# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 5 Conclusions  
Based on the Gaussian capacity model, we used MODIS surface temperature products for Beijing from 2003 to 2020 to compare and analyze the intensity, center, direction, and range of SUHI and CLHI in Beijing through model parameters. The main conclusions are as follows:  
1. The monthly mean heat island intensities of SUHI and CLHI show a V-shaped distribution at night, with the strongest intensity in winter and the weakest in summer, and an inverted V-shaped distribution during the day, with the strongest intensity in summer and the weakest in winter.
2. The centers of SUHI and CLHI are concentrated at night, with longitude mainly in the range of 116.3\({}^{\circ}\)-116.4\({}^{\circ}\) E, and the latitude mainly in the range of 39.90\({}^{\circ}\)-39.95\({}^{\circ}\) N; they are scattered during the day, with longitude mainly in the range of 116.2\({}^{\circ}\)-116.5\({}^{\circ}\) E, and latitude mainly in the range of 39.7\({}^{\circ}\)-40.0\({}^{\circ}\) N. They move eastward and northward during hot seasons, and they move westward and southward during cold seasons.
3. The monthly average ellipse area of daytime SUHI and CLHI varies greatly among months; the corresponding maximum differences are 2662 km\({}^{2}\) and 2293 km\({}^{2}\), respectively. Meanwhile, nighttime values vary slightly; the corresponding maximum differences are 484 km\({}^{2}\) and 265 km\({}^{2}\), respectively.
4. The average area as a whole is on the rise. The annual average area of SUHI and CLHI has steadily increased over time; every year, the center of the heat island has moved eastward, and the direction of the heat island has gradually shifted towards the northeast-southwest direction.  
The expansion direction and scope of the urban area will continue to affect the movement and extent of the heat island center. It is expected that with the further development of the city, the urban heat island center will continue to move to the southeast, and the heat island scope will further expand. This study examined the urban heat island effect at both the surface and canopy levels using remote sensing data and Gaussian capacity modeling,  
Figure 16: Distribution of standard deviation ellipses and center of gravity offset trajectories for construction sites.  
offering insights for mitigating this phenomenon. However, it is important to note that our analysis only considers the characteristics of the heat island effect from a remote sensing perspective. Future research should integrate multiple sources of information, including ground observation data, meteorological data, and socio-economic data, to comprehensively elucidate the formation mechanism and influencing factors of the urban heat island effect. Additionally, attention should be given to variations between different cities in order to develop targeted strategies for mitigating the urban heat island.  
Conceptualization, D.Y.; methodology, D.Y., L.Z. and W.S.; software, Y.F.; validation, D.Y. and Y.F.; formal analysis, D.Y., L.Z. and D.F.; investigation, D.Y.; resources, data curation, W.S.; writing--original draft preparation, D.Y., L.Z. and Y.F.; writing--review and editing, D.F. and X.Z.; visualization, supervision, project administration, funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hebei Natural Science Foundation Ecological Smart Mine Joint Fund (No. E2020402086) and the National Natural Science Foundation of China (No. 52174160).  
Not applicable.  
The data presented in this study are avalable on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.