Articles | Volume 73, issue 1
https://doi.org/10.5194/egqsj-73-69-2024
https://doi.org/10.5194/egqsj-73-69-2024
Research article
 | 
26 Jan 2024
Research article |  | 26 Jan 2024

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

Mirijam Zickel, Marie Gröbner, Astrid Röpke, and Martin Kehl

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Cited articles

Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, R. E.: A land use and land cover classification system for use with remote sensor data, Tech. rep., USGS Publications Warehouse, Professional Paper, 964, Reston, USA, https://doi.org/10.3133/pp964, 1976.​​​​​​​ a
Arnay, R., Hernández-Aceituno, J., and Mallol, C.: Soil micromorphological image classification using deep learning: The porosity parameter, Appl. Soft Comput., 102, 107093, https://doi.org/10.1016/j.asoc.2021.107093, 2021. a
Arpin, T. L., Mallol, C., and Goldberg, P.: Short contribution: A new method of analyzing and documenting micromorphological thin sections using flatbed scanners: Applications in geoarchaeological studies, Geoarchaeology, 17, 305–313, https://doi.org/10.1002/gea.10014, 2002. a, b
Beckmann, T.: Präparation bodenkundlicher Dünnschliffe für mikromorphologische Untersuchungen, Hohenheimer Bodenkundliche Hefte, 40, 89–103, 1997. a
Breimann, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a, b, c, d
Short summary
With our open-source toolbox, MiGIS for QGIS 3, we intend to advance digital micromorphological analysis. This approach focuses on the classification of micromorphological constituents based on their distinct colour values (multi-RGB signatures), acquired using flatbed scanning of thin sections in different modes (transmitted, cross-polarised, and reflected light). The resulting thin section maps enable feature quantification, visualisation of spatial patterns, and reproducibility.
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