Safety zones are areas where firefighters can retreat to in order to avoid bodily harm when threatened by burnover or entrapment from wildland fire. At present, safety zones are primarily designated by firefighting personnel as part of daily fire management activities. Though critical to safety zone assessment, the effectiveness of this approach is inherently limited by the individual firefighter’s or crew boss’s ability to accurately and consistently interpret vegetation conditions, topography, and spatial characteristics of potential safety zones (e.g. area and geometry of a forest clearing). In order to facilitate the safety zone identification and characterization process, this study introduces a new metric for safety zone evaluation: the Safe Separation Distance Score (SSDS). The SSDS is a numerical representation of the relative suitability of a given area as a safety zone according to its size, geometry, and surrounding vegetation height. This paper describes an algorithm for calculating pixel-based and polygon-based SSDS from lidar data. SSDS is calculated for every potential safety zone within a lidar dataset covering Tahoe National Forest, California, USA. A total of 2367 potential safety zones with an SSDS ≥1 were mapped, representing areas that are suitable for fires burning in low wind and low slope conditions. The highest SSDS calculated within the study area was 9.65, a score that represents suitability in the highest wind-steepest slope conditions. Potential safety zones were clustered in space, with areas in the northern and eastern portions of the National Forest containing an abundance of safety zones while areas to the south and west were completely devoid of them. SSDS can be calculated for potential safety zones in advance of firefighting, and can allow firefighters to carefully compare and select safety zones based on their location, terrain, and wind conditions. This technique shows promise as a standard method for objectively identifying and ranking safety zones on a spatial basis.