Understanding natural fracture networks in the subsurface is highly challenging, as direct one-dimensional borehole data are unable to reflect their spatial complexity, and three-dimensional seismic data are limited in spatial resolution to resolve individual meter-scale fractures. Here, we present a prototype workflow for automated fracture detection along horizontal scan lines using terrestrial light detection and ranging (t-LIDAR). Data are derived from a kilometer-scale Pennsylvanian (locally upper Carboniferous) reservoir outcrop analog in the Lower Saxony Basin, northwestern Germany. The workflow allows the t-LIDAR data to be integrated into conventional reservoir-modeling software for characterizing natural fracture networks with regard to orientation and spatial distribution. The analysis outlines the lateral reorientation of fractures from a west-southwest/east-northeast strike, near a normal fault with approximately 600 m (similar to 1970 ft ) displacement, toward an east-west strike away from the fault. Fracture corridors, 10-20 m (33-66 ft) wide, are present in unfaulted rocks with an average fracture density of 3.4-3.9 m(-1) (11.2-12.8 ft(-1)). A reservoir-scale digital outcrop model was constructed as a basis for data integration. The fracture detection and analysis serve as input for a stochastically modeled discrete fracture network, demonstrating the transferability of the derived data into standard hydrocarbon exploration-and-production-industry approaches.