Enhanced Interpretation of Green Space Surface for Land Surface Temperature through a Novel Voxel-based Landscape Index from UAV LiDAR

Abstract

Urban forests are important for effectively mitigating urban heat island (UHI) effects. However, thorough investigations into how the three-dimensional (3D) structures of urban forests influences urban thermal conditions collectively and individually are limited. In this study, voxel-based landscape indices were innovatively extracted from UAV LiDAR data, and high-precision land surface temperature (LST) data were obtained using thermal infrared sensors mounted on a UAV. These were combined with a random forest (RF) model to analyze the relative influences and marginal effects of urban forest three-dimensional (3D) structure on LST. Our results showed the following: (1) The voxel-based landscape index exhibits a stronger capability to interpret LST than both the 2D landscape index and the gradient-based landscape index, with significant enhancements in model accuracy across all dimensions (an increase in R of 0.17-0.25 and a decrease in RMSE by 0.39-1.59°C). (2) Considering the vertical stratification of tree canopies, which voxel-based landscape index has the greatest LST fitting precision (R = 0.75, RMSE = 3.11°C). Including the canopy's vertical layers in analyses is pivotal, with the upper canopy layers exerting the most significant influence on reducing LSTs. (3) The scale of the grid impacts the accuracy of LST fitting, showing a trend where accuracy increases and then decreases with increasing grid scale; at the 40-m scale, the landscape indices demonstrate their highest explanatory capacity for LST (2D landscape index R=0.43, RMSE=4.65°C; gradient-based landscape index R=0.56, RMSE=4.07°C; voxel-based landscape index R=0.68, RMSE=3.94°C; vertical stratification (VS) voxel-based landscape index R=0.75, RMSE=3.30°C.). (4) Volume, proportion of volume, surface area, and diversity represent the parameters that most significantly influence variations in LST. Notably, volume, proportion of volume, and surface area exhibit a significant negative correlation with temperature, whereas diversity displays a distinct positive correlation. For the whole canopy at the optimal scale of 40 meters, a volume within 4200 m3, proportion of volume within 0.8, and a surface area within 18000 m2 are associated with a cooling effect. For the upper canopy, volume within 1200 m3, proportion of volume within 0.22, and surface area within 2000 m2 are associated with a cooling effect. This study unequivocally confirms the feasibility of using drones with LiDAR and thermal infrared sensors to analyze small-scale UHI issues. This approach is beneficial for describing the 3D structure of a forest and fitting surface temperature. Urban planners can utilize these findings in practical applications by prioritizing forest configurations with optimal 3D structures in their planning efforts to effectively mitigate UHI effects. This research provides groundbreaking methods and highly reliable data to significantly deepen our understanding of the mechanisms behind the UHI effect.