Soil Distribution Pattern Analysis in a Low Relief Area Using Decision Trees Algorithm

Document Type : Research Paper

Authors

1 Department of Soil Science and Engineering-Faculty of Agriculture -RAZI UNIVERCITY-KERMANSHAH-IRAN

2 Department of Soil Science and Engineering, Faculty of Agriculture , RAZI Univercity, Kermanshah, Iran

Abstract

Digital soil mapping (DSM) can be defined as a production of spatial soil information. Decision tree (DT) algorithm is one of the most popular machine learning methods which was applied in several recent DSM studies. This study was carried out to evaluate the capability of DT in mapping soils in Miandarband region with area of 50,000 ha in Kermanshah province. The C5.0 decision tree algorithm (with and without boosting meta-algorithm) used to establish spatial relationships between known soil taxonomic classes and environmental variables. Using simple systematic sampling, 78 pedons were studied and 6 great groups and 14 subgroups of Soil Taxonomy (ST) were identified. Thirty environmental items were derived from a digital elevation model (DEM) file and a landsat-8 OLI/TIRS (July/Tir 1394) image of the area. Predictions made by C5.0 algorithm showed OA values of 73 percent for great group and subgroup, while comparable values for Kappa Index were 0.61 and 0.63, respectively. Combination of boosting meta-algorithm with C5.0 increased OA values for ST categories 0.80 and 0.76 and Kappa Index values to 72 percent and 66 percent. Results showed a considerable capability for DT in recognition of soil pattern over the study area and the topographic variables seems to be most important. Also, analysis of the produced maps, compared with the observed soil pattern during the field survey, revealed a reasonable agreement of decision tree algorithm predictions with reality.

