Digital Mapping of Soil Texture Using Regression Tree and Artificial Neural Network in Bijar, Kurdistan

آب و خاک

Article 9, Volume 28, Issue 5, November 2015, Pages 1025-1036   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.28735

Authors

kamal nabiollahi^* ; ahmad haidari¹; rohollah taghizade mehrjardi²

¹tehran university

²University of Ardakan

Abstract

Soil texture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. Therefore, at present research, 103 soil profiles were dogged and then sampled in order to prepare digital map of soil texture in Bijar, Kurdistan. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. To make a relationship between the soil data set (i.e. Clay, sand and silt) and auxiliary data, regression tree (RT) and artificial neural network (ANN) were applied. Results showed that the RT had the higher accuracy than ANN for spatial prediction of three parameters. For the clay fraction, determination of coefficient (R2) and root mean square root (RMSE) calculated for two models were 0.46, 0.81 and 17.10, 12.50, based on validation data set (20%). Our results showed some auxiliary variables had more influence on predictive soil class model which included: geomorphology map, wetness index, multi-resolution index of valley bottom flatness, elevation, slope length, and B3. In general, results showed that decision tree models had higher accuracy than ANN models and also their results are more convenient for interpretation. Therefore, it is suggested using of decision tree models for spatial prediction of soil properties in future studies.

Keywords

Auxiliary data; Spatial variation; Digital soil mapping

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