3D compact form as the key role in the cooling effect of greenspace landscape pattern

Abstract

Climate change and urbanization have led to an increase in extreme weather and urban heat islands. Green space can help regulate the thermal environment, but previous studies have focused on two-dimensional (2D) indicators, neglecting the intrinsic three-dimensional (3D) characteristics of green space. Therefore, we developed a set of 3D landscape indices to define green space configurations, including characteristic, shape, and compactness, based on landscape ecology and the law of universal gravity, and quantify its benefits for the thermal environment. We collected high-resolution LiDAR point cloud data from Siming Mountain, China, to calculate both 2D and 3D green space landscape patterns and investigate their effects on land surface temperature (LST) using correlation analysis and spatial statistical methods. Our study revealed: (1) All the selected green space landscape indices had a significant negative effect on LST, with a non-linear enhancement when 2D or 3D indices interacted. (2) The 3D compactness index (VCI) could be the most reliable and concise index to explain LST spatial distribution. Adjusting the VCI allowed us to reduce the LST of the study area by up to 3.71 °C in summer and 2.14 °C in winter. However, an overly compact 3D green space above the threshold could lead to higher LST in summer. (3) Terrain had a weaker effect on LST in winter than in summer, and it showed a stronger interaction with the 3D form of green space than with the 2D form. We concluded that the 3D compact form of green space can be utilized as the key factor in maximizing its cooling effect, by taking into account both terrain factors and seasonal variations. Our study can shed light on the scientific planning of green space aiming at optimizing the thermal environment for more sustainable forest and urban habitats.

Keywords

• 3D landscape indices
• LiDAR
• Landscape ecology
• LST
• Geographic detector