Contrast-enhanced repacked soil cores as a proxy for soil organic matter spatial arrangement
Ilaria Piccoli![https://orcid.org/0000-0001-7748-5470](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0001-7957-3212](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-0235-4596](/media/client/orcid_16x16.png)
A DAFNAE Department, University of Padova, Italy.
B Department of Computer Science, University of Auckland, New Zealand.
C Corresponding author. Email: ilaria.piccoli@unipd.it
Soil Research 57(6) 535-545 https://doi.org/10.1071/SR18191
Submitted: 17 July 2018 Accepted: 1 April 2019 Published: 13 June 2019
Abstract
Soil organic matter (SOM) plays a key role in soil structure formation, although the bidirectional relationship between SOM and the soil pore network is complex and needs further investigation. Despite great advances provided by X-ray computed microtomography (µCT), it has only been used in a few studies to investigate the organic matter 3D-arrangement within the soil matrix. Results are based on the X-ray linear attenuation coefficient (α), and mixtures of organic and mineral soil fractions could imply overlapping of information that makes any segmentation procedure difficult. The aim of this study was to visualise, segment, and quantify the particulate organic matter fraction (POM) within the soil matrix through X-ray µCT in combination with contrast agents (phosphomolybdic acid and silver nitrate). Two series of repacked soil cores, ‘dry’ and ‘wet’, were scanned through X-ray µCT at a 7-µm resolution. Different segmentation approaches were tested to separate POM from other soil phases: manual, global, and local thresholding methods. Reported algorithms were also compared with a supervised grey value-based (GV) approach followed by morphological operations. Results showed contrast agents increased α of POM, simplifying its identification and the following segmentation on dry cores. The POM was discriminated from the mineral fraction and its content correctly estimated. This was particularly accurate when applying manual thresholding or GV approach with respect to indicator kriging, suggesting that operator-based ability to set threshold level is still the best solution for accurate POM segmentation. Beyond single-phase accounting, different thresholding algorithms and morphological operations also affected POM morphological characteristics. In particular, the simpler was an object shape, the easier was its segmentation. Improvements are thus required to increase the efficiency of automated thresholding algorithms. Moreover, wet cores were exposed to washing-out phenomena that compromised any digital image processing and further POM quantification, implying that more effort should be made to find other suitable staining agents.
Additional keywords: soil image analysis, soil organic matter, X-ray computed microtomography.
References
Abramoff MD, Magalhães PJ, Ram SJ (2004) Image processing with ImageJ. Biophotonics International 11, 36–42.Barrett JF, Keat N (2004) Artifacts in CT: recognition and avoidance. Radiographics 24, 1679–1691.
| Artifacts in CT: recognition and avoidance.Crossref | GoogleScholarGoogle Scholar | 15537976PubMed |
Baveye PC, Laba M, Otten W, Bouckaert L, Dello Sterpaio P, Goswami RR, Grinev D, Houston A, Hu YP, Liu JL, Mooney S, Pajor R, Sleutel S, Tarquis A, Wang W, Wei Q, Sezgin M (2010) Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma 157, 51–63.
| Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data.Crossref | GoogleScholarGoogle Scholar |
Beckers E, Plougonven E, Roisin C, Hapca S, Léonard A, Degré A (2014) X-ray microtomography: a porosity-based thresholding method to improve soil pore network characterization? Geoderma 219–220, 145–154.
| X-ray microtomography: a porosity-based thresholding method to improve soil pore network characterization?Crossref | GoogleScholarGoogle Scholar |
Chenu C, Hassink J, Bloem J (2001) Short-term changes in the spatial distribution of microorganisms in soil aggregates as affected by glucose addition. Biology and Fertility of Soils 34, 349–356.
| Short-term changes in the spatial distribution of microorganisms in soil aggregates as affected by glucose addition.Crossref | GoogleScholarGoogle Scholar |
Cnudde V, Boone MN (2013) High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth-Science Reviews 123, 1–17.
| High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications.Crossref | GoogleScholarGoogle Scholar |
Ekschmitt K, Kandeler E, Poll C, Brune A, Buscot F, Friedrich M, Gleixner G, Hartmann A, Kästner M, Marhan S, Miltner A, Scheu S, Wolters V (2008) Soil-carbon preservation through habitat constraints and biological limitations on decomposer activity. Journal of Plant Nutrition and Soil Science 171, 27–35.
