Soil is one of the most precious resources on Earth because of its role in storing
and recycling water and nutrients essential for life, providing a variety of
ecosystem services. This vulnerable resource is at risk from degradation by
erosion, salinity, contamination and other effects of mismanagement. Information
from soil is therefore crucial for its sustainable management. While the demand
for soil information is growing, the quantity of data collected in the field is reducing
due to financial constraints. Digital Soil Mapping (DSM) supports the creation of
geographically referenced soil databases generated by using field observations
or legacy data coupled, through quantitative relationships, with environmental
covariates. This enables the creation of soil maps at unexplored locations at
reduced costs. The selection of an optimal scale for environmental covariates is
still an unsolved issue affecting the accuracy of DSM.
The overall aim of this research was to explore the effect of spatial scale
alterations of environmental covariates in DSM. Three main targets were
identified: assessing the impact of spatial scale alterations on classifying soil
taxonomic units; investigating existing approaches from related scientific fields
for the detection of scale patterns and finally enabling practitioners to find a
suitable scale for environmental covariates by developing a new methodology for
spatial scale analysis in DSM.
Three study areas, covered by detailed reconnaissance soil survey, were
identified in the Republic of Ireland. Their different pedological and
geomorphological characteristics allowed to test scale behaviours across the
spectrum of conditions present in the Irish landscape. The investigation started
by examining the effects of scale alteration of the finest resolution environmental
covariate, the Digital Elevation Model (DEM), on the classification of soil
taxonomic units. Empirical approaches from related scientific fields were
subsequently selected from the literature, applied to the study areas and
compared with the experimental methodology. Wavelet analysis was also
employed to decompose the DEMs into a series of independent components at
varying scales and then used in DSM analysis of soil taxonomic units. Finally, a
new multiscale methodology was developed and evaluated against the previously
presented experimental results.
The results obtained by the experimental methodology have proved the
significant role of scale alterations in the classification accuracy of soil taxonomic
units, challenging the common practice of using the finest available resolution of
DEM in DSM analysis. The set of eight empirical approaches selected in the
literature have been proved to have a detrimental effect on the selection of an
optimal DEM scale for DSM applications. Wavelet analysis was shown effective
in removing DEM sources of variation, increasing DSM model performance by
spatially decomposing the DEM. Finally, my main contribution to knowledge has
been developing a new multiscale methodology for DSM applications by
combining a DEM segmentation technique performed by k-means clustering of
local variograms parameters calculated in a moving window with an experimental
methodology altering DEM scales. The newly developed multiscale methodology
offers a way to significantly improve classification accuracy of soil taxonomic units
in DSM.
In conclusion, this research has shown that spatial scale analysis of
environmental covariates significantly enhances the practice of DSM, improving
overall classification accuracy of soil taxonomic units. The newly developed
multiscale methodology can be successfully integrated in current DSM analysis
of soil taxonomic units performed with data mining techniques, so advancing the
practice of soil mapping. The future of DSM, as it successfully progresses from
the early pioneering years into an established discipline, will have to include scale
and in particular multiscale investigations in its methodology. DSM will have to
move from a methodology of spatial data with scale to a spatial scale
methodology. It is now time to consider scale as a key soil and modelling attribute
in DSM