Abstract
Context
Land surface temperature (LST) directly responds to incoming solar radiation and is strongly influenced by vertical urban structures, such as trees and buildings that provide shade. Conventional LST-planar land-cover assessments do not explicitly address the vertical dimension of the “urbanscape” and therefore do not capture the heterogeneity of solar radiation exposure of planar surfaces adequately.
Objectives
To fill this gap, this study compares and integrates novel spherical land-cover fractions derived from Google Street View (GSV) with the conventional planar land-cover fractions in estimating daytime and nighttime LST variations in the Phoenix metropolitan area, AZ.
Methods
The GSV spherical dataset was created using big data and machine learning techniques. The planar land cover was classified from 1 m NAIP imagery. Ordinal least square (OLS) and geographically weighted regression (GWR) were used to assess the relationship between LST and urban form (spherical and planar fractions) at the block group level. Social-demographic variables were also added provide the most comprehensive assessment of LST.
Results
The GSV spherical fractions provide better LST estimates than the planar land-cover fractions, because they capture the multi-layer tree crown and vertical wall influences that are missing from the bird-eye view imagery. The GWR regression further improves model fit versus the OLS regression (R2 increased from 0.6 to 0.8).
Conclusions
GSV and spatial regression (GWR) approaches improve the specificity of LST identified by neighborhoods in Phoenix metro-area by accounting for shading. This place-specific information is critical for optimizing diverse cooling strategies to combat heat in desert cities.
Similar content being viewed by others
References
Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23(1):1–26
Boone CG, Cadenasso ML, Grove JM, Schwarz K, Buckley GL (2010) Landscape, vegetation characteristics, and group identity in an urban and suburban watershed: why the 60s matter. Urban Ecosyst 13(3):255–271
Brazel A, Selover N, Vose R, Heisler G (2000) The tale of two climates Baltimore and Phoenix urban LTER sites. Climate Res 15(2):123–135
Buyantuyev A, Wu J (2010) Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecol 25(1):17–33
Cai H, Xu X (2017) Impacts of built-up area expansion in 2D and 3D on regional surface temperature. Sustainability 9(10):1862
Charlton M, Fotheringham S, Brunsdon C (2009) Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland Maynooth
Chow WT, Brennan D, Brazel AJ (2012a) Urban heat island research in Phoenix, Arizona: theoretical contributions and policy applications. Bull Am Meteor Soc 93(4):517–530
Chow WT, Chuang WC, Gober P (2012b) Vulnerability to extreme heat in metropolitan Phoenix: spatial, temporal, and demographic dimensions. Prof Geogr 64(2):286–302
Connors JP, Galletti CS, Chow WT (2013) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecol 28(2):271–283
Da Silva AR, Fotheringham AS (2016) The multiple testing issue in geographically weighted regression. Geogr Anal 48(3):233–247
Eliasson I, Offerle B, Grimmond CSB, Lindqvist S (2006) Wind fields and turbulence statistics in an urban street canyon. Atmos Environ 40(1):1–16
Forman RT (2016) Urban ecology principles: are urban ecology and natural area ecology really different? Landsc Ecol 31(8):1653–1662
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New Jersey
Fotheringham AS, Yang W, Kang W (2017) Multiscale geographically weighted regression (mgwr). Ann Am Assoc Geogr 107(6):1247–1265
Gál T, Lindberg F, Unger J (2009) Computing continuous sky view factors using 3D urban raster and vector databases: comparison and application to urban climate. Theoret Appl Climatol 95(1–2):111–123
Gillespie A, Rokugawa S, Matsunaga T, Cothern JS, Hook S, Kahle AB (1998) A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans Geosci Remote Sens 36(4):1113–1126
Gober P, Brazel A, Quay R, Myint S, Grossman-Clarke S, Miller A, Rossi S (2009) Using watered landscapes to manipulate urban heat island effects: how much water will it take to cool Phoenix? J Am Planning Assoc 76(1):109–121
Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L (2006) Neighborhood microclimates and vulnerability to heat stress. Soc Sci Med 63(11):2847–2863
Harlan SL, Declet-Barreto JH, Stefanov WL, Petitti DB (2012) Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa County, Arizona. Environ Health Persp 121(2):197–204
Hondula DM, Georgescu M, Balling RC (2014) Challenges associated with projecting urbanization-induced heat-related mortality. Sci Total Environ 490:538–544
Huang G, Cadenasso ML (2016) People, landscape, and urban heat island: dynamics among neighborhood social conditions, land cover and surface temperatures. Landscape Ecol 31(10):2507–2515
Hulley GC, Hughes CG, Hook SJ (2012) Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data. J Geophys Res 117:D23
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(3):504–513
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(3):353–365
Jenerette GD, Harlan SL, Buyantuev A, Stefanov WL, Declet-Barreto J, Ruddell BL, Myint S, Kaplan S, Li X (2016) Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landscape Ecol 31(4):745–760
JPL (2001) ASTER higher-level product user guide, advanced spaceborne thermal emission and reflection radiometer. Jet Propulsion Laboratory, California Institute of Technology
Kane K, Connors JP, Galletti CS (2014) Beyond fragmentation at the fringe: a path-dependent, high-resolution analysis of urban land cover in Phoenix, Arizona. Appl Geogr 52:123–134
Krayenhoff ES, Voogt JA (2016) Daytime thermal anisotropy of urban neighbourhoods: morphological causation. Remote Sensing 8(2):108
Larson KL, Casagrande D, Harlan SL, Yabiku ST (2009) Residents’ yard choices and rationales in a desert city: social priorities, ecological impacts, and decision tradeoffs. Environ Manage 44(5):921–937
Li X, Kamarianakis Y, Ouyang Y, Turner BL II, Brazel A (2017) On the association between land system architecture and land surface temperatures: evidence from a Desert Metropolis—Phoenix, Arizona, USA. Landsc Urban Plan 163:107–120
Li X, Li W, Middel A, Harlan SL, Brazel AJ, Turner BL II (2016) Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: combined effects of land composition and configuration and cadastral–demographic–economic factors. Remote Sens Environ 174:233–243
Li X, Myint SW, Zhang Y, Galletti C, Zhang X, Turner BL II (2014) Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography. Int J Appl Earth Obs Geoinf 33:321–330
Li X, Ratti C, Seiferling I (2018) Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google Street View. Landsc Urban Plan 169:81–91
Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sensing Environ 115(12):3249–3263
Liu W, Feddema J, Hu L, Zung A, Brunsell N (2017) Seasonal and diurnal characteristics of land surface temperature and major explanatory factors in Harris County, Texas. Sustainability 9(12):2324
Mandanici E, Conte P, Girelli V (2016) Integration of aerial thermal imagery, LiDAR data and ground surveys for surface temperature mapping in urban environments. Remote Sensing 8(10):880
McCarthy MP, Best MJ, Betts RA (2010) Climate change in cities due to global warming and urban effects. Geophys Res Lett. https://doi.org/10.1029/2010GL042845
Middel A, Häb K, Brazel AJ, Martin CA, Guhathakurta S (2014) Impact of urban form and design on mid-afternoon microclimate in Phoenix Local Climate Zones. Landscape Urban Plan 122:16–28
Middel A, Krayenhoff ES (under review) Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform. Sci Total Environ
Middel A, Lukasczyk J, Maciejewski R (2017) Sky view factors from synthetic fisheye photos for thermal comfort routing—a case study in Phoenix, Arizona. Urban Plan 2(1):19
Middel A, Lukasczyk J, Maciejewski R, Demuzere M, Roth M (2018) Sky view factor footprints for urban climate modeling. Urban Clim 25:120–134
Middel A, Lukasczyk J, Zakrzewski S, Arnold M, Maciejewski R (2019) Urban form and composition of street canyons: a human-centric big data and deep learning approach. Landscape Urban Plan 183:122–132
