www.frontiersinecology.org © The Ecological Society of America

Cover and composition of ground vegetation affect ani-mal distributions and abundance by influencing habi-tat selection and providing critical resources for survival and reproduction (Daubenmire 1959; Rotenberry and Wiens 1980). It is therefore important to use sampling techniques that yield unbiased or nearly unbiased estima-tors of vegetation cover and composition to avoid mislead-ing conservation recommendations (Korb et al. 2003). Traditional methods for measuring ground cover and com-position are usually based on the Daubenmire (1959) tech-nique, in which observers visually estimate either percent cover or percent cover categories (eg 0–5, 5–25, 25–50, 50–75, 75–95, and 95–100%). However, subjective meth-ods based on visual estimation are prone to observer bias (Stohlgren et al. 1998) and are not repeatable (ie different observers will almost certainly record different measure-ments for the same quantity; Kercher et al. 2003). Published estimates of observer bias affecting visual esti-mation have ranged from 13 % to 90 % variance, depending on percent cover category (Hatton et al. 1986; Kennedy and Addison 1987) and plot size (Klime 2003). There have been a number of attempts to account for observer bias in visual estimation techniques, but none have completely removed subjectivity. Tilman’s tech-nique (1997) uses visual aids, such as cardboard cutouts of specific shapes and sizes to improve estimates. However, interpretation of these cutouts is still subject to bias. Others have used known levels of ground cover to train observers (Anderson and Kothmann 1982), but training alone will also not completely remove observer bias (Sykes et al. 1983). Recent advances in Geographic Information System (GIS) and image analysis software and technology offer potential for using digital images of habitat to objectively quantify ground cover and compo-sition of vegetation in a repeatable and timely manner. Here we evaluate the use of copyrighted image analysi