An Assessment of Urban Surface Energy Fluxes Using a Sub-Pixel Remote Sensing Analysis: A Case Study in Suzhou, China

Li, Kai; Fang, Jun-yong; Dong, Zhao; Li, Xue; Zhang, Xiaohong; Wang, Xiao; Li, Xueke

doi:10.3390/ijgi5020011

Cited by 19 publications

(9 citation statements)

References 70 publications

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“…More importantly, our sector-based LST gradients reflect spatial variations that help to identify the dominant factors of the LST at Suzhou. Using Suzhou as the case study area, Liu et al [68] found a positive correlation between LST and the ISA in 2010, and a negative correlation between LST and NDVI, consistent with our results.…”

Section: Discussionsupporting

confidence: 91%

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Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

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Section: Discussionsupporting

confidence: 91%

“…The LST in Suzhou and its influencing factors have been studied previously [67,68]. In these studies, Zhang et al [67] used MODIS images to derive LST and investigate diurnal changes of SUHI intensity, in July 2007.…”

Section: Discussionmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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“…In the literature, several studies have modeled the urban energy fluxes using a remotely-sensed approach [66][67][68][132][133][134][135][136][137][138]; however, only few of them made synchronous measurements of the turbulent fluxes available for validation purposes. Here, the obtained accuracy is considered to be consistent with the reported errors in recent studies of long-term, remotely-sensed turbulent fluxes [68,81,86].…”

Section: Discussionmentioning

confidence: 99%

Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Agathangelidis

Cartalis

Santamouris

2019

Climate

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Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top-down and bottom-up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.These four decisive parameters for the urban climate (H/W, β, ∆Q S , Q F ) are estimated in this work using meteorological, earth observation, and geographical information system (GIS) data; the Climate 2019, 7, 75 3 of 28 different variables are subsequently incorporated in the UHeatEx composite indicator via principal component analysis (PCA). An additional mapping of the study area using the LCZ scheme is applied, and similarities and differences between UHeatEx and LCZs are discussed. Both results are correlated with air temperature measurements from a network of automatic weather stations (AWSs) in the area under examination, for a six-year period (the warm period months). A qualitative and quantitative analysis examines the presence of thermal inequity in Athens and its relationship with socioeconomic characteristics. The growing importance of thermal indicators in sustainable urban planning, due to climate change, is demonstrated by presenting the simulated future conditions of the city. To this end, high-resolution climate projections from an ensemble of regional climate models (RCMs) were used.

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“…Wang, Hu, Chen and Yu [38] showed that high-value AHF areas are mainly distributed at airports, railway stations, industrial areas, and commercial centers. Previous studies have shown that AHF differs across different land covers and is highly correlated with impervious surface cover (ISC) [38,[41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

et al. 2020

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The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.

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An Assessment of Urban Surface Energy Fluxes Using a Sub-Pixel Remote Sensing Analysis: A Case Study in Suzhou, China

Cited by 19 publications

References 70 publications

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

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