Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data

Background Singular value decomposition (SVD), as an alternative solution to principal components analysis (PCA), may enhance the spectral profile of burned areas in satellite image composites. Methods In this regard, we combine the pre-processing options of centering, non-centering, scaling, and non-scaling the input multi-spectral data, prior to the matrix decomposition, and treat their combinations as four different SVD-based PCA versions. Using both unitemporal and bi-temporal data sets, we test all four combinations to derive principal components. We assess the effects of the transformations based on multiresponse permutation procedures and quantify the enhanced spectral separability between burned areas and other major land cover classes via the Jeffries-Matusita metric. Lastly, we evaluate visually and numerically all principal components and select a subset of interest. Results The best transformation for the subset of selected components, is the uncentered-unscaled one. Conclusions The results indicate that an uncentered and unscaled SVD may improve the spectral separability of burned areas in some of the higher order components.