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Frontiers of Earth Science

Front. Earth Sci.    2019, Vol. 13 Issue (3) : 445-463     https://doi.org/10.1007/s11707-017-0678-4
RESEARCH ARTICLE
On the effects of landscape configuration on summer diurnal temperatures in urban residential areas: application in Phoenix, AZ
Yiannis KAMARIANAKIS1(), Xiaoxiao LI2, B. L. TURNER II2,3,4, Anthony J. BRAZEL2,4
1. School of Mathematical and Statistical Sciences, Arizona State University, Tempe AZ 85287, USA
2. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe AZ 85298, USA
3. School of Sustainability, Arizona State University, Tempe AZ 85298, USA
4. Global Institute of Sustainability, Arizona State University, Tempe AZ 85287, USA
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Abstract

The impacts of land-cover composition on urban temperatures, including temperature extremes, are well documented. Much less attention has been devoted to the consequences of land-cover configuration, most of which addresses land surface temperatures. This study explores the role of both composition and configuration—or land system architecture—of residential neighborhoods in the Phoenix metropolitan area, on near-surface air temperature. It addresses two-dimensional, spatial attributes of buildings, impervious surfaces, bare soil/rock, vegetation and the “urbanscape” at large, from 50 m to 550 m at 100 m increments, for a representative 30-day high sun period. Linear mixed-effects models evaluate the significance of land system architecture metrics at different spatial aggregation levels. The results indicate that, controlling for land-cover composition and geographical variables, land-cover configuration, specifically the fractal dimension of buildings, is significantly associated with near-surface temperatures. In addition, statistically significant predictors related to composition and configuration appear to depend on the adopted level of spatial aggregation.

