Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22521
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dc.contributor.authorHapca, Simona Men_UK
dc.contributor.authorBaveye, Philippe Cen_UK
dc.contributor.authorWilson, Clareen_UK
dc.contributor.authorLark, R Murrayen_UK
dc.contributor.authorOtten, Wilfreden_UK
dc.date.accessioned2018-05-12T13:18:19Z-
dc.date.available2018-05-12T13:18:19Z-
dc.date.issued2015-09en_UK
dc.identifier.othere0137205en_UK
dc.identifier.urihttp://hdl.handle.net/1893/22521-
dc.description.abstractThere is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.en_UK
dc.language.isoenen_UK
dc.publisherPublic Library of Scienceen_UK
dc.relationHapca SM, Baveye PC, Wilson C, Lark RM & Otten W (2015) Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images. PLoS ONE, 10 (9), Art. No.: e0137205. https://doi.org/10.1371/journal.pone.0137205en_UK
dc.rights© 2015 Hapca et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are crediteden_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.titleThree-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Imagesen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1371/journal.pone.0137205en_UK
dc.identifier.pmid26372473en_UK
dc.citation.jtitlePLoS ONEen_UK
dc.citation.issn1932-6203en_UK
dc.citation.volume10en_UK
dc.citation.issue9en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderNatural Environment Research Councilen_UK
dc.author.emailc.a.wilson@stir.ac.uken_UK
dc.citation.date15/09/2015en_UK
dc.contributor.affiliationUniversity of Abertayen_UK
dc.contributor.affiliationRensselaer Polytechnic Instituteen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationBritish Geological Surveyen_UK
dc.contributor.affiliationUniversity of Abertayen_UK
dc.identifier.isiWOS:000361604400012en_UK
dc.identifier.scopusid2-s2.0-84945895420en_UK
dc.identifier.wtid885654en_UK
dc.contributor.orcid0000-0003-3148-9657en_UK
dc.contributor.orcid0000-0002-0287-8576en_UK
dc.date.accepted2015-08-13en_UK
dc.date.filedepositdate2015-11-17en_UK
dc.relation.funderprojectIntegrating physical and chemical techniques to characterise soil micro-sitesen_UK
dc.relation.funderrefNE/HO1263X/1en_UK
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