A Spectral Emissivity Library of Spoil Substrates

Pivovarník, Marek; Pikl, Miroslav; Frouz, Jan; Zemek, František; Kopačková, Veronika; Notesco, Gila; Dor, Eyal Ben

doi:10.3390/data1020012

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Air humidity and temperature were recorded through a weather station (Figure 2; Tables S4-S7) to calibrate the thermograms during image post-processing. Emissivity was assumed according to the literature data [39]. We filtered the raw input following two main operations: a radiometric correction and a geometric correction.…”

Section: Methodsmentioning

confidence: 99%

Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering

et al. 2022

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Knowledge of physical and mechanical properties of geomaterials is fundamental to characterise their response to external forcings (mechanical, climatic) at various scales. This is true, for instance, in slope stability assessments, civil engineering works, and agriculture. The direct evaluation of these properties in situ can be difficult, especially in inaccessible or vast areas, and so can be the sampling and subsequent testing in the laboratory—where ensuring the representativeness of the acquired data at the scale of analysis poses an additional challenge. Thus, empirical correlations with more readily determinable quantities remain a powerful and practical tool. Recently, several sensors, able to inform on various geomaterial properties, have been developed. However, applications have typically targeted rocks, while studies on uncemented geomaterials (soils, geotechnically speaking) are lacking. Here, we propose a simple method to evaluate the porosity and critical state friction angle of soils via infrared thermography, consisting of periodic acquisitions of images in infrared wavelengths. To demonstrate the method’s capability, we analysed the cooling behaviour of samples of bentonite, kaolin, and sand (for which an extensive characterisation exists in the literature), after compaction to different porosities and pre-heating in an oven. We interpreted the results by seeking the optimal time interval for which a cooling rate index (CRI) could be defined, which is best linked with the target property. We found that the CRI correlates very well with the critical state friction angle (R2 > 0.85) and that different materials show unique and strong (R2 = 0.86–0.99) relationships between their porosity and the CRI, which also varies in a material-specific fashion according to the explored time interval. Although a systematic investigation on a wide range of natural soils is warranted, we argue that our method can be highly informative and could be used to calibrate remote sensing-based full-scale implementations in situ for various purposes.

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Section: Methodsmentioning

confidence: 99%

Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering

et al. 2022

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Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

et al. 2017

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Abstract:The study tested a data mining engine (PARACUDA ® ) to predict various soil attributes (BC, CEC, BS, pH, C org , Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R 2 ), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

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A Spectral Emissivity Library of Spoil Substrates

Cited by 2 publications

References 17 publications

Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering

Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

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