Reliable communication between satellites and ground control stations (GCS) is fundamental to modern space missions, with its effectiveness being directly dependent on an unobstructed Line-of-Sight (LoS). Traditional site planning methods, relying on low-resolution terrain models, often overlook crucial obstacles like buildings or dense vegetation. This paper presents a comprehensive methodology using high-resolution Light Detection and Ranging (LiDAR) data, acquired from an Unmanned Aerial Vehicle (UAV), to precisely model horizon obstruction and optimize the placement of transceiver antennas. The methodology was verified on a real-world case study in Zielona Góra, Poland. The workflow included data acquisition, PPK-based trajectory processing, and point cloud subsampling using an Octree-based algorithm. The core of this work was the implementation of an algorithm to generate detailed elevation masks by calculating the maximum obstruction angle for defined azimuthal intervals. The analysis clearly identified the superior of two potential locations, proving the method's effectiveness as a decision-support tool in the space sector.