Middel A, Selover N, Hagen B, Chhetri N (2016) Impact of shade on outdoor thermal comfort—a seasonal field study in Tempe, Arizona. Int J Biometeorol 60(12):1849–1861
Myint SW, Wentz EA, Brazel AJ, Quattrochi DA (2013) The impact of distinct anthropogenic and vegetation features on urban warming. Landscape Ecol 28(5):959–978
Myint SW, Zheng B, Talen E, Fan C, Kaplan S, Middel A, Brazel A (2015) Does the spatial arrangement of urban landscape matter? Examples of urban warming and cooling in Phoenix and Las Vegas. Ecosyst Health Sustain 1(4):1–15
Oke TR (1981) Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. J Climatol 1(3):237–254
Oke TR, Mills G, Christen A, Voogt JA (2017) Urban climates. Cambridge University Press, Cambridge
Prince SD, Goetz SJ, Dubayah RO, Czajkowski KP, Thawley M (1998) Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deficit using advanced very high-resolution radiometer satellite observations: comparison with field observations. J Hydrol 212–213:230–249
Richards DR, Edwards PJ (2017) Quantifying street tree regulating ecosystem services using Google Street View. Ecol Ind 77:31–40
Sailor DJ (2011) A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. Int J Climatol 31(2):189–199
Steyn DG, Hay JE, Watson ID, Johnson GT (1986) The determination of sky view-factors in urban environments using video imagery. J Atmos Ocean Technol 3(4):759–764
Stisen S, Sandholt I, Nørgaard A, Fensholt R, Eklundh L (2007) Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sens Environ 110(2):262–274
Stoll MJ, Brazel AJ (1992) Surface-air temperature relationships in the urban environment of Phoenix, Arizona. Phys Geogr 13(2):160–179
Su YF, Foody GM, Cheng KS (2012) Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations. Landsc Urban Plan 107(2):172–180
Svensson MK (2004) Sky view factor analysis–implications for urban air temperature differences. Meteorol Appl 11(3):201–211
Turner BL II (2017) Land system architecture for urban sustainability: new directions for land system science illustrated by application to the urban heat island problem. J Land Use Sci 12(6):689–697
Unger J (2004) Intra-urban relationship between surface geometry and urban heat island: review and new approach. Clim Res 27:253–264
Vancutsem C, Ceccato P, Dinku T, Connor SJ (2010) Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens Environ 114(2):449–465
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384
Wang C, Middel A, Myint SW, Kaplan S, Brazel AJ, Lukasczyk J (2018) Assessing local climate zones in arid cities: the case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J Photogr Remote Sens 141:59–71
Wentz EA, Rode S, Li X, Tellman EM, Turner BL (2016) Impact of Homeowner Association (HOA) landscaping guidelines on residential water use. Water Resour Res 52(5):3373–3386
Zhang Y, Murray AT, Turner BL II (2017) Optimizing green space locations to reduce daytime and nighttime urban heat island effects in Phoenix, Arizona. Landsc Urban Plan 165:162–171
Zhou W, Pickett ST, Cadenasso ML (2017a) Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landscape Ecol 32(1):15–30
Zhou W, Wang J, Cadenasso ML (2017b) Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens Environ 195:1–12
Acknowledgements
This research was supported by Technische Universität Kaiserslautern, Grant “Microclimate Data Collection, Analysis, and Visualization”, the Gilbert F. White Fellowship, the Graduate. School Completion Fellowship, the Central Arizona-Phoenix Long-Term Ecological Research program (NSF Grant No. BCS-1026865), the National Science Foundation (NSF) under Grant No. SES-0951366, NSF IMEE Grant No. 1635490, NSF DMS Grant No. 1419593 and USDA NIFA Grant No. 2015-67003-23508. The research was undertaken in the Environmental Remote Sensing and Geoinformatics Lab, Arizona State University, AZ. We thank for the valuable inputs from our reviewers.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Zhang, Y., Middel, A. & Turner, B.L. Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression. Landscape Ecol 34, 681–697 (2019). https://doi.org/10.1007/s10980-019-00794-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10980-019-00794-y