TopCap: a tool to quantify soil surface topology and subsurface structure

The surface of a material such as soil, as characterised by its topology and roughness, typically has a profound effect on its functional behaviour. Whilst non-destructive imaging techniques such as X-ray Computed Tomography (CT) have been extensively employed in recent years to characterise the int...

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Main Authors: Garbout, Amin, Sturrock, Craig J., Armenise, Elena, Ahn, Sujung, Simmons, Robert W., Doerr, Stefan, Ritz, Karl, Mooney, Sacha J.
Format: Article
Language:English
Published: Soil Science Society of America 2018
Subjects:
Online Access:https://eprints.nottingham.ac.uk/46984/
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author Garbout, Amin
Sturrock, Craig J.
Armenise, Elena
Ahn, Sujung
Simmons, Robert W.
Doerr, Stefan
Ritz, Karl
Mooney, Sacha J.
author_facet Garbout, Amin
Sturrock, Craig J.
Armenise, Elena
Ahn, Sujung
Simmons, Robert W.
Doerr, Stefan
Ritz, Karl
Mooney, Sacha J.
author_sort Garbout, Amin
building Nottingham Research Data Repository
collection Online Access
description The surface of a material such as soil, as characterised by its topology and roughness, typically has a profound effect on its functional behaviour. Whilst non-destructive imaging techniques such as X-ray Computed Tomography (CT) have been extensively employed in recent years to characterise the internal architecture of soil, less attention has been paid to the morphology of the soil surface, possibly as other techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) are viewed as more appropriate. However, X-ray CT exploration of the surface of a soil also permits analysis immediately below its surface and beyond into the sample, contingent on its thickness. This provides important information such as how a connected structure might permit solute infiltration or gaseous diffusion through the surface and beyond into the subsurface matrix. A previous limitation to this approach had been the inability to segment and quantify the actual 3-D structural complexity at the surface, rather than a predefined geometrically simplistic volume immediately below it. To overcome this we formulated TopCap, a novel algorithm that operates with ImageJ as a plugin, which automatically captures the actual 3D surface morphology, segments the pore structure within the acquired 3D volume, and provides a series of incisive morphological measurements of the associated porous architecture. TopCap provides rapid, automated analysis of the immediate surface of materials and beyond, and whilst developed in the context of soil, is applicable to any 3D image volume.
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spelling nottingham-469842020-05-08T09:45:11Z https://eprints.nottingham.ac.uk/46984/ TopCap: a tool to quantify soil surface topology and subsurface structure Garbout, Amin Sturrock, Craig J. Armenise, Elena Ahn, Sujung Simmons, Robert W. Doerr, Stefan Ritz, Karl Mooney, Sacha J. The surface of a material such as soil, as characterised by its topology and roughness, typically has a profound effect on its functional behaviour. Whilst non-destructive imaging techniques such as X-ray Computed Tomography (CT) have been extensively employed in recent years to characterise the internal architecture of soil, less attention has been paid to the morphology of the soil surface, possibly as other techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) are viewed as more appropriate. However, X-ray CT exploration of the surface of a soil also permits analysis immediately below its surface and beyond into the sample, contingent on its thickness. This provides important information such as how a connected structure might permit solute infiltration or gaseous diffusion through the surface and beyond into the subsurface matrix. A previous limitation to this approach had been the inability to segment and quantify the actual 3-D structural complexity at the surface, rather than a predefined geometrically simplistic volume immediately below it. To overcome this we formulated TopCap, a novel algorithm that operates with ImageJ as a plugin, which automatically captures the actual 3D surface morphology, segments the pore structure within the acquired 3D volume, and provides a series of incisive morphological measurements of the associated porous architecture. TopCap provides rapid, automated analysis of the immediate surface of materials and beyond, and whilst developed in the context of soil, is applicable to any 3D image volume. Soil Science Society of America 2018-03-29 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/46984/34/vzj-17-1-170091.pdf Garbout, Amin, Sturrock, Craig J., Armenise, Elena, Ahn, Sujung, Simmons, Robert W., Doerr, Stefan, Ritz, Karl and Mooney, Sacha J. (2018) TopCap: a tool to quantify soil surface topology and subsurface structure. Vadose Zone Journal, 17 (1). p. 170091. ISSN 1539-1663 soil surface soil crust X-ray Computed Tomography threshold surface detection porosity https://dl.sciencesocieties.org/publications/vzj/abstracts/17/1/170091 doi:10.2136/vzj2017.05.0091 doi:10.2136/vzj2017.05.0091
spellingShingle soil surface
soil crust
X-ray Computed Tomography
threshold
surface detection
porosity
Garbout, Amin
Sturrock, Craig J.
Armenise, Elena
Ahn, Sujung
Simmons, Robert W.
Doerr, Stefan
Ritz, Karl
Mooney, Sacha J.
TopCap: a tool to quantify soil surface topology and subsurface structure
title TopCap: a tool to quantify soil surface topology and subsurface structure
title_full TopCap: a tool to quantify soil surface topology and subsurface structure
title_fullStr TopCap: a tool to quantify soil surface topology and subsurface structure
title_full_unstemmed TopCap: a tool to quantify soil surface topology and subsurface structure
title_short TopCap: a tool to quantify soil surface topology and subsurface structure
title_sort topcap: a tool to quantify soil surface topology and subsurface structure
topic soil surface
soil crust
X-ray Computed Tomography
threshold
surface detection
porosity
url https://eprints.nottingham.ac.uk/46984/
https://eprints.nottingham.ac.uk/46984/
https://eprints.nottingham.ac.uk/46984/