scholarly journals The Spatial Patterns of Land Surface Temperature and Its Impact Factors: Spatial Non-Stationarity and Scale Effects Based on a Geographically-Weighted Regression Model

2018 ◽  
Vol 10 (7) ◽  
pp. 2242 ◽  
Author(s):  
Hongbo Zhao ◽  
Zhibin Ren ◽  
Juntao Tan
Keyword(s):  
Surface Temperature ◽  
Regression Model ◽  
Spatial Patterns ◽  
Land Surface Temperature ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Scale Effects ◽  
Weighted Regression ◽  
Impact Factors ◽  
Geographically Weighted Regression Model

scholarly journals Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xue-Yuan Lu ◽  
Xu Chen ◽  
Xue-Li Zhao ◽  
Dan-Jv Lv ◽  
Yan Zhang
Keyword(s):  
Surface Temperature ◽  
Spatial Heterogeneity ◽  
Land Surface Temperature ◽  
Primary Productivity ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Weighted Regression ◽  
The Impact ◽  
Gwr Model

AbstractUrbanization had a huge impact on the regional ecosystem net primary productivity (NPP). Although the urban heat island (UHI) caused by urbanization has been found to have a certain promoting effect on urban vegetation NPP, the factors on the impact still are not identified. In this study, the impact of urbanization on NPP was divided into direct impact (NPPdir) and indirect impact (NPPind), taking Kunming city as a case study area. Then, the spatial heterogeneity impact of land surface temperature (LST) on NPPind was analyzed based on the geographically weighted regression (GWR) model. The results indicated that NPP, LST, NPPdir and NPPind in 2001, 2009 and 2018 had significant spatial autocorrelation in Kunming based on spatial analytical model. LST had a positive impact on NPPind in the central area of Kunming. The positively correlation areas of LST on NPPind increased by 4.56%, and the NPPind caused by the UHI effect increased by an average of 4.423 gC m−2 from 2009 to 2018. GWR model can reveal significant spatial heterogeneity in the impacts of LST on NPPind. Overall, our findings indicated that LST has a certain role in promoting urban NPP.

scholarly journals Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

2019 ◽  
Vol 11 (9) ◽  
pp. 1016 ◽  
Author(s):  
Huimin Liu ◽  
Qingming Zhan ◽  
Sihang Gao ◽  
Chen Yang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Seasonal Variations ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Urban Landscape ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Environment Interaction

There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

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