Disaggregation of conventional soil map by generating multi realizations of soil class distribution (case study: Saadat Shahr plain, Iran)
- 41 Downloads
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.
KeywordsClassification trees Disaggregation DSMART, Environmental variables SCORPAN
The authors gratefully thank the support of the University of Zanjan (ZNU).
- Chaney, N., Hempel, J. W., Odgers, N.P., McBratney, A. B., & Wood, E. F. (2014). Spatial disaggregation and harmonization of gSSURGO. In ASA, CSSA and SSSA international annual meeting, Long Beach. ASA, CSSA and SSSA.Google Scholar
- Ditzler, C., Scheffe, K., & Monger, H. C. (2017). Soil Survey Manual, USDA Handbook 18, Soil Science Division Staff.Google Scholar
- Emberger, L. (1963). Bioclimatic map of the Mediterranean zone: explanatory notes (Vol. 21). UNESCO-FAO.Google Scholar
- Gallant, J. P., & Wilson, J. C. (2000). Terrain analysis: principles and applications. Hoboken: Wiley.Google Scholar
- Hassanshahi, H. (1991). Semi detailed soil survey of Saadat Shar, Seidan, Sivand and Arsenjan plains in Fars Province. Tehran: Soil and Water Research Institute of Iran. No: 838 115 p. (In Persian).Google Scholar
- Hengl, T. (2009). A practical guide to geostatistical mapping (Vol. 52). Amsterdam: Office for Official Publications of the European Communities.Google Scholar
- Holmes, K. W., Odgers, N. P., Griffin, E. A., & van Gool, D. (2014). Spatial disaggregation of conventional soil mapping across Western Australia using DSMART. Global Soil Map: Basis of the global spatial soil information system. London: Taylor& Francis, 273–279.Google Scholar
- Lagacherie, P., Arrouays, D., & Walter, C. (2013). Cartographie numérique des sols: Principe, mise en œuvre et potentialités. Etude et Gestion des Sols, 20(1), 83–98.Google Scholar
- Mahler, P. J. (1970). Manual of multipurpose land classification, Report no. 212. Tehran: Soil and Water Research Institute.Google Scholar
- Minasny, B., McBratney, A. B., Malone, B. P., & Wheeler, I. (2013). Digital mapping of soil carbon. In Advances in Agronomy 118, 1–47.Google Scholar
- National Cartographic Center of Iran. (1998). 1:25,000 digital topographic maps, Tehran, Iran. http://www.ngdir.ir/. Accessed 1998.
- Qi, J., Kerr, Y., & Chehbouni, A. (1994). External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 723-730.Google Scholar
- Quinlan‚ J. R. (2003). C5.0 Online Tutorial. Quinlan‚ J. R. C5.0 Online Tutorial. https://www.rulequest.com.
- R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing Internet: http://www.R-project.org. Accessed 2012.
- Rudiyanto Minasny, B., Setiawan, B. I., Arif, C., Saptomo, S. K., & Chadirin, Y. (2016). Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands. Geoderma, 117(1–2), 3–52.Google Scholar
- SAGA, G, 2013. System for automated geoscientific analyses. Available at: www.saga-gis.org/en/index.html. Accessed 2013.
- Soil Survey Staff. (2014). Keys to Soil Taxonomy (12th ed.). Washington DC: USDA-Natural Resources Conservation Service.Google Scholar
- Story, M., & Congalton, R. G. (1986). Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397–399.Google Scholar
- Wei, S., McBratney, A., Hempel, J., Minasny, B., Malone, B., D’Avello, T., Burras, L., & Thompson, J. (2010). Digital harmonisation of adjacent analogue soil survey areas–4 Iowa counties. In Proceedings World Congress of Soil Science: Soil solutions for a changing world, 19th, Brisbane, Australia (pp. 1–6).Google Scholar