Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE

Zhang, Hua; Yin, Yuxin; An, Hong‐Wei; Lei, Jinping; Li, Ming; Song, Jinyue; Han, Wuhong

doi:10.1007/s11356-022-21452-y

Cited by 17 publications

(8 citation statements)

References 48 publications

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“…The four subregions selected are the hotspots of anthropogenic land use and management changes. Specifically, HHHP and MYP include three of China's five major urban agglomerations, which have undergone rapid expansion in recent 20 years (H. Zhang, Yin, et al., 2022). NP, HHHP, and MYP are the three major grain‐producing regions of China and have experienced extensive agricultural practices (D. Yu et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

Li,

et al. 2023

JGR Atmospheres

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Over the past decades, human activities have directly or indirectly driven the land surface changes in eastern China. These anthropogenic forcings could trigger biochemical feedback and alter the surface biophysical properties, thus affecting local temperature. However, the latter is recognized as the “noise” and ignored when assessing historical or future climate. Here, we adopt the “observation minus reanalysis” (OMR) method to isolate the biophysical temperature footprint of multiple surface changes in eastern China over 2001–2018, using remote sensing land surface temperature and reanalysis skin temperature. A spatial regression model was used to separate the contributions from different processes. We find the biophysical feedbacks of surface changes have an annual cooling effect of −0.072 K/decade in eastern China, and the contributions from urban expansion, agricultural development, and natural vegetation greening are 0.042, −0.042, and −0.072 K/decade, correspondingly. The Northeast Plain shows agricultural activities induced cooling of −0.040 K/decade; the Loess Plateau shows natural vegetation recovery dominated cooling of −0.145 K/decade; the Huang‐Huai‐Hai Plain demonstrates a predominant urbanization warming effect of 0.124 K/decade; the Middle‐lower Yangtze shows natural vegetation greening related cooling of −0.106 K/decade. Both the intensity of the land surface changes and the temperature sensitivities drive the large spatial variability of the temperature effect. Overall, the temperature effects of surface changes are spatially heterogeneous and show considerable magnitudes. We emphasize that vegetation changes in eastern China show a strong surface cooling effect, and may contribute to the regional climate mitigation in the context of global warming.

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Section: Methodsmentioning

confidence: 99%

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

Li,

et al. 2023

JGR Atmospheres

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“…Many scholars have analyzed the pattern and form [16,17], change process [16,18], and influencing factors [19,20] of urban heat islands through observations from remote sensing sensors of Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging (MODIS), Enhanced Thematic Mapper Plus (ETM+), and so on. Studies on the spatial changes of urban heat islands have mainly used correlation analysis [21], the section line method [22], the triangle method [23], and the fractal method [24]. Streutker [25] fitted the spatial distribution of urban heat islands to a Gaussian surface and found that urban heat island size was negatively correlated with rural temperature, but the spatial extent was independent of both levels, providing new perspectives for exploring the spatial trend and continuity of urban heat islands.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

Yuan,

Zhang,

Fan

et al. 2024

Applied Sciences

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Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

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“…Numerous studies have explored the factors and mitigation strategies for the urban heat island effect. Land cover has an obvious relationship with urban heat island [17], and urban green space has been found to effectively lower surface temperature and mitigate the heat island effect [18]. Using geographically weighted regression (GWR) to consider the effect of spatial correlation, Elijah A. Njoku and David E. Tenenbaum [19] found a significant relationship between land use/land cover and land surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

Chen

Wang

et al. 2023

Remote Sensing

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The spatial scaling of patterns and processes is a hot topic of research in landscape ecology, and different scales may yield completely inconsistent results. Therefore, to understand the impact of the scale effect on urban heat island effect, this study analyzes the correlation between surface temperature and landscape index at different spatial scales over Nanjing. The scale effect is calculated thorough curve fitting of the Pearson’s correlation coefficient between ten landscape indices and land surface temperature at different window sizes, and the optimal one is determined. We have found that landscape indices can be divided into exponential and Gaussian landscape indices whose correlation with land surface temperature at different windows conforms to binomial exponential or multi-Gaussian functions, respectively. The optimal window size is approximately 4000–5100 m for exponential landscape indices, 1000–2000 m for aggregation index (AI) and percentage of like adjacencies (PLADJ), 6330 m for contagion (CONTAG) and 4380 m for total edge contrast index (TECI). Moreover, CONTAG and TECI have a high correlation coefficient plateau where the Pearson correlation coefficient is high and changes by less than 0.03 as the window size changes by more than 3000 m, which makes it possible to decrease the window size in order to save the calculation time without an obvious decrease in the Pearson correlation coefficient. To achieve this, we proposed a suitable window selection function so that the window size becomes 4260 m and 2070 m, respectively. The window sizes obtained in this study are just suitable in Nanjing, but the window sizes in other cities can also be obtained by the method in this study. This study provides a reference for future research on the relationship between landscape pattern and land surface temperature and its driving mechanisms, as well as for the impact of urban land use planning on the heat island effect.

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Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE

Cited by 17 publications

References 48 publications

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

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