Various disturbances such as disease and management practices cause canopy gaps that change patterns of forest stand structure. This study examined the usefulness of digital image analysis using aerial photos, Fourier Tranforms, and cluster analysis to investigate how different spatial statistics are affected by spatial scale. The specific aims were to: 1) evaluate how a Fourier filter could be used to classify canopy gap sizes objectively, 2) determine which statistics might be useful for detecting and measuring disturbance impacts, and 3) examine the potential for this method to determine spatial domains in a pair of ponderosa pine (Pinus ponderosa) stands in the Black Hills of South Dakota, USA. The eventual goal is to develop an operational method of assessing the impacts of natural disturbances such as disease. Results indicated that several spatial metrics discriminated between harvested and unharvested stands. We hypothesize that these metrics will be useful as spatial measures of disease impact if the analyses are performed on specific size classes of forest gaps.