Optimising interpolation as a tool for use in soil property mapping
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Date
2014-01-31
Authors
Mtshawu, Babalwa
Journal Title
Journal ISSN
Volume Title
Publisher
University of the Free State
Abstract
Inverse distance weighting (IDW) and kriging are robust and widely used estimation techniques
in earth sciences (soil science). Variance of Kriging is often proposed as a statistical technique
with superior mathematical properties such as a minimum error variance. However, the
robustness and simplicity of IDW motivate its continued use. This research aims to compare the
two interpolation techniques (Inverse Distance Weighting and Kriging), as well as to evaluate
the effect of sampling density on mapping accuracy of soil properties with diverse spatial
structure and diverse variability in a quest to improve interpolation quality for soil chemical
property mapping.
The comparison of these interpolation methods is achieved using the total error of crossvalidation
and validation statistics. Mean Prediction Error and Root Mean Square Error are
calculated and combined to determine which interpolator produced the lowest total error. The
interpolator that produced the lowest total error portrays the most accurate soil property
predictions of the study area.
The finding of this study strongly suggests that the accuracy achieved in mapping soil properties
strongly depends on the spatial structure of the data. This was clearly visible, in that, when the
subset training data set was decreased, the total error increased. The results also confirmed
that systematic sampling pattern provides more accurate results than random sampling pattern.
The overall results obtained from the comparison of the two applied interpolation methods
indicated that Kriging was the most suitable method for prediction and mapping the spatial
distribution of soil chemical properties in this study area.
Description
Keywords
Dissertation (M.Sc. (Geography))--University of the Free State, 2014, Soil mapping, Landforms, Interpolation, Kriging