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In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green blue (NGB) digital images were classified using an-object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (R-NIR-R-Green)/(R-NIR+R-Green)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., similar to 5% (0.01) for polar semi-desert; similar to 10% (0.04) for mesic tundra; and similar to 12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (R-x - R-y)/(R-x + R-y)), where R-x is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 9493 nm) bands; R-y is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R-2’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season. (C) 2016 Elsevier B.V. All rights reserved.