The spatial relationship between seasonal surface temperature and landscape pattern of the urban agglomeration on the northern slope of the Tianshan Mountains
The spatial relationship between seasonal surface temperature and landscape pattern of the urban agglomeration on the northern slope of the Tianshan Mountains
Exploring the spatial relationship between landscape pattern and land surface temperature(LST)in arid regions is of great significance for promoting the stable development of ecological environment in arid regions.Based on Google Earth Engine(GEE),we examine the temporal and spatial distribution characteristics of day and night and seasonal(daytime)LST in the urban agglomeration on the northern slope of the Tianshan Mountains from 2003 to 2020,and use the Mann-Kendall nonparametric test and Sen's slope and Hurst index to explore the change trend of LST and predict future development direction,adopt standard deviation ellipse and gravity center migration model to analyze the spatial migration characteristics of LST,apply bivariate spatial autocorrelation and multi-scale geographically weighted regression(MGWR)model to identify the spatial relationship between LST and landscape pattern.The results show that from 2003 to 2020,the average LST of the study area is 23℃ during the day and-0.5℃ at night.The future change trend of LST shows that the temperature rise rate at night will be higher than that in daytime,that is,the phenomenon that the intensity of cold island is higher during the day than at night will be alleviated.The center of gravity of the medium and low temperatures,which represent construction land and vegetation,is observed in Urumqi.Patch Density(PD),Edge Density(ED),and Mean Shape Index(SHAPE_MN)were spatially negatively correlated with LST,while Largest Path Index(LPI),Mean Patch Size(AREA_MN),Agglomeration Index(AI),and Percent of Landscape(PLAND)were spatially positively correlated with LST,indicating that patches of aggregated and continuous landscapes had higher LST.Comparing the OLS,GWR and MGWR models,we found that the MGWR model has the best fitting effect,the effects of ED and AI on the LST are the most significant on the right side of the study area,and the effect of PLAND on the LST is the most significant in the central-south part of the study area,and other landscape pattern indices have a moderate impact intensity.
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