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Main Subjects


Adu-Poku, S. (2012). Comparing classification algorithms in data mining. MSc. dissertation. Central Connecticut State University.
Banaii, M. H. 1977. Soil moisture and temperature regimes map of Iran. Soil and Water research Institute of Iran. Ministry of Agriculture, Tehran, Iran. (In Farsi).
Barthold, F. K., Wiesmeier, M., Breuer, L., Frede, H. G., Wu, J., and Blank, F. B. (2013). Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia. Journal of arid environments, 88, 194-205.
Boettinger, J. L., Ramsey, R. D., Bodily, J. M., Cole, N. J., Kienast-Brown, S., Nield, S. J., and Stum, A. K. (2008) Digital soil mapping with limited data. In A. E. Hartemink., A. B. McBratney., and de Lourdes Mendonça-Santos, M. (Ed,). Landsat spectral data for digital soil mapping. (Vol. 16). (pp. 193-202). Springer Science and Business Media.
Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., Edwards Jr., T. C., (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239–240, 68–83.
Bui, E. N., Henderson, B. L. and Viergever, K. (2006). Knowledge discovery from models of soil properties developed through data mining. Ecological Modelling, 191(3-4), 431-446.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46.
Cole, N. J. and Boettinger, J. L. (2006). . Developments in Soil Science. In P. Lagacherie, A.B. McBratney, M. Voltz (Ed.), Pedogenic Understanding Raster Classification Method for Mapping Soils, Powder River Basin, Wyoming, USA. Developments in Soil Science, (Vol. 31). (pp. 377-388).
Congalton, R.G. (1991). A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
Cook, S. E., Jarvis, A., and Gonzalez, J. P. (2008) Digital Soil Mapping with Limited Data. In A. E. Hartemink., A. B. McBratney., de Lourdes Mendonça-Santos. M., (Ed.), A new global demand for digital soil information. (Vol. 3). (pp. 31-41).
Daigle, J. J., Hudnall, W. H., Gabriel, W. J., Mersiovsky, E., and Nielson, R. D. (2005). The National Soil Information System (NASIS): Designing soil interpretation classes for military land-use predictions. Journal of terramechanics, 42(3), 305-320.
Du, C., Ren, H., Qin, Q., Meng, J., and Zhao, S. (2015). A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 7(1), 647-665.
Elnaggar, A. A. (2007). Development of predictive mapping techniques for soil survey and salinity mapping. Ph. D. dissertation, Oregon State University.
Franklin, J. (1995). Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in physical geography, 19(4), 474-499.
Grunwald, S., Thompson, J. A., and Boettinger, J. L. (2011). Digital soil mapping and modeling at continental scales: Finding solutions for global issues. Soil Science Society of America Journal, 75 (4): 1201-1213.
Hash, S. J. (2008). Use of decision tree analysis for predictive soils mapping and implementation on the Malheur County. Ph. D. dissertation, Oregon State University.
Henderson, B. L., Bui, E. N., Moran, C. J. and Simon, D. A. P. 2005. Australia-wide predictions of soil properties using decision trees. Geoderma, 124(3-4), 383-398.
Heung, B., Ho, H. C., Zhang, J., Knudby, A., Bulmer, C. E., and Schmidt, M. G. (2016). An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265, 62-77.
Hengl, T., Toomanian, N., Reuter, H. I., and Malakouti, M. J. (2007). Methods to interpolate soil categorical variables from profile observations: lessons from Iran. Geoderma, 140(4), 417-427.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309
Hutchinson, F. M., and Gallant, J. C. (2000) Terrain analysis: principles and applications. In Gallant J. C., Willson. J. P., (Ed.), Digital elevation models and representation of terrain shape. (Vol. 2). (pp. 29-50). New York: Wiley.
Jafari, A., Finke, P. A., Vande Wauw, J., Ayoubi, S., and 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.
Jafari, H. Khademi, SH. Ayoubi. (2013). Digital Mapping of Soil Diagnostic Horizons and Great Groups in Zarand Region of Kerman. Journal of Water and Soil Science, 16 (62), 177-193. (In Farsi)
Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective, (4th ed.). Prentice Hall, Inc., Upper Saddle River, New Jersey.
Kim, J., Grunwald, S., Rivero, R. G., & Robbins, R. (2012). Multi-scale modeling of soil series using remote sensing in a wetland ecosystem. Soil Science Society of America Journal, 76(6), 2327-2341.
Klute, A. (1986). Methods of soil analysis. Part 1. Physical and mineralogical properties. (2th ed.). Wisconsin USA: Madison
Lagacherie, P., and McBratney, A. B. (2006). Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. Developments in soil science, 31, 3-22.
Malone, B. (2013). Use of R for digital soil mapping. Soil Security Laboratory, University of Sydney, Australia. (pp. 1-209).
Mark, D. M., and Csillag, F. (1989). The nature of boundaries on ‘area-class’ maps. Cartographica: The International Journal for Geographic Information and Geovisualization, 26(1), 65-78.
Marchetti, A., Piccini, C., Santucci, S., Chiuchiarelli, I., and Francaviglia, R. (2011). Simulation of soil types in Teramo province (Central Italy) with terrain parameters and remote sensing data. Catena, 85(3), 267-273.
McBratney, A. B., Odeh, I. O., Bishop, T. F., Dunbar, M. S., and Shatar, T. M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97(3), 293-327.
McBratney, A. B., Santos, M. M., and Minasny, B. 2003. On digital soil mapping. Geoderma, 117(1), 3-52.
 Metternicht, G. I., and Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote sensing of Environment, 85(1), 1-20.
Msanya, B. M., Magoggo, J. P., and Otsuka, H. (2002). Development of soil surveys in Tanzania. Japanese Society of Pedology, 46 (2), 79-88.
Moran, C. J., and Bui, E. N. (2002). Spatial data mining for enhanced soil map modelling. International Journal of Geographical Information Science, 16(6): 533-549.
Nabiollahi, K., Haidari, A., and Taghizade-mehrjardi, R. (2014). Digital Mapping of Soil Texture Using Regression Tree and Artificial Neural Network in Bijar, Kurdistan. Journal of Water And Soil, 28(5), 1025-1036. (In Farsi)
Nauman, T. (2009). Digital soil-landscape classification for soil survey using ASTER satellite and digital elevation data in Organ Pipe Cactus National Monument, Arizona. M. Sc. dissertation, The University of Arizona.
Nelson, M. A., and Odeh, I. O. A. (2009). Digital soil class mapping using legacy soil profile data: a comparison of a genetic algorithm and classification tree approach. Soil Research, 47(6), 632-649.
Nield, S. J., Boettinger, J. L., and Ramsey, R. D. (2007). Digitally mapping gypsic and natric soil areas using Landsat ETM data. Soil Science Society of America Journal, 71(1), 245-252.
pahlavan rad, M., Khormali, F., Toomanian, N., Kiani, F., Komaki, B.(2015a). Digital soil mapping using Random Forest model in Golestan province. Journal of Soil and Water Conservation, 21 (6), 73-93. (In Farsi)
pahlavan rad, M., Khormali, F., Toomanian, N., Kiani, F., Komaki, B.(2015b). Forecasting soil classes with random decision making and logistic regression methods in Golestan province. In: 14th Iranian Soil Science Congress - Genesis, Classification, Soil and Land Scape Evaluation, 7-9 September., Iran, Rafsanjan , Univercity of Rafsanjan, pp. 101-115.
Sparks, D. L., Page, A. L., Helmk, P. A., Leopert, R. H., Soltanpour, P. N., Tabatabai, M. A., Johnston, C. T., Sumner, M. E. (Ed.). (1996) Methods of soil analysis. Part 3. Chemical Method. Wisconsin USA: Madison.
Pearson, R. L., Miller, L. D., (1972). Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grassland, Colorado. 8th In: Proceedings of 8th International Symposium on Remote Sensing of Environment, pp. 1357–1381.
Schoeneberger, P. J., D. A. Wysocki, E.C. Benham, and Soil Survey Staff. (2012). Field book for describing and sampling soils, Version 3.0. Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE.
Segal, D. B. (1982). Theoretical basis for differentiation of ferric-iron bearing minerals using Landsat MSS data. In: Proceedings. International Symposium on Remote Sensing of Environment, 2nd Thematic Conference, Remote Sensing for Exploration Geology, pp. 949-951.
Scull, P., Franklin, J., and Chadwick, O. A. 2005. The application of classification tree analysis to soil type prediction in a desert landscape. Ecological modelling, 181(1): 1-15.
Shirali, R. (2016). Classification Trees and Rule-Based Modeling Using the C5. 0 Algorithm for Self-Image across Sex and Race in St. MSc. dissertation, Washington University St. Louis.
Subburayalu, S. K., Jenhani, I., and Slater, B. K. (2014). Disaggregation of component soil series on an Ohio County soil survey map using possibilistic decision trees. Geoderma 213, 334-345.
Taghizadeh-Mehrjardi, R., Sarmadian, F., and Omid, M., Toomanian, F., RoUsta, M. J., and Rahimian, M. H. (2015). Digital mapping of soil classes using different data mining techniques in Ardakan region, Yazd province. Journal of Agricultural Engineering, 37 (2), 101-115. (In Farsi)
Taghizadeh-Mehrjardi, R., Sarmadian, F., Minasny, B., Triantafilis, J., and Omid, M. (2014). Digital mapping of soil classes using decision tree and auxiliary data in the Ardakan region, Iran. Arid Land Research and Management, 28(2), 147-168.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.