[Purposes] To address the challenges of unmanned aerial vehicle (UAV) localization failure in satellite-denied environments, threats from dynamic obstacles, and insufficient path planning safety and real-time performance caused by complex terrains, a hierarchical risk-aware UAV path planning method was proposed. [Methods] Based on hardware configuration, a “global guidance–local optimization” hierarchical architecture was established. At the global layer, an improved D*-Lite algorithm (dynamic A star lite, D*-Lite) was employed, integrating an obstacle distance risk kernel function and UAV kinematic constraints to generate a coarse-grained risk-aware map and output a safe corridor. At the local layer, a dynamic-biased Informed-RRT* algorithm (informed rapidly-exploring random tree star, Informed-RRT*) was utilized to adaptively adjust the sampling probability distribution within the safe corridor, avoid high-risk grids, and achieve real-time dynamic obstacle avoidance as well as path smoothing optimization. [Findings] Simulation experiments demonstrated that in complex scenarios with 25% obstacle density, the proposed algorithm achieved a planning success rate of 96.8%, increased by 17.7% and 7.4% compared to the traditional D* algorithm (dynamic A star, D*) and the Informed-RRT* algorithm, respectively. The path length ratio was reduced to 1.14, the average path risk value decreased to 0.21, and the average planning time per instance was only 87 ms. Real-world flight validation further confirmed that the system can effectively avoid dynamic obstacles in real time within a distance of 0.5 meters, meeting centimeter-level positioning accuracy and 22-minute endurance time requirements. [Conclusions] The hierarchical risk-aware path planning method significantly enhances UAV navigation safety, path quality, and real-time performance in satellite-denied environments, providing a safe and efficient autonomous flight solution for complex scenarios such as post-disaster rescue.