Engaging Citizen Scientists in Characterizing Urban Heat Island at the Neighborhood Scale Using Satellite and Ground Observations
Abstract
The built environments of urban metropolitan areas have higher heat storage capacities than their surrounding rural counterparts. Hence, built environments are significantly warmer than adjoining rural areas. This geophysical phenomenon is known as the urban heat island (UHI) effect, and it has the potential of increasing both the frequency and the severity of extreme heat events in cities and thereby adversely impacting the health and welfare of urban residents. Increased temperatures in cities have been reported to cause heat stroke, heat exhaustion, and heat syncope, especially among the most vulnerable city-dwellers (the elderly and children) and especially within underserved communities. Although the UHI has been studied closely on citywide scales, often with data sourced exclusively from satellites and other remote sensing tools, not enough studies have been conducted with the use of ground-based data from individual neighborhoods within cities. There have also been insufficient neighborhood-scale analyses to ground-truth remote-sensed data sources. Therefore, the alignment between satellite data and ground-sourced data in Bedford-Stuyvesant (a community in Brooklyn, New York) was examined. Satellite data were analyzed with the Google Earth Engine platform, and the resulting maps were used to identify land surface temperature (LST) variations that were then evaluated against ground-based measurements. Community residents became citizen scientists as a pilot program was developed to train them in the collection and the understanding of their own neighborhood's ground-based data. Formative and summative assessments of the program's efficacy were conducted via surveys, interviews, and qualitative/quantitative community response data. Overall, preliminary results indicate that the satellite and ground data showed similar spatial variation in land surface temperature; however, the satellite estimates underestimated the highest land surface temperatures.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN51E0677L
- Keywords:
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- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1999 General or miscellaneous;
- INFORMATICS