How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago

Coseo, Paul; Larsen, Larissa

doi:10.1016/j.landurbplan.2014.02.019

Cited by 292 publications

(149 citation statements)

References 37 publications

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“…The UHI of big cities has increased gradually since the last few decades (Akbari, Pomerantz, & Taha, 2001;Oke, 1976;Stone, 2007) with urban concentrations generating modelled and observed changes in regional temperatures (Georgescu, Moustaoui, Mahalov, & Dudhia, 2011;He, Liu, Zhuang, Zhang, & Liu, 2007;Kalnay & Cai, 2003;Li, Wang, Shen, & Song, 2004). The relationship between landscape pattern and UHI becomes globally considerable (Chen, Yao, Sun, & Chen, 2014;Coseo & Larsen, 2014;Du et al, 2016a;Du, Xiong, Wang, & Guo, 2016b;Li, Song, Cao, Meng, & Wu, 2011;Peng, Xie, Liu, & Ma, 2016). A large number of studies considered that the built-up area and bare land accelerate the effect of UHI, whereas green space and water reduce the UHI intensity (Amiri, Weng, Alimohammadi, & Alavipanah, 2009;Song, Du, Feng, & Guo, 2014).…”

Section: Introductionmentioning

confidence: 99%

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Guha

Govil

Dey³

et al. 2018

European Journal of Remote Sensing

401

153

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The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU-LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Guha

Govil

Dey³

et al. 2018

European Journal of Remote Sensing

401

153

View full text Add to dashboard Cite

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“…In many works, such higher-level attributes are computed after extracting features inside the blocks through a land cover classification procedure [12,[19][20][21]. This approach has been adopted in order to perform block-based UST and urban land use classification using multispectral [12,22] and hyperspectral images [11,23], as well as LiDAR data [11,15].…”

Section: Descriptive Attributes For Classifying Urban Structure Typesmentioning

confidence: 99%

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

Novack

Stilla

2018

Remote Sensing

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Up-to-date maps of a city's urban structure types (USTs) are very important for effective planning, as well as for different studies and applications. We present an approach for the classification of USTs at the level of urban blocks based on high-resolution spaceborne radar imagery. Images obtained at the satellite's ascending and descending orbits were used for extracting line and polygon features from each block. Most of the attributes considered in the classification concern the geometry of these features, as well as their spatial disposition inside the blocks. Furthermore, assuming the UST classes of neighboring blocks are probabilistically dependent, we explored the framework of probabilistic graphical models and propose different context-based classification models. These models differ with respect to (i) their type, i.e., Markov or conditional random fields, (ii) their parameterization and (iii) the criterion applied for establishing pairwise neighboring relationships between blocks. In our experiments, 1,695 blocks from the city of Munich (Germany) and five representative UST classes were considered. A standard classification performed with the Random Forest algorithm achieved an overall accuracy of nearly 70%. All context-based classifications achieved overall accuracies up to 7% higher than that. The results indicate that denser pairwise block-neighborhood structures lead to better results and that the accuracy improvement is higher when the strength of the contextual influences is proportional to the similarity of the neighboring blocks attributes.

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“…A modelling study in Washington, DC found that the additional tree shading and evapotranspiration provided by vegetation in an urban area decreased surface air temperatures in urban street canyons by 4.1 • C, road surface temperatures by 15.4 • C, and building wall temperatures 8.9 • C (Loughner et al, 2012). Coseo and Larsen (2014) also found that neighbourhoods with a higher percentage of impermeable surface cover, as compared to those with higher canopy cover, had higher nighttime surface temperatures. The two factors explained 68% of the temperature variance overnight between the two neighbourhoods, which increased to 91% during EHEs (Coseo and Larsen, 2014).…”

Section: Relationship Between Canopy Cover and Heat-related Morbiditymentioning

confidence: 99%

The relationship between neighbourhood tree canopy cover and heat-related ambulance calls during extreme heat events in Toronto, Canada

Graham

Vanos

Kenny

et al. 2016

Urban Forestry & Urban Greening

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The relationship between neighbourhood tree canopy cover and heat-related ambulance calls during extreme heat events in Toronto, Canada Two thirds of Canadians reside in urban areas and 85% of recent population growth occurs in these areas. The intensity and duration of extreme hot weather events are predicted to increase in Canadian cities and in cities globally. It is well established that human suffering due to extreme heat is exacerbated in urban as compared to rural environments. Understanding the characteristics of urban landscapes that play the greatest roles in exacerbating the human health impact of extreme heat is thus imperative. This study explores the relationship between the amount of canopy cover from trees and the incidence of heat-related morbidity during extreme heat events in 544 neighbourhoods of Toronto, Ontario, Canada. Four extreme heat events from three years were studied. Heat-related ambulance calls were found to be 12.3% higher during the heat events than in the preceding or the following week. The number of heat-related ambulance calls was negatively correlated to canopy cover (Spearman Rank rho = −0.094, p = 0.029) and positively correlated to hard surface cover (Spearman Rank rho = 0.150, p < 0.001). Toronto neighbourhoods, as defined by Census Tracts, with less than 5% canopy cover had approximately five times as many heat-related calls as those with greater than 5% tree canopy cover, and nearly fifteen times as many heat-related calls as Census Tracts with greater than 70% tree canopy cover. These data suggest that even a marginal increase in the tree canopy cover from <5% to >5% could reduce heat-related ambulance calls by approximately 80%. These results have important implications for human health during heat events, particularly in the context of global climate change and urban heat islands, both of which are trending toward hotter urban environments in future.

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How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago

Cited by 292 publications

References 37 publications

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

The relationship between neighbourhood tree canopy cover and heat-related ambulance calls during extreme heat events in Toronto, Canada

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