Keywords land system architecture      urban heat island effect      linear mixed-effects models      near-surface air temperature      land-cover configuration     
Corresponding Authors: Yiannis KAMARIANAKIS   
Just Accepted Date: 30 October 2017   Online First Date: 01 December 2017    Issue Date: 15 October 2019
 Cite this article:   
Yiannis KAMARIANAKIS,Xiaoxiao LI,B. L. TURNER II, et al. On the effects of landscape configuration on summer diurnal temperatures in urban residential areas: application in Phoenix, AZ[J]. Front. Earth Sci., 2019, 13(3): 445-463.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-017-0678-4
http://journal.hep.com.cn/fesci/EN/Y2019/V13/I3/445
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Yiannis KAMARIANAKIS
Xiaoxiao LI
B. L. TURNER II
Anthony J. BRAZEL
Fig.1  The Phoenix, AZ, metropolitan area. The boundaries and names identify the cities in the metroplex. The residential study sites correspond to the 44 selected weather stations.
Metric/abbreviationDescriptionClass specific metricsAggregated parcel metrics
Percent cover of a land-cover class/PLANDProportion of the land-cover type (patch type) on the unit landscape plot (0<PLAND≤100)PLAND of Building, Soil, Soil, Tree/Shrub, GrassN/A
Patch density/PDNumber of patches/ha (>0, determined by grain or pixel size)PD of Building, Soil, Soil, Tree/Shrub, GrassPD
Edge Density/EDTotal length of edges for all patches/ha (≥0, where 0 refers to a landscape composed of one patch)ED of Building, Soil, Soil, Tree/Shrub, GrassED
Landscape Shape index/LSITotal length of all patch edges divided by the minimum possible length of the area of the unit landscape plot (≥1, where the greater the LSI above 1 the more the shape deviates from a compact shape, i.e., a square)LSI of Building, Soil, Soil, Tree/Shrub, GrassLSI
Fractal dimension/
FRAC
A measure of shape complexity by calculating the departure of the patch from its Euclidean geometry (1≤FRAC≤2, where 1 corresponds to very simple shapes and 2 to extremely complex shapes)FRAC of Building, Soil, Soil, Tree/Shrub, GrassFRAC
Contagion/CONTAGA measure of adjacency of patches (0<CONTAG≤100, where patches are maximally disaggregated and dispersed when the values are small; 100 is the reverse )N/ACONTAG
Shannon’s diversity index/SHDIA measure of the diversity of patches (0 is a landscape with 1 patch)N/ASHDI
Tab.1  Land architecture metrics
Fig.2  Selected samples for buffer zones at different scales.
Fig.3  Air temperatures observed in 44 stations in the Phoenix metroplex during June 2011.
Fig.4  (a) Distributions of de-trended temperatures across 44 measurement locations. (b) Diurnal pattern of hourly averaged wind speed (in meters per second) measured at 44 weather stations during June, 2011.
Fig.5  Distributions of the percentage of buildings (a), roads (b) and grass (c) across measurement sites, for different levels of spatial aggregation.
Fig.6  (a) Coefficients of determination (R2) for site-specific regression models; Model 1 versus Model 2. (b) Site-specific slopes of the linear trends in Model 2.
Fig.7  Pearson’s correlations of the parameters in site-specific models (2) with land system architecture metrics. Plots correspond to different levels of spatial aggregation a) 50 m; b) 150 m; c) 250 m; d) 350 m; e) 450 m; f) 550 m. Darker tones represent stronger linear associations; solid squares depict positive correlations. Correlations which are not significant at the 0.01 level are not displayed. Land architecture metrics in each plot were selected using a backward stepwise procedure based on VIF. Land cover classes are designated as follows. 1: Buildings, 2: Roads, 3: Soil, 4: Trees/Shrubs, 5: Grass and 11: Pools.
Fig.8  Observed versus predicted near surface temperatures: leave-one-site cross-validation based on LMMcomb.
VariableLMM0LMMgeogLMMcomb
CoefficientStd ErrorCoefficientStd ErrorCoefficientStd Error
12AM28.956 (1.454)0.23428.956 (1.450)0.23328.959 (1.174)0.185
3AM25.275 (1.341)0.22325.276 (1.447)0.22325.279 (1.158)0.183
6AM22.563 (1.333)0.24122.564 (1.550)0.24122.566 (1.286)0.201
9AM28.898 (1.358)0.2328.898 (1.489)0.2328.901 (1.539)0.238
12PM35.506 (1.427)0.20435.507 (1.253)0.20235.510 (1.453)0.226
3PM38.928 (2.371)0.33238.929 (2.176)0.33238.931 (2.239)0.342
6PM39.005 (1.455)0.19139.006 (1.187)0.19139.008 (1.347)0.21
9PM34.101 (1.226)0.17134.101 (1.069)0.1734.103 (0.976)0.157
Lin. Trend2.7330.0432.7330.0432.7330.043
Elevation–0.4960.085–0.2230.089
FRAC1_350–0.4850.088
ME/°C0.0150.007–0.007
MedAE/°C1.5831.5551.554
RMSE/°C2.3012.2682.256 
Tab.2  Estimated mixed-effects models LMM0, LMMgeog, LMMcomb, for air temperatures. Fixed effects coefficients are significant with p<0.01. Standard deviations of significant random effects are shown in parentheses, next to the corresponding fixed-effects coefficients. Land cover classes are designated as follows. 1: Buildings, 2: Roads, 3: Soil, 4: Trees/Shrubs, 5: Grass
VariableLMM50LMM150LMM250
 CoefficientStd ErrorCoefficientStd ErrorCoefficientStd Error
12AM28.959 (1.311)0.20528.959 (1.259)0.18928.959 (1.201)0.189
3AM25.278 (1.297)0.20325.278 (1.243)0.18725.279 (1.189)0.187
6AM22.566 (1.411)0.21922.566 (1.359)0.20522.566 (1.315)0.205
9AM28.901 (1.450)0.22528.901 (1.531)0.23728.901 (1.533)0.237
12PM35.509 (1.366)0.21335.509 (1.398)0.22435.509 (1.441)0.224
3PM38.931 (2.248)0.34338.931 (2.203)0.34138.931 (2.235)0.341
6PM39.008 (1.241)0.19539.008 (1.278)0.20839.008 (1.329)0.208
9PM34.103 (0.981)0.15734.103 (0.973)0.15634.103 (0.968)0.156
Lin. Trend2.7330.0422.7330.0432.7330.043
Elevation–0.4780.083–0.2830.09–0.2370.09
PD3_50–0.3080.083
FRAC1_150–0.3860.09
FRAC1_250–0.4500.089
ME/°C–0.009–0.008–0.007
MedAE/°C1.5611.5531.554
RMSE/°C2.263 2.258 2.256 
Tab.3  Estimated mixed-effects models LMM50, LMM150, LMM250, for air temperatures. Fixed effects coefficients are significant with p<0.01. Standard deviations of significant random effects are shown in parentheses, next to the corresponding fixed-effects coefficients. Land cover classes are designated as follows. 1: Buildings, 2: Roads, 3: Soil, 4: Trees/Shrubs, 5: Grass
VariableLMM350LMM450LMM550
 CoefficientStd ErrorCoefficientStd ErrorCoefficientStd Error
12AM28.959 (1.174)0.18528.959 (1.188)0.18728.959 (1.223)0.192
3AM25.279 (1.158)0.18325.278 (1.175)0.18525.278 (1.210)0.19
6AM22.566 (1.286)0.20122.566 (1.303)0.20422.566 (1.333)0.208
9AM28.901 (1.539)0.23828.901 (1.530)0.23728.900 (1.516)0.235
12PM35.510 (1.453)0.22635.509 (1.437)0.22335.509 (1.410)0.219
3PM38.931 (2.239)0.34238.931 (2.228)0.3438.931 (2.215)0.338
6PM39.008 (1.347)0.2139.008 (1.332)0.20839.008 (1.309)0.205
9PM34.103 (0.976)0.15734.103 (0.985)0.15834.103 (1.001)0.16
Lin. Trend2.7330.0432.7330.0432.7340.043
Elevation–0.2230.089–0.2350.089–0.2660.089
FRAC1_350–0.4850.088
FRAC1_450–0.4670.089
FRAC1_550–0.4180.089
ME/°C–0.007–0.006–0.004
MedAE/°C1.5541.5521.559
RMSE/°C2.256 2.257 2.258 
Tab.4  Estimated mixed-effects models LMM350, LMM450, LMM550, for air temperatures. Fixed effects coefficients are significant with p<0.01. Standard deviations of significant random effects are shown in parentheses, next to the corresponding fixed-effects coefficients. Land cover classes are designated as follows. 1: Buildings, 2: Roads, 3: Soil, 4: Trees/Shrubs, 5: Grass
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