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University of New South Wales (UNSW) 2017

Digital Soil Mapping Of Landscape Units And Salinity Across The Bourke Irrigation District

Zare, Ehsan

Titre : Digital Soil Mapping Of Landscape Units And Salinity Across The Bourke Irrigation District

Auteur : Zare, Ehsan

Université de soutenance : University of New South Wales (UNSW)

Grade : Doctor of Philosophy (PhD) 2017

In Bourke Irrigation district (BID), one of the most productive agricultural soil types is the Vertosol which has been extensively developed for irrigated agricultural production. However, there have been increasing instances of decline in productivity as a result of secondary soil salinization due to injudicious location of water storages and conveyance channels in irrigated farmlands. Therefore, it is important to map the soil in this area to understand the long-term sustainability issues and spatial variability in soil types. A great deal of soil data is required to characterise large areas such as BID. One approach is to use digital soil mapping (DSM) methods because they rely on the use of low-cost ancillary data to value add to limited soil information via the use of spatial and non-spatial numerical methods. In this thesis, two different DSM approaches are used in order to identify soil landscape units and a soil property (i.e. ECe dS/m). Firstly ancillary data, including remotely sensed air-borne gamma-ray (gamma-ray) spectrometer (i.e. potassium-K, uranium-U, thorium-Th and total counts-TC) and proximal sensed EM38 in the horizontal (EM38h) and vertical (EM38v) mode of operation are used with a non-spatial numerical clustering algorithm (fuzzy k-means : FKM). The FKM analysis (using Mahalanobis metric) of the kriged ancillary (i.e. common 100 m grid) data revealed a fuzziness exponent (phi) of 1.4 was suitable for further analysis and that k = 4 classes was smallest for the fuzziness performance index (FPI) and normalised classification entropy (NCE). Using laboratory measured physical (i.e. clay) and chemical (i.e. CEC, ECe and pH) properties revealed k = 4 was minimized in terms of mean squared prediction error (i.e. sigma2p,C) when considering topsoil (0-0.3 m) and subsoil (0.9-1.2 m) Clay, CEC, ECe and pH (i.e. only for topsoil). Secondly, in order to map a soil property limiting agricultural productivity, ancillary data, including remote and proximal, were used with a spatial numerical model (linear mixed modelling-LMM and restricted likely - REML) to map the spatial distribution of the saturated soil paste –extract (ECe dS/m). DSM of ECe using the same sources of ancillary data and empirical best linear unbiased prediction (E-BLUP) showed elevation, radioelement of thorium (Th) and logEM38v were the most statistically useful ancillary data. It was also found that the development of an error budget procedure, enabled the quantification of the relative contributions that model, input, combined and covariate error made to the prediction error of the map of ECe. The combined error is approximately 4.44 dS/m, which is relatively large compared to the standard deviation of measured ECe (3.61 dS/m). Of this error, most of it is attributable to the input error (1.38 dS/m) which is larger than the model error (0.02 dS/m). In terms of the input error, it is determined that the larger standard deviation is attributable to the lack of ancillary data, namely the ECa in areas adjacent to the Darling River and also on the aeolian dune where data collection was difficult owing to dense native vegetation.

Mots clés : Electromagnetic induction ; Digital soil mapping ; Gamma ray spectrometry


Page publiée le 14 juillet 2017