Landscape Ecology

, Volume 31, Issue 4, pp 745–760 | Cite as

Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA

  • G. Darrel JeneretteEmail author
  • Sharon L. Harlan
  • Alexander Buyantuev
  • William L. Stefanov
  • Juan Declet-Barreto
  • Benjamin L. Ruddell
  • Soe Win Myint
  • Shai Kaplan
  • Xiaoxiao Li
Research Article



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.


Urban 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 (


  1. American Association for Public Opinion Research (2008) Standard definitions: final dispositions of case codes and outcome rates for surveys (revised).
  2. Baatz M, Hoffmann C, Willhauck G (2008) Progressing from object-based to object-oriented image analysis. In: Blaschke T, Lang S, Hay GJ (eds) Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer, Berlin, pp 29–42CrossRefGoogle Scholar
  3. Balbus JM, Malina C (2009) Identifying vulnerable subpopulations for climate change health effects in the United States. J Occup Environ Med 51:33–37CrossRefPubMedGoogle Scholar
  4. Basu R, Samet JM (2002) Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence. Epidemiol Rev 24:190–202CrossRefPubMedGoogle Scholar
  5. Belanger D, Gosselin P, Valois P, Abdous B (2015) Neighbourhood and dwelling characteristics associated with the self-reported adverse health effects of heat in most deprived urban areas: a cross-sectional study in 9 cities. Health Place 32:8–18CrossRefPubMedGoogle Scholar
  6. 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
  7. Buscail C, Upegui E, Viel JF (2012) Mapping heatwave health risk at the community level for public health action. Int J Health Geogr. doi: 10.1186/1476-1072x-1111-1138 PubMedPubMedCentralGoogle Scholar
  8. Buyantuyev A, Wu JG (2010) Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecol 25:17–33CrossRefGoogle Scholar
  9. Cao X, Onishi A, Chen J, Imura H (2010) Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc Urban Plan 96:224–231CrossRefGoogle Scholar
  10. Chow WTL, Brennan D, Brazel AJ (2012) Urban heat island research in Phoenix, Arizona: theoretical Contributions and policy applications. Bull Am Meteorol Soc 93:517–530CrossRefGoogle Scholar
  11. Connors JP, Galletti CS, Chow WTL (2013) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecol 28:271–283CrossRefGoogle Scholar
  12. Deng CB, Wu CS (2013) Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sens Environ 131:262–274CrossRefGoogle Scholar
  13. Elachi C (1987) Introduction to the physics and techniques of remote sensing. Wiley, New YorkGoogle Scholar
  14. Georgescu M, Morefield PE, Bierwagen BG, Weaver CP (2014) Urban adaptation can roll back warming of emerging megapolitan regions. Proc Natl Acad Sci USA 111:2909–2914CrossRefPubMedPubMedCentralGoogle Scholar
  15. Gober P, Brazel A, Quay R, Myint S, Grossman-Clarke S, Miller A, Rossi S (2010) Using watered landscapes to manipulate urban heat island effects: how much water will it take to cool Phoenix? J Am Plan Assoc 76:109–121CrossRefGoogle Scholar
  16. Goldreich Y (2006) Ground and top of canopy layer urban heat island partitioning on an airborne image. Remote Sens Environ 104:247–255CrossRefGoogle Scholar
  17. Grimm NB, Redman CL (2004) Approaches to the study of urban ecosystems: the case study of Central Arizona—Phoenix. Urban Ecosyst 7:199–213CrossRefGoogle Scholar
  18. Grove JM, Locke DH, O’Neil-Dunne JPM (2014) An ecology of prestige in New York City: examining the relationships among population density, socio-economic status, group identity, and residential canopy cover. Environ Manag 54:402–419CrossRefGoogle Scholar
  19. Harlan SL, Declet-Barreto JH, Stefanov WL, Petitti DB (2013) Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa County, Arizona. Environ Health Perspect 121:197–204CrossRefPubMedPubMedCentralGoogle Scholar
  20. Hondula DM, Barnett AG (2014) Heat-related morbidity in Brisbane, Australia: spatial variation and area-level predictors. Environ Health Perspect 122:831–836PubMedPubMedCentralGoogle Scholar
  21. Hondula DM, Davis RE, Leisten MJ, Saha MV, Veazey LM, Wegner CR (2012) Fine-scale spatial variability of heat-related mortality in Philadelphia County, USA, from 1983-2008: a case-series analysis. Environ Health. doi: 10.1186/1476-1069x-1111-1116 PubMedPubMedCentralGoogle Scholar
  22. Hook SJ, Myers JEJ, Thome KJ, Fitzgerald M, Kahle AB (2001) The MODIS/ASTER airborne simulator (MASTER)—a new instrument for earth science studies. Remote Sens Environ 76:93–102CrossRefGoogle Scholar
  23. Imhoff ML, Zhang P, Wolfe RE, Bounoua L (2010) Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ 114:504–513CrossRefGoogle Scholar
