Urban tree canopies are a vital component of green infrastructure, especially in the context of the accelerating urban heat island effect and global climate change. Quantifying urban canopy cover in relation to land use and land cover changes is therefore crucial. However, accurately evaluating visual changes remains a challenge. In this study, we introduced the Urban Cover View Factor (VF) and Potential Influence Intensity Grade (PIIG) for tree canopy (TC) mapping using airborne Light Detection and Ranging (LiDAR) remote-sensing three-dimensional point clouds (3DPCs) from the Incheon metropolitan area, South Korea. The results demonstrated that airborne LiDAR 3DPCs effectively segmented non-sky urban cover views. Furthermore, the PIIG map, derived from the TC VF map, showed a significant correlation between surface heat risks and energy consumption patterns. Areas with lower PIIG grades tended to have higher energy consumption and greater vulnerability to surface heat risks, while areas with higher PIIG grades exhibited the opposite trend. Nevertheless, further exploration of complex urban cover and the collection of sufficient ground-based evidence is crucial for practical PIIG application. Further remote sensing research should support the management of urban tree canopies and urban agriculture to promote sustainable urban greening in response to evolving environmental needs.