The situation awareness capability of Unmanned Aerial Vehicles (UAVs) is a critical technology for ensuring successful mission execution and flight safety, playing an indispensable role in the development and application of UAVs. To enhance the situation awareness capabilities of UAVs while reducing the volume of mapping data, this paper proposes a situation awareness method for UAVs based on octree map. Initially, the motion trajectory of the UAV is determined using Kalman filtering for its pose information. Subsequently, feature extraction is performed using FAST key points and BRIEF descriptors, and feature points from adjacent frames are matched using a Fast Library for Approximate Nearest Neighbors algorithm to integrate image information. Finally, the octree map is constructed to enable effective situation awareness by combining the UAV's motion trajectory with image matching information. Simulation results demonstrate that this method not only achieves superior situation awareness for UAVs but also exhibits better data compression characteristics compared with point cloud maps.