Digital soil mapping of a coastal acid sulfate soil landscape
Jingyi Huang A , Terence Nhan A , Vanessa N. L. Wong B , Scott G. Johnston C , R. Murray Lark D and John Triantafilis A E
+ Author Affiliations
- Author Affiliations
A School of Biological, Earth and Environmental Science, The University of New South Wales, Sydney, NSW 2052, Australia.
B School of Geography and Environmental Science, Monash University, Wellington Road, Clayton, Vic. 3800, Australia.
C Southern Cross Geoscience, Southern Cross University, Lismore, NSW 2480, Australia.
D British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK.
E Corresponding author. Email: j.triantafilis@unsw.edu.au
Soil Research 52(4) 327-339 https://doi.org/10.1071/SR13314
Submitted: 28 October 2013 Accepted: 13 February 2014 Published: 1 May 2014
Abstract
Coastal floodplains are commonly underlain by sulfidic sediments and coastal acid sulfate soils (CASS). Oxidation of sulfidic sediments leads to increases in acidity and mobilisation of trace metals, resulting in an increase in the concentrations of conducting ions in sediment and pore water. The distribution of these sediments on floodplains is highly heterogeneous. Accurately identifying the distribution of CASS is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) using ancillary information. Proximal sensing instruments such as an EM38 can provide data on the spatial distribution of soil salinity, which is associated with CASS, and can be complemented by digital elevation models (DEM). We used EM38 measurements of the apparent soil electrical conductivity (ECa) in the horizontal and vertical modes in combination with a high resolution DEM to delineate the spatial distribution of CASS. We used a fuzzy k-means algorithm to cluster the data. The fuzziness exponent, number of classes (k) and distance metric (i.e. Euclidean, Mahalanobis and diagonal) were varied to determine a set of parameters to identify CASS. The mean-squared prediction error variance of the class mean of various soil properties (e.g. EC1:5 and pH) was used to identify which of these metrics was suitable for further analysis (i.e. Mahalanobis) and also determine the optimal number of classes (i.e. k = 4). The final map is consistent with previously defined soil–landscape units generated using traditional soil profile description, classification and mapping. The DSM approach is amenable for evaluation on a larger scale and in order to refine CASS boundaries previously mapped using the traditional approach or to identify CASS areas that remain unmapped.
Additional keywords: apparent electrical conductivity, coastal acid sulfate soils, coastal floodplain, electromagnetic induction, EM38.
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