Land use/land cover change and surface urban heat island intensity: source–sink landscape-based study in Delhi, India

2019 ◽  
Vol 22 (8) ◽  
pp. 7331-7356 ◽  
Author(s):  
Suvamoy Pramanik ◽  
Milap Punia
2007 ◽  
Vol 90 (3-4) ◽  
pp. 217-226 ◽  
Author(s):  
J. F. He ◽  
J. Y. Liu ◽  
D. F. Zhuang ◽  
W. Zhang ◽  
M. L. Liu

Urban Climate ◽  
2022 ◽  
Vol 41 ◽  
pp. 101052
Author(s):  
Shahfahad ◽  
Mohd Waseem Naikoo ◽  
Abu Reza Md. Towfiqul Islam ◽  
Javed Mallick ◽  
Atiqur Rahman

Author(s):  
Shahfahad ◽  
Mohd Rihan ◽  
Mohd Waseem Naikoo ◽  
Mohd Akhter Ali ◽  
Tariq Mahmood Usmani ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1678 ◽  
Author(s):  
Yan-jun Guo ◽  
Jie-jie Han ◽  
Xi Zhao ◽  
Xiao-yan Dai ◽  
Hao Zhang

In this study, 167 land parcels of downtown Shanghai, China, were used to investigate the relationship between parcel-level land use/land cover (LULC) components and associated summertime intra-surface urban heat island (SUHI) effect, and further analyze the potential of mitigating summertime intra-SUHI effect through the optimized LULC components, by integrating a thermal sharpening method combining the Landsat-8 thermal band 10 data and high-resolution Quickbird image, statistical analysis, and nonlinear programming with constraints. The results show the remarkable variations in intra-surface urban heat island (SUHI) effect, which was measured with the mean parcel-level blackbody sensible heat flux in kW per ha (Mean_pc_BBF). Through measuring the relative importance of each specific predictor in terms of their contributions to changing Mean_pc_BBF, the influence of parcel-level LULC components on excess surface flux of heat energy to the atmosphere was estimated using the partial least square regression (PLSR) model. Analysis of the present and optimized parcel-level LULC components and their contribution to the associated Mean_pc_BBF were comparable between land parcels with varying sizes. Furthermore, focusing on the gap between the present and ideally optimized area proportions of parcel-level LULC components towards minimizing the Mean_pc_BBF, the uncertainties arising from the datasets and methods, as well as the implications for sustainable land development and mitigating the UHI effect were discussed.


2020 ◽  
Vol 9 (10) ◽  
pp. 568
Author(s):  
Ying-ying Li ◽  
Yu Liu ◽  
Manjula Ranagalage ◽  
Hao Zhang ◽  
Rui Zhou

In this study, a retrospective analysis of the relationship between the land use/land cover (LULC) change and associated surface urban heat island (SUHI) effect in fast-growing Greater Hefei between 1995 and 2016 was performed. Our results reveal the heterogeneous patterns of LULC change. The concentric buffer-based urban–rural gradient analysis reveals that most of the newly emerging developed land occurred within downtown Hefei. In contrast, in three suburban municipality/county jurisdictions, the overall area change in the non-developed land was much lower, but the net increase in developed land is remarkable. Meanwhile, the spatiotemporal patterns of SUHI are in good agreement with that of the developed land, as evidenced by the notable increase in SUHI intensity (SUHII) levels and SUHI spatial extent (SUHISE) in response to the rapid urban expansion, particularly along transportation corridors. In addition, partial least square regression (PLSR) models indicate that the buffer-based predictors/independent variables are significantly related to the responses (SUHII and SUHISE), explaining approximately 61.3% of the variance in the SUHII and 79.8% of the variance in the SUHISE, respectively. Furthermore, the relative strength of the independent variables in determining the relationship was quantitatively examined. The findings of this study provide clear evidence for decision making for sustainable land development and mitigation of the SUHI effect.


Author(s):  
Tao Chen ◽  
Anchang Sun ◽  
Ruiqing Niu

Man-made materials now cover a dominant proportion of urban areas, and such conditions not only change the absorption of solar radiation, but also the allocation of the solar radiation and cause the surface urban heat island effect, which is considered a serious problem associated with the deterioration of urban environments. Although numerous studies have been performed on surface urban heat islands, only a few have focused on the effect of land cover changes on surface urban heat islands over a long time period. Using six Landsat image scenes of the Metropolitan Development Area of Wuhan, our experiment (1) applied a mapping method for normalized land surface temperatures with three land cover fractions, which were impervious surfaces, non-chlorophyllous vegetation and soil and vegetation fractions, and (2) performed a fitting analysis of fierce change areas in the surface urban heat island intensity based on a time trajectory. Thematic thermal maps were drawn to analyze the distribution of and variations in the surface urban heat island in the study area. A Multiple Endmember Spectral Mixture Analysis was used to extract the land cover fraction information. Then, six ternary triangle contour graphics were drawn based on the land surface temperature and land cover fraction information. A time trajectory was created to summarize the changing characteristics of the surface urban heat island intensity. A fitting analysis was conducted for areas showing fierce changes in the urban heat intensity. Our results revealed that impervious surfaces had the largest impacts on surface urban heat island intensity, followed by the non-chlorophyllous vegetation and soil fraction. Moreover, the results indicated that the vegetation fraction can alleviate the occurrence of surface urban heat islands. These results reveal the impact of the land cover fractions on surface urban heat islands. Urban expansion generates impervious artificial objects that replace pervious natural objects, which causes an increase in land surface temperature and results in a surface urban heat island.


2017 ◽  
Vol 9 (2) ◽  
pp. 312 ◽  
Author(s):  
Chaobin Yang ◽  
Xingyuan He ◽  
Fengqin Yan ◽  
Lingxue Yu ◽  
Kun Bu ◽  
...  

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