  24. Intergovernmental Panel on Climate Change (2014) Climate change 2014: impacts, adaptation, and vulnerability. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  25. Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L, Stefanov WL (2007) Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecol 22:353–365CrossRefGoogle Scholar
  26. Jenerette GD, Harlan SL, Stefanov WL, Martin CA (2011) Ecosystem services and urban heat riskscape moderation: water, green spaces, and social inequality in Phoenix, USA. Ecol Appl 21:2637–2651CrossRefPubMedGoogle Scholar
  27. Jenerette GD, Miller G, Buyantuev A, Pataki DE, Gillespie T, Pincetl S (2013) Urban vegetation and income segregation in drylands: a synthesis of seven metropolitan regions in the southwestern United States. Environ Res Lett 8:044001CrossRefGoogle Scholar
  28. Johnson BR, Young SJ (1998) In-scene atmospheric compensation: application to SEBASS data collected at the ARM site. The Aerospace CorporationGoogle Scholar
  29. Johnson DP, Wilson JS, Luber GC (2009) Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. Int J Health Geogr. doi: 10.1186/1476-1072x-1188-1157 PubMedPubMedCentralGoogle Scholar
  30. Johnson DP, Stanforth A, Lulla V, Luber G (2012) Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Appl Geogr 35:23–31CrossRefGoogle Scholar
  31. Kalkstein LS (1991) A new approach to evaluate the impact of climate on human mortality. Environ Health Perspect 96:145–150CrossRefPubMedPubMedCentralGoogle Scholar
  32. Kalma JD, McVicar TR, McCabe MF (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469CrossRefGoogle Scholar
  33. Kealy PS, Hook SJ (1993) Separating temperature and emissivity in thermal infrared multispectral scanner data—Implications for recovering land-surface temperatures. IEEE Trans Geosci Remote Sens 31:1155–1164CrossRefGoogle Scholar
  34. Kuras ER, Hondula DM, Brown-Saracino J (2015) Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure. Int J Biometeorol. doi: 10.1007/s00484-00014-00946-x PubMedGoogle Scholar
  35. Kustas W, Anderson M (2009) Advances in thermal infrared remote sensing for land surface modeling. Agric For Meteorol 149:2071–2081CrossRefGoogle Scholar
  36. Laaidi K, Zeghnoun A, Dousset B, Bretin P, Vandentorren S, Giraudet E, Beaudeau P (2012) The impact of heat islands on mortality in Paris during the August 2003 heat wave. Environ Health Perspect 120:254–259CrossRefPubMedPubMedCentralGoogle Scholar
  37. Lee S (2015) Self-rated health in health surveys. In: Johnson TP (ed) Handbook of health survey methods. Wiley, HobokenGoogle Scholar
  38. Li JX, Song CH, Cao L, Zhu FG, Meng XL, Wu JG (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115:3249–3263CrossRefGoogle Scholar
  39. Li X, Myint SW, Zhang Y, Galletti C, Zhang X, Turner Ii BL (2014) Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography. Int J Appl Earth Obs Geoinf 33:321–330CrossRefGoogle Scholar
  40. Liang SL (2001) Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sens Environ 76:213–238CrossRefGoogle Scholar
  41. Liang BQ, Weng QH (2008) Multiscale analysis of census-based land surface temperature variations and determinants in Indianapolis, United States. J Urban Plan Dev 134:129–139CrossRefGoogle Scholar
  42. Lundholm J, MacIvor JS, MacDougall Z, Ranalli M (2010) Plant species and functional group combinations affect green roof ecosystem functions. PLoS One. doi: 10.1371/journal.pone.0009677 PubMedPubMedCentralGoogle Scholar
  43. McCarthy HR, Pataki DE, Jenerette GD (2011) Plant water-use efficiency as a metric of urban ecosystem services. Ecol Appl 21:3115–3127CrossRefGoogle Scholar
  44. Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng QH (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115:1145–1161CrossRefGoogle Scholar
  45. Myint SW, Wentz EA, Brazel AJ, Quattrochi DA (2013) The impact of distinct anthropogenic and vegetation features on urban warming. Landscape Ecol 28:959–978CrossRefGoogle Scholar
  46. Myint SW, Zheng B, Talen E, Fan C, Kaplan S, Middel A, Smith M, Huang HP, Brazel A (2015) Does the spatial arrangement of urban landscape matter? Examples of urban warming and cooling in Phoenix and Las Vegas. Ecosystem Health and Sustainability 1:art15CrossRefGoogle Scholar
  47. Nuzzo R (2014) Statistical errors. Nature 506:150–152CrossRefPubMedGoogle Scholar
  48. Oke TR (1973) City size and urban heat island. Atmos Environ 7:769–779CrossRefGoogle Scholar
  49. Pataki DE, Boone CG, Hogue TS, Jenerette GD, McFadden JP, Pincetl S (2011) Ecohydrology bearings-invited commentary socio-ecohydrology and the urban water challenge. Ecohydrology 4:341–347CrossRefGoogle Scholar
