Recently, laser scanning systems, onboard airborne and terrestrial mobile mapping systems, have been established as a leading technology for collecting high density 3D information from an object's surface. The availability of generated surface models is very important for various industrial, military, environmental, and public applications. The accuracy of the derived point cloud coordinates from a LiDAR system is affected by inherent systematic and random errors. The impact of random errors depends on the precision of the system's measurements, which comprise position and orientation information from the GPS/INS unit, mirror angles, and ranges. On the other hand, systematic errors are mainly caused by biases in the mounting parameters (i.e., lever arm offset and boresight angles) relating the system components as well as biases in the system measurements (e.g., ranges and mirror angles). In order to ensure the geometric quality of the collected point cloud, the LiDAR systems should undergo a rigorous calibration procedure to estimate the system parameters that minimize the discrepancies between conjugate surface elements in overlapping LiDAR strips. The main objective of this paper is to look into an existing LiDAR system calibration technique, which is based on manual selection of overlapping regions between LiDAR strips and how to increase the efficiency of this technique by automatic selection of appropriate overlapping strip pairs, which should achieve the minimum optimal flight configuration that maximizes the impact of the discrepancies among conjugate surface elements in overlapping strips as well as automatic selection of regions within the appropriate overlapping strip pairs. The methodology of the proposed technique can be summarized as follows: first, the LiDAR strip pairs are grouped based on the flight configuration; second, appropriate overlapping strip pairs from each group is automatically selected; third, regions within the appropriate overlapping strip pairs are automatically selected based on their angles (slopes and aspects) and distribution; finally, the calibration procedure is applied. The experimental results have shown that the quality of the estimated parameters using the automatic selection are quite comparable to the estimated parameters using the manual selection while the proposed method is fully automated, and much faster.