Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city's thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001-2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns.

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.

Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia.

Due to the intensity of urban development around the world, there is an increasing body of studies attempting to investigate urban heat island (UHI) in various spatial and temporal scales. In surface heat urban island (SUHI) studies, extended periods of time, broader regions and local government area (LGA) level have become more crucial and will shed light on causes of UHI. Moreover, the spatial pattern and structure of SUHI will be useful for policy-makers to develop mitigation strategies. This study focused on three objectives. Firstly, analyzing land surface temperature (LST), normalized difference built-up (NDBI) and vegetation (NDVI) indices. Secondly, investigating interrelationships among LST, NDVI, and NDBI. Thirdly, identifying LST patterns in the Melbourne metropolitan area. These objectives were achieved through three different methods. The modified automatic mapping method for the first objective, the correlation analysis for the second, and spatial statistical methods for the third. The methodological innovations of this study were considering LGA in interrelationship analysis among LST, NDBI and NDVI, and calculation of NDVI for each acquisition date. The results indicated that the clustering pattern of LST expanded toward the north-west and south-east during the period of the study. Furthermore, the north-west part of the city has the highest positive (0.6) correlation between NDBI and LST, and the south-east part of the city has the lowest negative (-0.8) correlation between NDVI and LST. The most significant increase and decrease in mean LST happened respectively from January 6th to 22nd 2017, and January 14th to 30th January 2014. The temperature degree altered from 19.61 °C to 27.86 °C in inner western suburbs, and from 35.49 °C to 26.88 °C in most LGA's. These findings are critical for planners to localize UHI mitigation action plans, target hot spots in LGA's and allocate resources to respond to the adverse effect of UHI.

Study of the Relationships between the Spatial Extent of Surface Urban Heat Islands and Urban Characteristic Factors Based on Landsat ETM+ Data.

Ten cities with different population and urban sizes located in the Pearl River Delta, Guangdong Province, P.R. China were selected to study the relationships between the spatial extent of surface urban heat islands (SUHI) and five urban characteristic factors such as urban size, development area, water proportion, mean NDVI (Normalized Vegetation Index) and population density, etc. The spatial extent of SUHI was quantified by using the hot island area (HIA). All the cities are almost at the same latitude, showing similar climate and solar radiation, the influence of which could thus be eliminated during our computation and comparative study. The land surface temperatures (LST) were retrieved from the data of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) band 6 using a mono-window algorithm. A variance-segmenting method was proposed to compute HIA for each city from the retrieved LST. Factors like urban size, development area and water proportion were extracted directly from the classification images of the same ETM+ data and the population density factor is from the official census. Correlation and regression analyses were performed to study the relationships between the HIA and the related factors, and the results show that HIA is highly correlated to urban size (r=0.95), population density (r=0.97) and development area (r=0.83) in this area. It was also proved that a weak negative correlation existed between HIA and both mean NDVI and water proportion for each city. Linear functions between HIA and its related factors were established, respectively. The HIA can reflect the spatial extent and magnitude of the surface urban heat island effect, and can be used as reference in the urban planning.