  50. Peng SS, Piao SL, Ciais P, Friedlingstein P, Ottle C, Breon FM, Nan HJ, Zhou LM, Myneni RB (2012) Surface urban heat island across 419 global big cities. Environ Sci Technol 46:696–703CrossRefPubMedGoogle Scholar
  51. Petitti DB, Harlan SL, Chowell-Puente G, Ruddell D (2013) Occupation and environmental heat-associated deaths in Maricopa County, Arizona: a case-control study. PLoS One 8:e62596CrossRefPubMedPubMedCentralGoogle Scholar
  52. Petitti DB, Hondula DM, Yang S, Harlan SL, Chowell G (2015) Multiple trigger points for quantifying heat-health impacts: new evidence from a hot climate. Environ Health Perspect. doi: 10.1289/ehp.1409119 PubMedPubMedCentralGoogle Scholar
  53. Reid CE, O’Neill MS, Gronlund CJ, Brines SJ, Brown DG, Diez-Roux AV, Schwartz J (2009) Mapping community determinants of heat vulnerability. Environ Health Perspect 117:1730–1736PubMedPubMedCentralGoogle Scholar
  54. Ruddell D, Harlan SL, Grossman-Clarke S, Chowell G (2012) Scales of perception: public awareness of regional and neighborhood climates. Clim Change 111:581–607CrossRefGoogle Scholar
  55. Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens Environ 88:170–186CrossRefGoogle Scholar
  56. Song J, Du S, Feng X, Guo L (2014) The relationships between landscape compositions and land surface temperature: quantifying their resolution sensitivity with spatial regression models. Landsc Urban Plan 123:145–157CrossRefGoogle Scholar
  57. Stone B Jr (2012) The city and the coming climate: climate change in the places we live. Cambridge University Press, New YorkCrossRefGoogle Scholar
  58. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  59. Uejio CK, Wilhelmi OV, Golden JS, Mills DM, Gulino SP, Samenow JP (2011) Intra-urban societal vulnerability to extreme heat: the role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health Place 17:498–507CrossRefPubMedGoogle Scholar
  60. United States Environmental Protection Agency (2006) Excessive heat events guidebook. EPA 430-B-06-005 (June).
  61. United States Environmental Protection Agency (2008) Chapter 1 Heat island basics in reducing urban heat islands: compendium of strategies.
  62. 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
  63. Vanos JK (2015) Children’s health and vulnerability in outdoor microclimates: a comprehensive review. Environ Int 76:1–15CrossRefPubMedGoogle Scholar
  64. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384CrossRefGoogle Scholar
  65. Weng QH, Rajasekar U, Hu XF (2011) Modeling urban heat islands and their relationship with impervious surface and vegetation abundance by using ASTER images. IEEE Trans Geosci Remote Sens 49:4080–4089CrossRefGoogle Scholar
  66. Xiang TT, Vivoni ER, Gochis DJ (2014) Seasonal evolution of ecohydrological controls on land surface temperature over complex terrain. Water Resour Res 50:3852–3874CrossRefGoogle Scholar
  67. Zhan WF, Chen YH, Zhou J, Wang JF, Liu WY, Voogt J, Zhu XL, Quan JL, Li J (2013) Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sens Environ 131:119–139CrossRefGoogle Scholar
  68. Zheng B, Myint SW, Fan C (2014) Spatial configuration of anthropogenic land cover impacts on urban warming. Landsc Urban Plan 103:104–111CrossRefGoogle Scholar
  69. Zhou WQ, Huang GL, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102:54–63CrossRefGoogle Scholar
  70. Zhou WQ, Qian YG, Li XM, Li WF, Han LJ (2014) Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecol 29:153–167CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • G. Darrel Jenerette
    • 1
    Email author
  • Sharon L. Harlan
    • 2
  • Alexander Buyantuev
    • 3
  • William L. Stefanov
    • 4
  • Juan Declet-Barreto
    • 2
    • 5
  • Benjamin L. Ruddell
    • 6
  • Soe Win Myint
    • 7
  • Shai Kaplan
    • 8
  • Xiaoxiao Li
    • 9
  1. 1.Department of Botany and Plant SciencesUniversity of California RiversideRiversideUSA
  2. 2.School of Human Evolution and Social ChangeArizona State UniversityTempeUSA
  3. 3.Department of Geography and PlanningUniversity at Albany, State University of New YorkAlbanyUSA
  4. 4.Astromaterials Research and Exploration Science Division, Exploration Integration and Science DirectorateNASA Lyndon B. Johnson Space CenterHoustonUSA
  5. 5.Natural Resources Defense CouncilWashington, DCUSA
  6. 6.Fulton Schools of EngineeringArizona State UniversityTempeUSA
  7. 7.School of Geographical Sciences and Urban PlanningArizona State UniversityTempeUSA
  8. 8.The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevBeershebaIsrael
  9. 9.Julie Ann Wrigley Global Institute of SustainabilityArizona State UniversityTempeUSA

Personalised recommendations