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

Abstract

Abstract. ​​​​​​​Micromorphological analysis using a petrographic microscope is one of the conventional methods to characterise microfacies in rocks (sediments) and soils. This analysis of the composition and structure observed in thin sections (TSs) yields seminal, but primarily qualitative, insights into their formation. In this context, the following question arises: how can micromorphological features be measured, classified, and particularly quantified to enable comparisons beyond the micro scale? With the Micromorphological Geographic Information System (MiGIS), we have developed a Python-based toolbox for the open-source software QGIS 3, which offers a straightforward solution to digitally analyse micromorphological features in TSs. By using a flatbed scanner and (polarisation) film, high-resolution red–green–blue (RGB) images can be captured in transmitted light (TL), cross-polarised light (XPL), and reflected light (RL) mode. Merging these images in a multi-RGB raster, feature-specific image information (e.g. light refraction properties of minerals) can be combined in one data set. This provides the basis for image classification with MiGIS. The MiGIS classification module uses the random forest algorithm and facilitates a semi-supervised (based on training areas) classification of the feature-specific colour values (multi-RGB signatures). The resulting classification map shows the spatial distribution of thin section features and enables the quantification of groundmass, pore space, minerals, or pedofeatures, such nodules being dominated by iron oxide and clay coatings. We demonstrate the advantages and limitations of the method using TSs from a loess–palaeosol sequence in Rheindahlen (Germany), which was previously studied using conventional micromorphological techniques. Given the high colour variance within the feature classes, MiGIS appears well-suited for these samples, enabling the generation of accurate TS feature maps. Nevertheless, the classification accuracy can vary due to the TS quality and the academic training level, in micromorphology and in terms of the classification process, when creating the training data. However, MiGIS offers the advantage of quantifying micromorphological features and analysing their spatial distribution for entire TSs. This facilitates reproducibility, visualisation of spatial relationships, and statistical comparisons of composition among distinct samples (e.g. related sediment layers).

Assessment of the environmental impacts of conflict-driven Internally Displaced Persons: A sentinel-2 satellite based analysis of land use/cover changes in the Kas locality, Darfur, Sudan

“…Unregulated access, however, leads to overexploitation hence a reduction in vegetation cover. These findings conform with that by [ 14 , 43 , 62 ] who cite overexploitation of forest resources for firewood and charcoal production as a key driver of vegetation cover loss. The forests and shrubs further provide timber, building poles and tree branches which are used in the establishment of the temporary settlement structures by the IDPs (built-up areas) which has increased over time.…”

“…Three classifiers RF, KNN, and SVM were run on both Sentinel 2 images, and the output raster was generated. Default parameters of Dzetsaka plugin were used to run all algorithms [ 43 , 46 ]. Before image classification we used raster calculator tool in QGIS to calculate the NDVI (Normalised Different Vegetation Index) using Eq ( 2 ) [ 51 ].…”

“…There is no predetermined size for this; it relies on the specific attributes of each data set and the intended class number. [43][44][45].…”