Analysis of Land Surface Temperature Driving Factors and Spatial Heterogeneity Research Based on Geographically Weighted Regression Model

Abstract

Acceleration of urbanization has brought about a series of problems, which include irreversible changes to urban surfaces and continuous increases in land surface temperatures (LSTs). In this context, analysis of the driving factors and spatial heterogeneity of urban LST is of considerable importance for mitigating urban heat island effects and promoting healthy and comfortable urban living environments. This study explored the relationship between the spatial characteristics and driving factors of the LST by using a geographically weighted regression (GWR) model to analyze multisource data from the Xigang District of Dalian City. The results showed that the urban heat island effect in Xigang District is significant, with LSTs generally above 28°C at the end of August, mostly concentrated in the range of 38–40°C. The highest LST values were detected in northern port and harbor areas; the lowest LST values occurred in mountainous forest areas. The global Moran’s I value was 0.994, which was indicative of a very high positive correlation, and local Moran’s I values formed H-H and L-L type clusters concentrated in the northern harbor area and southern mountainous area, respectively. Finally, the GWR model could reflect the spatial heterogeneity of the relationships between LST and its driving factors well. Among these, in terms of natural physical factors, digital elevation model, normalized difference vegetation index, and modified normalized difference water index data were found to be negatively correlated with LSTs in most cases; in the social dimension, the point-of-interest number and building-coverage ratio were generally positively correlated with LSTs.

Evaluating the impacts of land use/land cover changes across topography against land surface temperature in Cameron Highlands

“…The results obtained indicated a similarity to those of the study of Phan et al [64], which was conducted in a mountainous region of Vietnam when both MODIS and Landsat datasets were averaged as well. Rising temperatures not only cause hardships for agriculture but also negatively impact the tourism sector [64][65][66].…”

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

“…The correlation coefficient between NDVI and LST also indicates strong negative influences on LST dynamics. Previous studies have confirmed the significant inverse influences of water and vegetation dynamics on surface temperature ( Zhi et al., 2020 ). The positive correlations between the NDBAI and LST (Pearson's r = +0.4643, +0.4899 and +0.47701) indicate that the increase of bare lands has been accelerating the surface temperature of Dhaka.…”

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

“…The results show that the interactions between MNDWI, IBI, and NDVI were the main factors influencing the variations of Wuhan's LST in each season. Previous studies [43,[87][88][89] have confirmed that physical factors such as water and vegetation play an important role in mitigating SUHI by using the remote sensing index (MNDWI and NDVI). Similarly, there is no hightemperature agglomeration in Wuhan's water area and forest land.…”

“…With the acceleration of urbanization and industrialization, the influence of socioeconomic factors on LST variations is becoming more and more important [88,91]. The impact of socio-economic factors, such as anthropologic heat releases and build-up intensity, on the spatial distribution pattern of SUHI were confirmed in China's 32 major cities [92].…”