Predicting regolith properties using environmental correlation: a comparison of spatially global and spatially local approaches Laffan, Shawn en_US Lees, Brian en_US 2021-11-25T13:09:50Z 2021-11-25T13:09:50Z 2004 en_US
dc.description.abstract In this paper we assess the utility of mapping regolith properties as continuously varying fields using environmental correlation over large spatial areas. The assessment is based on a comparison of results from spatially global and local analysis methods. The two methods used are a feed-forward, back propagation neural network, applied globally, and moving window regression, applied locally. These methods are applied to five regolith properties from a field site at Weipa, Queensland, Australia. The properties considered are the proportions of oxides of aluminum, iron, silica and titanium present in samples, as well as depth to the base of the bauxite layer. These are inferred using a set of surface measurable features, consisting of Landsat data, geomorphometric indices, and distances from streams and swamps. The moving window regression results show a much stronger relationship than do those from the spatially global neural networks. The implication is that the scale of the analysis required for environmental correlation is of the order of hundreds of metres, and that spatially global analyses may incur an automatic reduction in accuracy by not modelling geographically local relationships. in this case, this effect is up to 45% error at a tolerance near half of a standard deviation. en_US
dc.identifier.issn 0016-7061 en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.subject.other regolith mapping en_US
dc.subject.other soil-landscape mapping en_US
dc.subject.other artificial neural network en_US
dc.subject.other Moving window regression en_US
dc.title Predicting regolith properties using environmental correlation: a comparison of spatially global and spatially local approaches en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.identifier.doiPublisher en_US
unsw.relation.faculty Science
unsw.relation.ispartofissue 3-4 en_US
unsw.relation.ispartofjournal Geoderma en_US
unsw.relation.ispartofpagefrompageto 241-258 en_US
unsw.relation.ispartofvolume 120 en_US
unsw.relation.originalPublicationAffiliation Laffan, Shawn, Biological, Earth & Environmental Sciences, Faculty of Science, UNSW en_US
unsw.relation.originalPublicationAffiliation Lees, Brian en_US School of Biological, Earth & Environmental Sciences *
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