Abstract: Autonomous navigation in dense, dynamic clutter remains a fundamental challenge for Unmanned Aerial Vehicles (UAVs) due to the heterogeneous obstacle scales and complex motion patterns. Existing methods often rely on fragile explicit tracking or noise-sensitive implicit flow estimation, both of which struggle with irregular geometries and real-time constraints. We propose D-Nav, a novel end-to-end reinforcement learning framework that directly maps raw LiDAR observations to control actions through a saliency-driven dual-resolution spatio-temporal representation. At the global level, D-Nav constructs a spatio-temporal spherical depth representation that encodes scene structure and motion trends directly from sequential LiDAR measurements. Building on this global context, a saliency-based refinement mechanism is designed to identify dynamically critical regions and extract fine-grained geometric and motion cues at the local level. This formulation enables the policy to reason about both large-scale dynamic context and small, irregular, fast-moving obstacles in complex dynamic environments. In addition, D-Nav introduces a new mission-aware waypoint-goal condition mechanism that explicitly guides the policy to balance collision avoidance with the need to traverse task-critical regions. Extensive simulations demonstrate a significant improvement in navigation success, increasing from 0.37 to 0.56 in complex dynamic environments. Real-world experiments further validate the robustness and real-time capability of the proposed system across diverse dynamic scenarios. The code is available at https://anonymous.4open.science/r/D-NAV-2EF6.