Abstract
Landsat imagery mosaics developed using model II regression have been shown to successfully model percent tree-canopy cover (ptcc). Creating model II regression mosaics, however, is a time-consuming, manual process. The objective of this study is to evaluate the effectiveness of using more easily automated image composites techniques, such as median Landsat-5 image composites or maximum NDVI Landsat-5 image composites, as alternatives to model II regression mosaics for the modeling of PTCC. This study found all composite types were effective in modeling PTCC, but the maximum NDVI composites included anomalies, clouds, shadows, and tended to be pixelated, whereas the median composites and the model II regression mosaics had none of these issues. The median composite procedure is automated and was found to be an effective approach to statistically reduce a much larger ensemble of images on a pixel basis to create images suitable for vegetation modeling.
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