Colorimetric analysis of soils using digital cameras

Кириллова, Н. П.; Sileva, T. M.

doi:10.3103/s0147687417010045

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…The exposure settings vary among cameras. Kirillova & Sileva () compared a Sony DSC‐HX50 digital camera (Sony Corporation, Kōnan, Tokyo, Japan) with an X‐Rite i1 Pro spectrophotometer (X‐Rite Inc.) and observed that colours of 84% of the soil samples ( n = 136) were determined accurately by the camera. They recommended calibrating the camera‐derived colours with a standard chart.…”

Section: Discussionmentioning

confidence: 99%

Digital mapping of a soil profile

Zhang

Hartemink

2018

European J Soil Science

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Summary Detailed information on soil profiles is required for site characterization, soil mapping, studies on pedogenesis, soil–landscape modelling and soil classification. In this study, we used digital soil mapping techniques to map the profile wall of an Alfisol (90‐cm depth × 100‐cm width). Geochemical data (sampled at 10 cm × 10 cm) and digital images (resolution 1 cm × 1 cm) were used as input data in models that predicted a range of properties across the soil profile. Fuzzy c‐means clustering was applied to the profile and colour maps, and a confusion index was calculated. The colour coordinates were strongly correlated with SOC (soil organic carbon), sand and silt contents, and weathering indices. Random forest using the RGB model (R2 = 0.57, RMSE (root mean square error) = 2.41) showed less accurate prediction of SOC content than random forest using the CIE L*a*b* (R2 = 0.84, RMSE = 1.57) and HSV, hue, saturation, chroma (R2 = 0.85, RMSE = 1.46) models. Detailed profile maps showed considerable within‐horizon variation and identified animal holes, roots and limestone fragments. Colour and profile maps of soil properties and weathering indices matched field‐delineated soil horizons. Clusters obtained by soil properties and weathering indices represented the field‐delineated horizons and had the largest overall accuracy (P = 0.85 and 0.86, respectively). The confusion index showed that the horizons had wavy and somewhat irregular boundaries. We conclude that colour coordinates are useful for predicting and mapping soil properties in soil profiles. Random forest can be used to produce high‐resolution soil profile maps, and fuzzy c‐means clustering is useful for delineating soil horizons. Highlights We investigated the use of digital images in soil horizon delineation and within soil horizon variation We used various colour models to map soil profile properties and define horizons Colour and profile maps of soil properties and weathering indices matched field delineated soil horizons. Random forest can produce high‐resolution soil profile maps, and fuzzy c‐means clustering can delineate soil horizons

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Section: Discussionmentioning

confidence: 99%

Digital mapping of a soil profile

Zhang

Hartemink

2018

European J Soil Science

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“…In this case, a constant artificial light source is used, or the photograph is taken in natural light, but using calibration scales. RGB values extracted from a photo can be converted to XYZ (RGB to XYZ) and then to CIE-L*a*b* [47].…”

Section: Resultsmentioning

confidence: 99%

Differentiating Soils from Arable and Fallow Land Using Spectrometry

2021

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In this study, we analyse soil system responses—in particular, the colours of soils resulting from different types of land use (arable land and residential areas) caused by the construction of an ancient boundary rampart near a multilayer monument dating from between prehistory and the Middle Ages within the boundaries of the Bosporus Kingdom (Eastern Crimea)—in an area of modern and ancient (4th–2nd centuries BC) land use (Northwestern Crimea). These differences are of interest because they offer the chance to decipher different types of ancient land use and systems of land surveying, incorporating data from colour aerial photographs obtained with the help of unmanned aerial vehicles. Soil samples displaying different types of anthropogenic transformation were taken from the ancient boundary ramparts and adjacent land. The soil colour coordinates in the CIE L*a*b system were measured with the help of an AvaSpec-2048 spectrometer. Differences in colour coordinates were analysed using analysis of variance (NPMANOVA) based on the permutation test, the Kruskal–Wallis test, and the Mann–Whitney test, corrected according to multiple comparisons carried out as per the Bonferroni method. The results of this statistical analysis show that there are statistically significant differences in soil colour coordinates between samples collected on the ramparts and under various other types of land use. These differences are more pronounced in the samples characterised by the agrogenic transformation of soil. This makes it possible to use remote sensing data to detect traces of ancient boundary ramparts, even if the ramparts are partially destroyed by ploughing.

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“… The plotting of soil reflectivity curves in these conditions was an indication of the biological status of the soil in certain time periods.  This method can be adopted for the taxonomic studies of soils for the purposes of biological detection, as the flatbed scanner with the use of the glass calibration plate proved that it is an effective and promising method in terms of stabilization of lighting and accurate passage over all samples in an equal manner [13] .…”

Section: Discussionmentioning

confidence: 99%

Impact of soil biological and chemical activity on spectral reflectivity under anaerobic conditions

AlJawwadi¹

2022

World J. Adv. Res. Rev.

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Numbers of soil samples from different areas had been selected, then wetted and putted them in enclosed place to ensure the anaerobic conditions, then taking digital images for this soils on different period, by using flatbed scanner, and reading its reflective during the time to find relation of the time effect on soil reflective, Photoshop 10 CS3 was using to read RGB values then making mathematical operations to calculate reflective. The study referred to the ability to draw the reflectivity of soil curves as part of soil studying in anaerobic circumstances, and it became obvious that the Spectral reflectivity of the wind deposit soil is decrement through the time in greatly form relatively, An indication of the activity of anaerobic organisms.

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Colorimetric analysis of soils using digital cameras

Cited by 13 publications

References 7 publications

Digital mapping of a soil profile

Digital mapping of a soil profile

Differentiating Soils from Arable and Fallow Land Using Spectrometry

Impact of soil biological and chemical activity on spectral reflectivity under anaerobic conditions

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