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Principal component analysis is often used with remote sensing to decrease the dimensionality of multi-band imagery and to realign data with new axes to maximize variance in the imagery (Campbell and Wynne, 2011). Relief shaded maps using illumination from numerous aspect directions and inclinations from the horizon can also be input to principal component analysis (PCA), resulting in principal component maps that optimize variance and mimic relief shading. Using 15° increments of azimuth and inclination to vary illumination direction yields 121 relief shadings of the Churfirsten, Switzerland at 30 m. resolution, one of the sample elevation models available from http://shadedrelief.com/SampleElevationModels/ (Kennelly et al., 2021). PCA results in the three principal component images shown in Figure 1, accounting for more than 99% of the variance found in the 121 input relief shadings. While the first two principal component maps are similar to relief shading illuminated from near the horizon and separated by 90 degrees of azimuth, the third principal component map is similar to slope shading [ABSTRACT FROM AUTHOR]