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Climate 2019, 7(1), 5; https://doi.org/10.3390/cli7010005

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

1
School of Urban Studies & Planning, Portland State University, Portland, OR 97201, USA
2
Science Museum of Virginia, Richmond, VA 23220, USA
*
Author to whom correspondence should be addressed.
Received: 1 December 2018 / Revised: 17 December 2018 / Accepted: 22 December 2018 / Published: 3 January 2019
(This article belongs to the Special Issue Climate Change, Heat Waves, and Human Health)
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Abstract

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. View Full-Text
Keywords: urban heat; satellite; ground-based; land use and land cover; machine learning urban heat; satellite; ground-based; land use and land cover; machine learning
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Shandas, V.; Voelkel, J.; Williams, J.; Hoffman, J. Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat. Climate 2019, 7, 5.

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