Research on the influence of thermal radiation of tree canopies to adjacent exterior walls has relevance to the selection of tree species and the spatial arrangement of trees for urban planning. In the last decade, there have been many studies on the influence of tree shadows on the thermal environment and energy consumption of buildings. However, there is a lack of research on how trees affect the thermal radiation of adjacent buildings, when they do not cast direct shadows on the walls. In view of this, a combination of experiment and simulation was used to explore the influence of spherical canopy on the intensity changes of net long-wave thermal radiation (T_RDL) and net short-wave thermal radiation (T_RDS) absorbed by the adjacent wall. Both measured and simulated results show that the tree canopy has a T_RD (the sum of T_RDL and T_RDS) effect on the south wall of adjacent buildings in summer. The peak of T_RD from the tree to the adjacent wall was obtained by ENVI-met under 27 scenarios. A functional relationship was further given between the peak T_RD and the canopy diameter (D_C), the minimum distance between wall and tree canopy (D_W-T). Moreover, the influence of D_C, D_W-T and leaf area density (LAD) on T_RD was discussed by simulation. Additionally, the T_RD of canopy decays exponentially in the horizontal direction and linearly in the vertical direction of the wall. The above methods and results can guide the selection of tree species, green space design around buildings and the evaluation of the influence of trees on indoor cooling energy consumption in summer.

Shape coefficient (SC) is an important factor affecting energy consumption of residential buildings. Due to lack of suitable and efficient methods for building three-dimensional data acquisition, there are few studies on the regional characteristics of rural residential SC. In view of this, taking the rural residential buildings in Hangzhou as the case, a new analysis method for regional similarity of rural residential shape coefficient at the municipal level is presented in this paper. This method contains four steps of work. Firstly, the applicability tests need to be conducted for the selected remote sensing images in Google Earth. Secondly, Hangzhou is divided into several sub-regions and the sample villages are selected in each sub-region. Thirdly, SC of rural residences in each sample village can be obtained based on Google Earth. Lastly, regional similarity of residential SC can be analyzed at municipal level by cluster analysis method. The case study shows that there are obvious regional similarity characteristics for the SC of rural residences. When the clustering distance is 10, the sample villages can be divided into three types, namely type A villages, type B villages and type C villages which have different mean and standard deviation. The new method makes it feasible to assess the geographical distribution characteristics of SC of a certain type of buildings, which can provide basic reference data not only for policymakers to make the most suitable building retrofit policy but also for building designers to conduct energy efficiency designs at regional scale.