Georeferenced soil provenancing with digital signatures

Abstract The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply...

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Autores principales:	M. Tighe, N. Forster, C. Guppy, D. Savage, P. Grave, I. M. Young
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81c0194e82ea47a794556e7521c734ba
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id	oai:doaj.org-article:81c0194e82ea47a794556e7521c734ba
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spelling	oai:doaj.org-article:81c0194e82ea47a794556e7521c734ba2021-12-02T15:08:27ZGeoreferenced soil provenancing with digital signatures10.1038/s41598-018-21530-72045-2322https://doaj.org/article/81c0194e82ea47a794556e7521c734ba2018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-21530-7https://doaj.org/toc/2045-2322Abstract The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science – that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.M. TigheN. ForsterC. GuppyD. SavageP. GraveI. M. YoungNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. Tighe N. Forster C. Guppy D. Savage P. Grave I. M. Young Georeferenced soil provenancing with digital signatures
description	Abstract The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science – that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.
format	article
author	M. Tighe N. Forster C. Guppy D. Savage P. Grave I. M. Young
author_facet	M. Tighe N. Forster C. Guppy D. Savage P. Grave I. M. Young
author_sort	M. Tighe
title	Georeferenced soil provenancing with digital signatures
title_short	Georeferenced soil provenancing with digital signatures
title_full	Georeferenced soil provenancing with digital signatures
title_fullStr	Georeferenced soil provenancing with digital signatures
title_full_unstemmed	Georeferenced soil provenancing with digital signatures
title_sort	georeferenced soil provenancing with digital signatures
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/81c0194e82ea47a794556e7521c734ba
work_keys_str_mv	AT mtighe georeferencedsoilprovenancingwithdigitalsignatures AT nforster georeferencedsoilprovenancingwithdigitalsignatures AT cguppy georeferencedsoilprovenancingwithdigitalsignatures AT dsavage georeferencedsoilprovenancingwithdigitalsignatures AT pgrave georeferencedsoilprovenancingwithdigitalsignatures AT imyoung georeferencedsoilprovenancingwithdigitalsignatures
_version_	1718388130782904320

Georeferenced soil provenancing with digital signatures

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