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dc.contributor.authorPaterson, Stacey
dc.date.accessioned2019-01-29
dc.date.available2019-01-29
dc.date.issued2018-09-07
dc.identifier.urihttp://hdl.handle.net/2123/19895
dc.description.abstractUnderstanding how soil variability changes with spatial scale is critical to our ability to understand and model soil processes at scales relevant to decision makers. This thesis uses legacy data to address the ongoing challenge of understanding soil spatial variability in a number of complementary ways. We use a range of information: precision agriculture studies; compiled point datasets; and remotely observed raster datasets. We use classical geostatistics, but introduce a new framework for comparing variability of spatial properties across scales. My thesis considers soil spatial variability from a number of geostatistical angles. We find the following: • Field scale variograms show differing variance across several magnitudes. Further work is required to ensure consistency between survey design, experimental methodology and statistical methodology if these results are to become useful for comparison. • Declustering is a useful tool to deal with the patchy design of legacy data. It is not a replacement for an evenly distributed dataset, but it does allow the use of legacy data which would otherwise have limited utility. • A framework which allows ‘roughness’ to be expressed as a continuous variable appears to fit the data better than the mono-fractal or multi-fractal framework generally associated with multi–scale modelling of soil spatial variability. • Soil appears to have a similar degree of stochasticity to short range topographic variability, and a higher degree of stochasticity at short ranges (less than 10km and 100km) than vegetation and Radiometrics respectively. • At longer ranges of variability (i.e. around 100km) only rainfall and height above sea level show distinctly different stochasticity. • Global variograms show strong isotropy, unlike the variograms for the Australian continent.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherFaculty of Scienceen_AU
dc.publisherSchool of Life and Environmental Sciencesen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectsoilen_AU
dc.subjectgeostatisticsen_AU
dc.subjectspatialen_AU
dc.subjectscaleen_AU
dc.subjectenvironmentalen_AU
dc.subjectmapen_AU
dc.titleSoil Spatial Scaling: Modelling variability of soil properties across scales using legacy dataen_AU
dc.typePhD Doctorateen_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU


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