PCA-based supervised identification of biological soil crusts in multispectral images

It was the aim of the method development to classify types of various biological soil crusts (biocrusts) using principle component analysis (PCA) on multispectral images. To address this aim, visible (RGB) and NIR images of bare sandy soil, algal and moss biocrusts were registered, per channel reflection values were determined using a calibration color chart on a pixel basis, and a PCA was performed on the unfolded RGB-NIR reflectance hypercubes (i.e. three-dimensional hypercubes were transformed into x.y × λ 2D-matrices with λ channels serving as variables for PCA). The classification approach was based on the hypothesis that biocrust types map specifically in PCA ordination plots, meaning that distinct regions in ordination plots may be assigned specifically to individual biocrust types. Reallocation of the pixels assigned to biocrust types to their respective image coordinates would then yield biocrust classification plots. • Allows manual selection of features or identification of given features in PCA ordination plots. • Fully permits the selection of relevant and omission of irrelevant, as well as identification of unknown classes. • It is not restricted to RGB-NIR multispectral data only, but may be applied to any type of multimodal imaging data.