Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA
- 1.9k Downloads
With rapidly expanding urban regions, the effects of land cover changes on urban surface temperatures and the consequences of these changes for human health are becoming progressively larger problems.
We investigated residential parcel and neighborhood scale variations in urban land surface temperature, land cover, and residents’ perceptions of landscapes and heat illnesses in the subtropical desert city of Phoenix, AZ USA.
We conducted an airborne imaging campaign that acquired high resolution urban land surface temperature data (7 m/pixel) during the day and night. We performed a geographic overlay of these data with high resolution land cover maps, parcel boundaries, neighborhood boundaries, and a household survey.
Land cover composition, including percentages of vegetated, building, and road areas, and values for NDVI, and albedo, was correlated with residential parcel surface temperatures and the effects differed between day and night. Vegetation was more effective at cooling hotter neighborhoods. We found consistencies between heat risk factors in neighborhood environments and residents’ perceptions of these factors. Symptoms of heat-related illness were correlated with parcel scale surface temperature patterns during the daytime but no corresponding relationship was observed with nighttime surface temperatures.
Residents’ experiences of heat vulnerability were related to the daytime land surface thermal environment, which is influenced by micro-scale variation in land cover composition. These results provide a first look at parcel-scale causes and consequences of urban surface temperature variation and provide a critically needed perspective on heat vulnerability assessment studies conducted at much coarser scales.
KeywordsUrban heat island Parcel MASTER Land surface temperature Social surveys Vulnerability
This work was supported by National Science Foundation Grants GEO-0816168, GEO-0814692, BCS-1026865, EF-1049251, EF-1049224, and DEB-0919006. We thank Anthony Brazel and Chris Martin for their advice on the MASTER data collection effort and David Hondoula for insightful discussions. All data are available from CAP-LTER (caplter.asu.edu).
- American Association for Public Opinion Research (2008) Standard definitions: final dispositions of case codes and outcome rates for surveys (revised). http://www.aapor.org/AAPORKentico/Education-Resources/For-Researchers/Poll-Survey-FAQ/Response-Rates-An-Overview.aspx
- Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A, Ratkowski AJ, Felde G, Hoke ML (2005) Validation of the quick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery. In: SPIE proceedings, algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XI, pp 668–678Google Scholar
- Elachi C (1987) Introduction to the physics and techniques of remote sensing. Wiley, New YorkGoogle Scholar
- Johnson BR, Young SJ (1998) In-scene atmospheric compensation: application to SEBASS data collected at the ARM site. The Aerospace CorporationGoogle Scholar
- Lee S (2015) Self-rated health in health surveys. In: Johnson TP (ed) Handbook of health survey methods. Wiley, HobokenGoogle Scholar
- United States Environmental Protection Agency (2006) Excessive heat events guidebook. EPA 430-B-06-005 (June). http://www.epa.gov/heatisland/about/pdf/EHEguide_final.pdf
- United States Environmental Protection Agency (2008) Chapter 1 Heat island basics in reducing urban heat islands: compendium of strategies. http://www.epa.gov/heatisland/resources/compendium.htm
- United Nations Department of Economic and Social Affairs Population Division (2014) World urbanization prospects: the 2014 revision, highlights (ST/ESA/SER.A/352)Google Scholar