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Disaggregation of conventional soil map by generating multi realizations of soil class distribution (case study: Saadat Shahr plain, Iran)

  • M. Jamshidi
  • M. A. DelavarEmail author
  • R. Taghizadehe-Mehrjerdi
  • C. Brungard
Article
  • 41 Downloads

Abstract

Conventional soil maps generally depict information about soil spatial distribution in the framework of crisp boundaries of tessellated soil polygons. Such maps in standard soil survey procedures determine map unit composition on the basis of relative acreage occupied by individual major and minor soil components within soil map unit without addressing the specific location of each component in the polygon boundary. These limitations in addition to the sharp-edge boundaries of conventional soil maps are considered obstacles for modern land resource management. To increase detail in the polygon of conventional soil maps, we have produced a spatially disaggregated soil class map of a relatively flat agricultural plain called Saadat Shahr in South-Central Iran, using DSMART algorithm. DSMART is a known DSM-based disaggregation and harmonization algorithm that works through resampled classification trees to estimate the probability of the existence of each possible soil classes and also to prepare the maps of the most probable soil class, second most probable, and so on in raster format. The conventional soil map and 124 georeferenced profiles, as well as a set of numerical and categorical auxiliary data in 10-m resolutions in the extent of the study area utilized as the SCORPAN variables, were used as the inputs of the DSMART algorithm. A set of 78 independent sampling points generated by Latin hypercube sampling scheme were investigated and then used for validation of the DSMART raster outputs. The results indicated an improvement in disaggregated maps in the case of allocating soil components within the map units. In the generated DSMART, overall accuracy for seven soil subgroups was 68%. The best prediction obtained for Typic Xerorthents and Typic Calcixerepts, meanwhile a few classes were poorly predicted. For second most probable and third most probable maps, 17% and 0.5% of predicted soils match that observed respectively. This study revealed that DSMART as a disaggregation method can be used for enhancing existence soil map with poor descriptive data in the case of allocating soil classes in a more detailed way compared to the relevant original map.

Keywords

Classification trees Disaggregation DSMART, Environmental variables SCORPAN 

Notes

Acknowledgments

The authors gratefully thank the support of the University of Zanjan (ZNU).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Soil Science Department, College of AgricultureUniversity of Zanjan and Scientific Staff of Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO)TehranIran
  2. 2.Soil Science Department, College of AgricultureUniversity of ZanjanZanjanIran
  3. 3.Faculty of Agriculture and Natural ResourcesUniversity of ArdakanArdakanIran
  4. 4.Department of Plant and Environmental SciencesNew Mexico State UniversityLas CrucesUSA

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