1. Introduction
Urban heat island (UHI) is used to describe a phenomenon where the temperature in urban areas is higher than that in surrounding suburbs [
1]. The surface UHI (SUHI) caused by urbanization has become a major problem affecting the development of the ecological environment [
2,
3,
4,
5]. It has aggravated air pollution [
6] and endangered human health [
7,
8]. How to alleviate it has attracted much attention.
It has been considered that the UHI is the result of the combined effect of anthropogenic heat and solar radiation on urban space [
6]. Obviously, controlling and optimizing urban morphology is an important way to alleviate the impact of the urban thermal environment and build a climate-friendly city [
7]. Therefore, many researchers take this as a focus to explore the relationship between urban morphology and the UHI in order to provide theoretical support for related planning and management. For example, Guo et al. [
8] pointed out that building height and building density can significantly affect the UHI, and the influence degree of building density is higher. Wang et al. [
9] studied the quantitative relationship between the spatial distribution of a water body and surface temperature to help us understand the cooling effect of a water body on the surrounding thermal environment. Yin et al. [
10] established the relationship between six morphological indexes, such as building density, impervious surface ratio, vegetation coverage, and surface temperature, by using spatial regression model.
The above research is of great significance to explore the formation mechanism of the UHI effect, but the conclusions are difficult to be transformed into specific and spatially targeted urban morphology regulation strategies, and they ignoredthe effect of spatial heterogeneity on the correlation between urban form and UHI intensity. The current research on the spatial distribution and variation characteristics of urban heat islands has achieved great results, but these studies on urban heat islands do not take into account the spatial variability of surface temperature. However, the linkage between elements at different scales in the urban area is significant, and the interdependence forms a complex system. It is particularly important to further understand the comprehensive action laws of different regional elements within the complex system. Therefore, some scholars try to divide the city into functional areas, such as commercial area, residential area, and industrial area, and further analyze the relationship between the urban morphology index and surface temperature on the basis of comparing the surface temperature of each functional area [
11]. Some scholars have also found that changes in land-use types have led to significant changes in the spatiotemporal pattern of the SUHI [
12]. Such research will help urban planners to assess and improve the heat island status of each functional area more specifically. However, criteria for urban functional zoning and land use are not strongly correlated with climate and urban form. Therefore, the proposed urban morphological regulation strategy, guided by mitigating the heat island effect, has strong uncertainty.
The LCZ (local climate zone) provides an opportunity to solve the above problems. It is a classification system proposed according to the difference of response ability of different urban form types to the thermal environment; cities can be divided into 10 types of building (LCZ1–LCZ10) and 7 types of natural coverage (LCZA–LCZG) [
13]. Similar LCZs have similar morphological characteristics and temperature attributes. Bechtel et al. [
14] used remote sensing image classification technology to develop the world urban database and access portal tool (WUDAPT) [
15], which promoted the dissemination and application of local climate areas. At present, research on the UHI based on the LCZ has been gradually carried out in cities around the world. Saliba et al. [
16] presented a methodology to assess the impact of Beirut’s urban structure on the UHI. Malley et al. [
17] identified urban forms that are prone to heat storage in Japan based on the LCZ. Researchers analyzed the distribution of the UHI based on the LCZ and found that different LCZs had different impacts on land surface temperature [
18]. In addition, different seasons will also lead to the difference of the UHI intensity of different LCZs [
19]. Through the study of urban agglomeration, it is found that the LCZ types of areas with high LST are consistent, indicating that the research results are universal [
20]. Simanjuntak et al. [
21] found that not only the type of LCZ but also the composition and configuration pattern of the LCZ significantly affected the surface temperature.
The above research reveals the close relationship between the SUHI and urban morphology from the temperature difference of each LCZ, but research on LCZ-SUHI is not deep and detailed enough, ignoring the spatial heterogeneity of the relationship between relevant driving factors and the SUHI, and there are relatively few studies on the influence of different urban morphology indices on the SUHI in different climate regions, which leads to the lack of guidance of the research results to the planning in the specific implementation. In addition, the time period selected in the above study to explore the influence of urban morphology on the heat island effect is daytime, so the conclusions obtained are only applicable to the improvement of the daytime thermal environment. Studies have shown that the intensity of the night UHI in some areas is stronger than that of daytime [
22], and the mitigation strategies for the night UHI need to be studied separately from those during the day. Therefore, in view of the shortcomings of the above research, this study adds spatial heterogeneity to the traditional heat island research, and further studies the differences between day and night urban heat islands. This study takes summer in Beijing as an example, explores differences in the driving indices of the SUHI in different LCZs, and develops an artificial neural network for SUHI distribution prediction through the combination of the LCZ and other indices. The improved model of the LCZ can more effectively predict and simulate the SUHI caused by urban planning and layout so that people can carry out reasonable urban planning and design, effectively reduce the SUHI in the region, and improve the livability and comfort of the city. The main contents of this study are as follows: (1) LCZ zoning based on the WUDAPT process and analyze the distribution of the SUHI in the study area during the day and night. (2) Build a parametric model to explore the influence of surface heat island intensity and parameters in different LCZs during the day and night. (3) Use city morphology indices to train an artificial neural network to predict the SUHI, and join the LCZ to improve the accuracy of the model and then analyze the prediction results. The research results of this study can provide a valuable reference for improving the urban thermal environment, community construction, and human environmental quality in future research.
