Thematic micro‐maps of soil components using high‐resolution spatially referenced mosaics from whole soil thin sections and image analysis

다국어 초록 (Multilingual Abstract)

In the quantification and analysis of the distribution of soil basic components, factors such as spatial resolution, scales of observation and analytical techniques used to interpret digital images of microscopic platforms are critical to obtain reproducible results. In addition, individual or discrete images, sometimes with high spatial resolution, distort features or basic components when zooming in or out, which limits their classification. This research proposes the use of micromorphological tools, spatially referenced mosaics with high resolution, image analysis and composite images to elaborate thematic maps of soil components. Therefore, we created mosaics with sequential digital images from soil thin sections (up to 3850 mm2) of different diagnostic horizons, with different magnifications (2×, 10× and 20×) and light sources. The mosaics (between 0.25 and 2.6 μm2 per pixel, and between 2 and 8 GB file size) were processed by image analysis (segmentation, supervised classification and accuracy assessments) for delimiting their basic soil components from composite images and with spatial operators to elaborate thematic maps at the microscopic level. The RGB (red, green, blue) brightness values of each soil component with different light sources enabled us to identify and quantify size classes of aggregates, voids, pedofeatures and organic matter with different degrees of decomposition at a high level of precision (up to 99% overall accuracy). It was also possible to develop good‐quality thematic maps of such components from high‐resolution spatially referenced mosaics and composite images Thematic maps of soil components can be explored without losing their spatial reference and analysed on multiple scales from a whole soil thin section.

Micromorphology and digital cartography to develop thematic maps of soil components
High spatial resolution mosaics from thin sections and image analysis were used for mapping
RGB brightness values of different light sources facilitated mapping of soil features
Accurate maps enabled quantification and analysis of spatial relations of soil components