| Soil-carbon preservation through habitat constraints and biological limitations on decomposer activity.Crossref | GoogleScholarGoogle Scholar |
Golchin A, Oades JM, Skjemstad JO, Clarke P (1994) Soil structure and carbon cycling. Soil Research 32, 1043–1068.
| Soil structure and carbon cycling.Crossref | GoogleScholarGoogle Scholar |
Hassink J, Bouwman LA, Zwart KB, Brussaard L (1993) Relationships between habitable pore space, soil biota and mineralization rates in grassland soils. Soil Biology & Biochemistry 25, 47–55.
| Relationships between habitable pore space, soil biota and mineralization rates in grassland soils.Crossref | GoogleScholarGoogle Scholar |
Houston AN, Schmidt S, Tarquis AM, Otten W, Baveye PC, Hapca SM (2013) Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma 207–208, 154–165.
| Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images.Crossref | GoogleScholarGoogle Scholar |
Hsieh J (2009) ‘Computed tomography: principles, design, artifacts, and recent advances.’ (SPIE: Bellingham, WA, USA)
Iassonov P, Gebrenegus T, Tuller M (2009) Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures. Water Resources Research 45, W09415
| Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures.Crossref | GoogleScholarGoogle Scholar |
Koestel J (2017) SoilJ: an ImageJ plugin for the semiautomatic processing of three-dimensional X-ray images of soils. Vadose Zone Journal 17, 170062
| SoilJ: an ImageJ plugin for the semiautomatic processing of three-dimensional X-ray images of soils.Crossref | GoogleScholarGoogle Scholar |
Kravchenko AN, Negassa W, Guber AK, Schmidt S (2014) New approach to measure soil particulate organic matter in intact samples using X-ray computed microtomography. Soil Science Society of America Journal 78, 1177–1185.
| New approach to measure soil particulate organic matter in intact samples using X-ray computed microtomography.Crossref | GoogleScholarGoogle Scholar |
Kravchenko AN, Negassa WC, Guber AK, Rivers ML (2015) Protection of soil carbon within macro-aggregates depends on intra-aggregate pore characteristics. Scientific Reports 5, 16261
| Protection of soil carbon within macro-aggregates depends on intra-aggregate pore characteristics.Crossref | GoogleScholarGoogle Scholar | 26541265PubMed |
Minelli S, Erlewein A, Castillo V (2017) Land degradation neutrality and the UNCCD: from political vision to measurable targets. ‘International Yearbook of Soil Law Policy 2016’. (Eds H Ginzky, IL Heuser, T Qin, OC Ruppel, P Wegerdt) pp. 85–104. (Springer: Cham)
Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Processing Magazine 13, 47–60.
| The expectation-maximization algorithm.Crossref | GoogleScholarGoogle Scholar |
Mooney SJ, Pridmore TP, Helliwell J, Bennett MJ (2012) Developing X-ray computed tomography to non-invasively image 3-D root systems architecture in soil. Plant and Soil 352, 1–22.
| Developing X-ray computed tomography to non-invasively image 3-D root systems architecture in soil.Crossref | GoogleScholarGoogle Scholar |
Negassa WC, Guber AK, Kravchenko AN, Marsh TL, Hildebrandt B, Rivers ML (2015) Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria. PLoS One 10, e0123999
| Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria.Crossref | GoogleScholarGoogle Scholar | 25909444PubMed |
Oh W, Lindquist BW (1999) Image thresholding by indicator kriging. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 590–602.
| Image thresholding by indicator kriging.Crossref | GoogleScholarGoogle Scholar |
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66.
| A threshold selection method from gray-level histograms.Crossref | GoogleScholarGoogle Scholar |
Peth S, Chenu C, Leblond N, Mordhorst A, Garnier P, Nunan N, Pot V, Ogurreck M, Beckmann F (2014) Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography. Soil Biology & Biochemistry 78, 189–194.
| Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography.Crossref | GoogleScholarGoogle Scholar |
Rawlins BG, Wragg J, Reinhard C, Atwood RC, Houston A, Lark RM, Rudolph S (2016) Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography. Soil (Göttingen) 2, 659–671.
| Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography.Crossref | GoogleScholarGoogle Scholar |
Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man, and Cybernetics 8, 630–632.
| Picture thresholding using an iterative selection method.Crossref | GoogleScholarGoogle Scholar |
Rosin PL (2001) Unimodal thresholding. Pattern Recognition 34, 2083–2096.
| Unimodal thresholding.Crossref | GoogleScholarGoogle Scholar |
Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW (2017) ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529
| ImageJ2: ImageJ for the next generation of scientific image data.Crossref | GoogleScholarGoogle Scholar | 29187165PubMed |
Schlüter S, Weller U, Vogel HJ (2010) Segmentation of X-ray microtomography images of soil using gradient masks. Computers & Geosciences 36, 1246–1251.
| Segmentation of X-ray microtomography images of soil using gradient masks.Crossref | GoogleScholarGoogle Scholar |
Schlüter S, Sheppard A, Brown K, Wildenschild D (2014) Image processing of multiphase images obtained via X-ray microtomography: a review. Water Resources Research 50, 3615–3639.
| Image processing of multiphase images obtained via X-ray microtomography: a review.Crossref | GoogleScholarGoogle Scholar |
Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M, Kögel-Knabner I, Lehmann J, Manning DAC, Nannipieri P, Rasse DP, Weiner S, Trumbore SE (2011) Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56.
| Persistence of soil organic matter as an ecosystem property.Crossref | GoogleScholarGoogle Scholar |
Seidell A, Linke W (1919) ‘Solubilities of inorganic and organic compounds.’ (D. Van Nostrand Company: New York, USA)
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13, 146–165.
| Survey over image thresholding techniques and quantitative performance evaluation.Crossref | GoogleScholarGoogle Scholar |
Six J, Paustian K (2014) Aggregate-associated soil organic matter as an ecosystem property and a measurement tool. Soil Biology & Biochemistry 68, A4–A9.
| Aggregate-associated soil organic matter as an ecosystem property and a measurement tool.Crossref | GoogleScholarGoogle Scholar |
Six J, Conant RT, Paul EA, Paustian K (2002) Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant and Soil 241, 155–176.
| Stabilization mechanisms of soil organic matter: implications for C-saturation of soils.Crossref | GoogleScholarGoogle Scholar |
Sleutel S, Cnudde V, Masschaele B, Vlassenbroek J, Dierick M, Van Hoorebeke L, Jacobs P, De Neve S (2008) Comparison of different nano-and micro-focus X-ray computed tomography set-ups for the visualization of the soil microstructure and soil organic matter. Computers & Geosciences 34, 931–938.
| Comparison of different nano-and micro-focus X-ray computed tomography set-ups for the visualization of the soil microstructure and soil organic matter.Crossref | GoogleScholarGoogle Scholar |
Taina IA, Heck RJ, Elliot TR (2008) Application of X-ray computed tomography to soil science: a literature review. Canadian Journal of Soil Science 88, 1–19.
| Application of X-ray computed tomography to soil science: a literature review.Crossref | GoogleScholarGoogle Scholar |
Van Loo D, Bouckaert L, Leroux O, Pauwels E, Dierick M, Van Hoorebeke L, Cnudde V, De Neve S, Sleutel S (2014) Contrast agents for soil investigation with X-ray computed tomography. Geoderma 213, 485–491.
| Contrast agents for soil investigation with X-ray computed tomography.Crossref | GoogleScholarGoogle Scholar |
Venkateswarlu NB, Raju PSVSK (1992) Fast isodata clustering algorithms. Pattern Recognition 25, 335–342.
| Fast isodata clustering algorithms.Crossref | GoogleScholarGoogle Scholar |
Vogel H-J, Weller U, Schlüter S (2010) Quantification of soil structure based on Minkowski functions. Computers & Geosciences 36, 1236–1245.
| Quantification of soil structure based on Minkowski functions.Crossref | GoogleScholarGoogle Scholar |
Wang W, Kravchenko AN, Smucker AJM, Rivers ML (2011) Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates. Geoderma 162, 231–241.
| Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates.Crossref | GoogleScholarGoogle Scholar |