht.
  16. Guo, zh., Adhikari,A., Chellasamy,M.,Grevec,M.B., Owens,R.,Greve,M.(2019), Selection of terrain attributes and its scale dependency on soil organic carbon prediction.Geoderma, 340:303–312.
  17. Jafari, A., Ayoubi, S., Khademi, H., Finke, P. A., & Toomanian, N. (2013). Selection of a taxonomic level for soil mapping using diversity and map purity indices: a case study from an Iranian arid region. Geomorphology, 201, 86-97.
  18. Jafari, A., Finke, P. A., Vande 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.
  19. Jeune, W., Francelino, M. R., de Souza, E., & Inácio, E. (2018). Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti. Rev Bras Cienc Solo, 42, e0170133.
  20. McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1), 3-52.
  21. Ohlmacher, G. C., & Davis, J. C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA.Engineering Geology, 69(3), 331-343.
  22. Pahlavan-Rad, M. R., Khormali, F., Toomanian, N., Brungard, C., Kiani, F., Komaki., CH, Bogaert., P. (2016). Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran. Geoderma, 279, 141–148.
  23. Roozitalab, M. H., Siadat, H., & Farshad, A. (Eds.). (2018). The Soils of Iran. Springer International Publishing.
  24. Schaetzl, R. J., & Anderson, S. (2005). Soils: Genesis and geomorphology. Cambridge Univ. Press, New York. Soils: Genesis and geomorphology. Cambridge Univ. Press, New York.
  25. Schoeneberger, P. J. (2012). Field book for describing and sampling soils. Government Printing Office.
  26. Soil Survey Staff, (2014). Keys to soil taxonomy. 12th edn. USDA Natural Resources Conservation Service, Washington, DC.
  27. Taghizadeh-Mehrjardi, R., Nabiollahi, K., Minasny, B., & Triantafilis, J. (2015). Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma, 253, 67-77.
  28. Vaysse, K., & Lagacherie, P. (2015). Evaluating digital soil mapping approaches for mapping Global Soil Map soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Regional, 4, 20-30.
  29. Wilson, J.(2018). Environmental applications of digital terrain modeling. John Wiley & Sons. 359pp. 
Statistics
Article View: 570
PDF Download: 488


counter
Journal management system. designed by sinaweb