The management of roe deer (Capreolus capreolus) means a challenge for hunters. Each hunting territory is designed differently and has its own special issues, so the management approaches vary from area to area. Furthermore, there are a lot of influences on habitat use that are often not adequately recorded, e.g., all kinds of disturbances. In the last decades, the stock of roe deer increased across Austria as well as Upper Austria, thus the need of an efficient management arises. The most important problem in the management of this game species is the lack of sound data on densities, which are frequently underestimated by 20 up to 100 percent. Exactly this fact plays the most important role for determining the accurate shooting limits for roe deer. The leading question for the study area St. Georgen an der Gusen is the actual density of roe deer, as the prescribed amount of roe deer culls cannot be achieved. Consequently, a sound estimate of the current deer density would allow the hunting community to improve and adapt their way of hunting. In addition, the browsing impact of roe deer in woodland areas should be analyzed in this study, without giving an assessment about damage or benefits of this browsing impact. For the survey of roe deer densities on agricultural areas we applied spotlighting, on woodland areas we used two methods of pellet group counts, i.e., Faecal Accumulation Rate (FAR) and Faecal Standing Crop (FSC). The results of the browsing survey were implemented in a logistic regression model to identify main parameters driving strong browsing impacts. Spotlighting yielded a mean roe deer density of 33/100 ha in the study area, whereas pellet counts estimated 23 individuals/100 ha (FSC) and 27 individuals/100 ha (FAR). So the prescribed amount of shot roe deer could actually be achieved. Browsing analyses showed that 19.3 percent of all investigated little trees were strongly browsed by roe deer. The logistic regression model comprised the predictors altitude, tree species, inclination, mesorelief, forest road, young stand, edge of the forest and the form of the terrain, and classified 85.1 percent of all cases correctly.