Developing High-Resolution Descriptions of Urban Heat Islands: A Public Health Imperative

Voelkel, Jackson; Shandas, Vivek; Haggerty, Brendon

doi:10.5888/pcd13.160099

Cited by 20 publications

(12 citation statements)

References 7 publications

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“…Due to the high spatial resolution of the UHI surfaces, it is conceivable that a heat/health mitigation strategy could be enacted at a household scale, which could be conducted through information dissemination (e.g., pamphlets on cooling center locations). Highly accurate and spatially precise exposure information increases the likelihood of a successful overall health outcome for urban populations [8].…”

Section: Discussionmentioning

confidence: 99%

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Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Voelkel

Shandas

2017

Climate

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Abstract:While there exists extensive assessment of urban heat, we observe myriad methods for describing thermal distribution, factors that mediate temperatures, and potential impacts on urban populations. In addition, the limited spatial and temporal resolution of satellite-derived heat measurements may limit the capacity of decision makers to take effective actions for reducing mortalities in vulnerable populations whose locations require highly-refined measurements. Needed are high resolution spatial and temporal information for urban heat. In this study, we ask three questions: (1) how do urban heat islands vary throughout the day? (2) what statistical methods best explain the presence of temperatures at sub-meter spatial scales; and (3) what landscape features help to explain variation in urban heat islands? Using vehicle-based temperature measurements at three periods of the day in the Pacific Northwest city of Portland, Oregon (USA), we incorporate LiDAR-derived datasets, and evaluate three statistical techniques for modeling and predicting variation in temperatures during a heat wave. Our results indicate that the random forest technique best predicts temperatures, and that the evening model best explains the variation in temperature. The results suggest that ground-based measurements provide high levels of accuracy for describing the distribution of urban heat, its temporal variation, and specific locations where targeted interventions with communities can reduce mortalities from heat events.

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Section: Discussionmentioning

confidence: 99%

“…Modern advances in data capture and analysis seem to have increased interest in the subject, with calls for greater resolution and direct public action [8,9]. While numerous cities have empirically documented UHIs, the extant literature suggests extensive variation in the outreach and prevention of fatalities during heat events.…”

Section: Introductionmentioning

confidence: 99%

Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Voelkel

Shandas

2017

Climate

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“…There are both direct and indirect ways the LULCs affect UHI in an area, and the configuration of LULCs within varying distances and clusters is important to understand-previous analyses have described a distance-decay effect in relation to LULCs and their impacts on temperatures at a given location [28,29]. This is, in effect.…”

Section: Focal Buffersmentioning

confidence: 99%

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Shandas

Voelkel

Williams

et al. 2019

Climate

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The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

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“…Even if one took an area-weighted average of the two, would that be "representative"? A growing body of literature is showing that urban greenery and related urban heat mitigation are systematically more available to more affluent residents, whereas lower-income neighborhoods frequently have lower canopy cover and higher local temperatures [11,14,26,[75][76][77]. This raises the question, then, not only of how to choose an "urban" end member to be representative of the urban landscape, but also what is "representative-and for whom?"…”

Section: Discussionmentioning

confidence: 99%

“…We also encourage future research to ask "Representative for whom? ", being conscious that LCs, urban canopy, urban temperatures, and micrometeorological infrastructure [31] are not typically equitably distributed among city residents [11,14,26,[75][76][77].…”

Section: Discussionmentioning

confidence: 99%

Urban-Rural Surface Temperature Deviation and Intra-Urban Variations Contained by an Urban Growth Boundary

2019

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The urban heat island (UHI) concept describes heat trapping that elevates urban temperatures relative to rural temperatures, at least in temperate/humid regions. In drylands, urban irrigation can instead produce an urban cool island (UCI) effect. However, the UHI/UCI characterization suffers from uncertainty in choosing representative urban/rural endmembers, an artificial dichotomy between UHIs and UCIs, and lack of consistent terminology for other patterns of thermal variation at nested scales. We use the case of a historically well-enforced urban growth boundary (UGB) around Portland (Oregon, USA): to explore the representativeness of the surface temperature UHI (SUHI) as derived from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data, to test common assumptions of characteristically “warm” or “cool” land covers (LCs), and to name other common urban thermal features of interest. We find that the UGB contains heat as well as sprawl, inducing a sharp surface temperature contrast across the urban/rural boundary. The contrast ranges widely depending on the end-members chosen, across a spectrum from positive (SUHI) to negative (SUCI) values. We propose a new, inclusive “urban thermal deviation” (UTD) term to span the spectrum of possible UHI-zero-UCI conditions. We also distinguish at finer scales “microthermal extremes” (MTEs), discrete areas tending in the same thermal direction as their LC or surroundings but to extreme (hot or cold) values, and microthermal anomalies (MTAs), that run counter to thermal expectations or tendencies for their LC or surroundings. The distinction is important because MTEs suggest a need for moderation in the local thermal landscape, whereas MTAs may suggest solutions.

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Developing High-Resolution Descriptions of Urban Heat Islands: A Public Health Imperative

Cited by 20 publications

References 7 publications

Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Urban-Rural Surface Temperature Deviation and Intra-Urban Variations Contained by an Urban Growth Boundary

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