Using topography and vegetation attributes for estimation of saturated hydraulic conductivity in semiarid rangelands of Iran

Topographic (elevation, slope, and aspect) and vegetation attributes are routinely available data from digital elevation models (DEMs) and normalized difference vegetation index (NDVI). The objective of this study was to use topographic and vegetation attributes in addition to soil attributes to develop pedotransfer functions (PTFs) for estimating soil saturated hydraulic conductivity (Ks) in the semiarid rangelands of Iran.

We use three artificial neural networks (ANNs) and multiple linear regressions in estimating Ks from measured particle size distribution, bulk density, topographic attributes, NDVI, soil organic carbon and CaCo3 in topsoil and subsoil. Improvements were achieved with all three neural network models over a multiple linear regression models. Combination of topographic and vegetation attributes in addition to surface soil properties performed significantly better than other inputs models.



Copyright: © Aristotle University of Thessaloniki
Source: 2nd International Conference 2009 (Juni 2009)
Pages: 6
Price: € 5,00
Autor: B. Khalilmoghadam
Prof. Majid Afyuni
Dr. Karim C. Abbaspour
Ahmad Jalalian
Prof. Dr. Rainer Schulin

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