High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core

Jacq, Kévin; Perrette, Yves; Fanget, Bernard; Sabatier, Pierre; Coquin, Didier; Martinez-Lamas, Ruth; Debret, Maxime; Arnaud, Fabien

doi:10.1016/j.scitotenv.2019.01.320

Cited by 29 publications

(25 citation statements)

References 34 publications

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“…The combination of the two HSI into a unique one (i.e., VNIR-SWIR) was made with image registration (Liu et al, 2011) adapted to HSI with a registration on a wavelength plane characteristic of a similar chemical compound. In previous work, we find that the 970 nm (VNIR) and 1200 nm (SWIR) wavelengths were optimal to combine them (Jacq et al, 2019c). They are related to hydroxyl chemical bonds mostly associated with moisture for sediment cores (Bull, 1991;Viscarra Rossel and Behrens, 2010).…”

Section: Sample Sites Descriptionmentioning

confidence: 97%

Sedimentary structures discriminations with hyperspectral imaging on sediment cores

Jacq¹,

Rapuc²,

Benoît³

et al. 2020

Preprint

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Hyperspectral imaging (HSI) is a non-destructive high-resolution sensor, which is currently under significant development to analyze geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that need to be separated to temporally and spatially describe the sample. Sediment sequences are composed of successive deposits (strata, homogenite, flood) that can be visible or not depending on sample properties. The classical methods to identify them are time-consuming, have a low spatial resolution (millimeter), and are generally based on a naked-eye counting. In this study, we propose to compare several supervised classification algorithms for the discrimination of sedimentological structures on lake sediments. Instantaneous events in lake sediments are generally linked to extreme geodynamical events (e.g., floods, earthquakes), their identification and counting are essential to understand long-term fluctuations and improve hazard assessments. This is done by reconstructing a chronicle of event layer occurrence, including estimation of deposit thicknesses. Here we applied two hyperspectral imaging sensors (Visible Near-Infrared VNIR, 60 μm, 400-1000 nm; Short Wave Infrared SWIR, 200 μm, 1000-2500 nm) on three sediment cores from different lake systems. We highlight that the SWIR sensor is the optimal one to create robust classification models with discriminant analyses. Indeed, the VNIR sensor is impacted by the surface reliefs and structures that are not in the learning set, which lead to miss-classification. These observations are also valid for the combined sensor (VNIR-SWIR). Several spatial and spectral pre-processing were also compared and allowed to highlight discriminant information specific to a sample and a sensor. These works show that the combined use of hyperspectral imaging and machine learning improves the characterization of sedimentary structures in laboratory conditions.

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Section: Sample Sites Descriptionmentioning

confidence: 97%

Sedimentary structures discriminations with hyperspectral imaging on sediment cores

Jacq¹,

Rapuc²,

Benoît³

et al. 2020

Preprint

Self Cite

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“…Here, the work proposed the use of Rb measurements to correct the metal concentrations estimated in the field on fresh sediment in which the presence of interstitial water diluted the sediment mass and attenuated the incident X-rays. The full potential of other low-cost and high-resolution techniques including hyperspectral spectroscopy imaging remains to be explored (Butz et al 2015 ; Aymerich et al 2016 ; Jacq et al 2019 ).…”

Section: Emerging Themes In Sediment Fingerprinting Research and Applmentioning

confidence: 99%

“…Recently, the use of hyperspectral sensors provided a way to analyse chlorophyll a to quantify changes in burnt organic matter levels in a lake draining a catchment exposed to forest fires (Van Exem et al 2018 ). Collaborations between palaeoclimatologists, geomorphologists and hydrologists will stimulate novel methods and corresponding findings (Jacq et al 2019 ).…”

Section: Emerging Themes In Sediment Fingerprinting Research and Applmentioning

confidence: 99%

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

et al. 2020

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Purpose This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

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“…• C represents the percentage of total inorganic carbon (TIC). The sum of TOC and TIC is the content of total carbon (TC) [12].…”

Section: Physicochemical Analysismentioning

confidence: 99%

Distribution of Potential Harmful Trace Elements and Potential Ecological Risk in the Jiulongchi Wetland of Fanjing Mountain, Southwest China

Shen

Xiong

Gao

et al. 2020

IJERPH

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In order to understand the distribution and ecological risk of potential harmful trace elements (PHTEs) in the high altitude areas of the Fanjing Mountain World Natural Heritage Property, 30 surface samples including soil and plants were collected in April, 2019 in the Jiulongchi wetland which lies in the saddle between the New Jinding Peak of Fanjing Mountain and Fenghuang Peak. The contents of 23 major and trace elements were determined, and the pollution characteristics and potential ecological risk of 11 PHTEs (Mn, V, Zn, Cr, Co, Ni, Cu, As, Cd, Sb and Pb) were discussed. The element contents showed significant differences in plant and soil samples. Enrichment factor and single-factor pollution index demonstrated that Mn, Zn, Co, As and Cd in the soil were in a clean state. The potential ecological risk index and pollution load index indicated an overall good ecological condition of Jiulongchi wetland, with a weak pollution degree. Comparisons of relevant studies showed the anthropogenic activities have considerable impacts on the pollution status of PHTEs with significant spatial differences in Fanjing Mountain. Multivariate statistical analysis proved that Pb and Sb were main pollutants of the soil in the Jiulongchi wetland, and the mining and smelting of minerals such as mercury, manganese and lead-zinc ore in the adjacent areas could be the main pollution sources through atmospheric deposition. This study could provide targeted strategies for the environmental protection and management of the Property and give scientific evidence for the pollution prevention in southwest China.

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High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core

Cited by 29 publications

References 34 publications

Sedimentary structures discriminations with hyperspectral imaging on sediment cores

Sedimentary structures discriminations with hyperspectral imaging on sediment cores

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

Distribution of Potential Harmful Trace Elements and Potential Ecological Risk in the Jiulongchi Wetland of Fanjing Mountain, Southwest China

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