4. Conclusions
Based on remote sensing images and building data, this study analyzed the spatial distribution characteristics of the day and night SUHI in Beijing and the factors affecting the intensity of the SUHI in each LCZ, and trained a neural network model to compare the prediction accuracy of the model with and without the LCZ. The conclusions are as follows:
(1) The super strong heat island areas in Beijing are concentrated in the central and western regions during the day. The night heat islands are scattered, and the SUHI is generally higher. SUHIN in Beijing is greater than SUHID. During the day and night, the super heat island zone mainly occurs in the compact high-rise zone (LCZ1), compact low-rise zone (LCZ3), and large low-rise building zone (LCZ8). There are big differences in the SUHI between different LCZs. The general rule of the day and night SUHI is compact building zone > large low-rise zone > open zone. Under the same density, the SUHI intensity shows the law of medium floor zone > low floor zone > high floor zone. At night, the SUHI of the open low-rise zone (LCZ6) decreases significantly, LCZ10 and LCZG increase significantly, and the SUHI of other climate zones increases.
(2) In terms of the overall correlation coefficient, the correlation between day and night is consistent, which shows that the correlation between the SUHI and building height and surface albedo is relatively low. There is a significant positive correlation with building density and impervious surface ratio, indicating that high-intensity land development can significantly enhance the heat island effect. There is a significant negative correlation with the NDVI, indicating that increasing vegetation cover in cities can effectively alleviate the heat island effect. It is found that the correlation between the SUHI and various indices is quite different in various LCZs, and the correlation of individual indices is different in the day and night. When formulating specific thermal environment optimization strategies, it should be adjusted according to the type of LCZ. As an example, the building density in the dense area (LCZ1–LCZ3) is more correlated than that in the open area (LCZ4–LCZ6); the building height is significantly negatively correlated with the SUHI in the high-rise building area, and it is positively correlated in the middle- and low-rise building area. Albedo has a greater correlation in dense areas during the day (LCZ1–LCZ3), while at night, the correlation between albedo and the SUHI decreases. In LCZ4–LCZ6, the correlation is not even significant. It shows that the use of high-albedo building materials can effectively reduce the surface heat island effect during the day, but the effect on the night heat island is limited.
(3) After adding the LCZ to the daytime model, the improved model’s ability to explain the SUHI distribution (R2) was significantly increased from 0.74 to 0.81, and the RMSE was reduced from 1.66, 1.70, and 1.76 to 1.05, 1.11, and 1.14 during training, verification, and testing, respectively, which shows that the addition of the LCZ improves the prediction accuracy of the model. The night model has a low R2 and a large error. After adding the LCZ, there is only a slight improvement, indicating that the selected city morphology indices are less explanatory for the night SUHI distribution than during the day. After adding the LCZ, the artificial areas with the best prediction effect during the day of the improved model are LCZ6 and LCZ8, and the areas with the best prediction effect at night are LCZ1 and LCZ5. In the daytime SUHI forecast, the average error of each area (except LCZT) increases after adding the LCZ. Among them, LCZ2, LCZ8, and LCZ10 are the most obvious improvement compared with other LCZ regions, indicating that the consideration of regional heterogeneity has significantly promoted the fitting and prediction of urban morphology and the SUHI in these regions. Compared with the daytime prediction results, the errors in all LCZs at night have increased. Even after the improvement, the error reduction is not obvious compared with that in the daytime. This shows that night prediction needs to consider more factors, and the LCZ can only slightly reduce the error. The model for predicting the distribution of the SUHI constructed by this research provides a reference for the planning of mitigating the intensity of the SUHI. It also illustrates the advantages and necessity of adding the concept of LCZs to consider spatial heterogeneity when analyzing and predicting the intensity of the SUHI.
Based on the above conclusions, it can be concluded that spatial heterogeneity plays an important role in the study of the heat island effect. Since the traditional correlation between SUHI intensity and urban form mainly focuses on the global linear regression model, less attention is paid to the spatial heterogeneity of the role of thermal environment factors in different regions. This study has made a systematic analysis of the relationship between spatial heterogeneity and the urban heat island. The correlation of the SUHI and urban form in different LCZs is analyzed, and the comparison between day and night is added. The test is carried out by inputting the regional classification into the neural network model. Through the analysis, it is found that considering the spatial heterogeneity, we can better understand the relationship between SUHI intensity and different urban morphological parameters in different morphological regions. According to the simulation results of the constructed model, the model with the LCZ can more accurately simulate the thermal environment, especially in the daytime. The above conclusions can provide some reference value for the related research of the SUHI.
This study also has some limitations. Because the research focuses on the SUHI in summer, the available remote sensing images are limited. The study of the SUHI in the day and night is only based on one image, which is lack of universality to a certain extent. Additionally, in the selection of urban form indicators, the types of indicators can continue to expand. Further research should screen and increase the indicators of the nighttime heat island effect, and study more influencing factors of the nighttime heat island. A series of studies will be conducted on more regions and seasons to explore universal laws and patterns. In view of the difference in the prediction effect of this model on the SUHI of different LCZs, it is necessary to further analyze the reasons.