Large-scale information on habitat suitability is indispensable for planning management actions to further endangered species with large-spatial requirements. So far, remote sensing based habitat variables mostly included environmental and land cover data derived from passive sensors, but lacked information on vegetation structure. This is a serious constraint for the management of endangered species with specific structural requirements. Light detection and ranging (LiDAR),in contrast to passive remote sensing techniques, may bridge this gap in structural information at the landscape scale. We investigated the potential of LiDAR data to quantify habitat suitability for capercaillie (Tetrao urogallus), an endangered forest grouse in Central Europe, in a forest reserve of 17.7 km². We used continuous variables of horizontal and vertical stand structure from first and last pulse LiDAR data and presence-absence information from field work to model habitat suitability with generalized linear models (GLM). The two final habitat suitability models explained the observed presence-absence pattern moderately well (AUC of 0.71 and 0.77) with horizontal structure explaining better than vertical structure. Relative tree canopy cover was the most important variable with intermediate values indicating highest habitat suitability. As such, LiDAR allowed us to translate the results from habitat modeling at the landscape scale to effective management recommendations at the local scale at a level of detail that hitherto was unavailable for large areas. LiDAR thus enabled us to integrate individual habitat preferences at the scale of entire populations and thus offers great potential for effective habitat monitoring and management of endangered species.