Environmental assessment of land surface temperature in street spaces using satellite remote sensing

Abstract

Urban greenspaces , such as street trees are gaining attention for their potential to mitigate the urban heat island effect. However, in Japan, it is difficult to integrate such greenery due to budget constraints and a shortage of maintenance personnel. In this context, incorporating them into urban areas through appropriate strategies based on their evidence-based functions and values is key to maximizing their effectiveness.

Although some existing research has identified the relationship between vegetation and land surface temperature (LST), the relationship within street spaces has not been well examined. In addition, previous studies suggested that artificial elements such as buildings’ height(BH) affect LST, their effects within street spaces has not been clearly identified.

Therefore, this study aims to identify how vegetation(NDVI), buildings’ height(BH) along the street, and the shadows they cast(BS) affect LST within street spaces through quantitative analysis using measured data obtained via remote sensing.

In this study, we found four key points. First, in general, more vegetation leads to lower LST. However, when buildings create shade, the effect varies depending on the situation. Second, we believe buildings influence LST through their shading effect, but there is no simple correlation — for example, larger buildings do not necessarily lead to lower LST. The impact depends on the level of urban development (e.g., land cover conversion or building height conditions). Third, we confirmed that street trees play a more important role in terms of LST deduction at the street-space scale than at the city scale, since the effect of NDVI is stronger in street-space scale than in the city scale. Fourth, we confirmed that, by using a sufficient amount of data, analyses at the road-space scale can reveal trends similar to those observed at the macro-scale city level. This suggests that remote sensing is a useful tool for analyzing environmental conditions in road spaces.