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Microclimates in a desert city were related to land use and vegetation index
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Urban Forestry & Urban Greening 3 (2005) 137–147 www.elsevier.de/ufug
Microclimates in a desert city were related to land use and vegetation index Linda B. Stablera, Chris A. Martina,, Anthony J. Brazelb a Department of Applied Biological Sciences, Arizona State University East, 7001 East William Field Road, Building 130, Mesa, AZ 85212, USA b Department of Geography, Arizona State University, AZ, USA
Abstract A heterogeneous patchwork mosaic of soil, vegetation, and built surfaces that result from a variety of urban land uses cause urban microclimates within cities. We studied the seasonal relationships of land use, urban plant cover and microclimate in Phoenix, Arizona, USA, metropolitan. Early morning (0500 HR) and afternoon (1500 HR) nearsurface temperatures and humidities were measured along multiple transects in this desert city and outlying areas during June and December 1999. A Landsat thematic mapper normalized differential vegetation index (NDVI) image was used to quantify spatial patterns of plant density. Land use had the most pronounced effect on microclimate during the early morning hours of summer. Agricultural and residential land uses had the highest relative humidities, dew point temperatures, and NDVI, and the lowest air temperatures. Commercial and industrial land uses had highest temperatures and lowest NDVI. Temperatures were generally negatively correlated to NDVI, while humidity and dew point temperatures were generally positively correlated to NDVI. Distance from the urban core did not affect NDVI but had a significant negative effect on adjusted air temperature. In addition, a historical comparison of land use, NDVI and microclimate data collected during 1976 and again during 1999 along two transects revealed overall decreases in NDVI and relative increases in air temperature indicative of urban expansion. These findings show that microclimates in this desert city are caused by more than just variations in plant cover, and are likely an interactive effect of vegetation density and other non-vegetative urban surfaces. r 2004 Elsevier GmbH. All rights reserved. Keywords: Humidity; Landscape; Land cover; Temperature; Urban heat island
Introduction The urban heat island (UHI) effect is a welldocumented phenomenon and the need to incorporate knowledge of urban climate in the management of urban forests has been acknowledged (de Schiller and Evans, 1996; Thamm et al., 1999; Eliasson, 2000). Built Corresponding author.
E-mail address: [email protected] (C.A. Martin). 1618-8667/$ - see front matter r 2004 Elsevier GmbH. All rights reserved. doi:10.1016/j.ufug.2004.11.001
surfaces such as asphalt and concrete absorb, store, and reradiate more thermal energy per unit area than do plants and soil. Also, loss of pervious soil surfaces in densely urbanized areas increase surface water runoff, reduce soil water storage capacity, and reduce evaporative cooling of the air. Conversely, urban forest plantings can modify urban microclimates by shading and evapotranspiration, and use of plants to ameliorate urban heating is one strategy that has generated significant interest (Sailor, 1998; Simpson and McPherson, 1998; Jo and McPherson, 2001).
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The relative cover of urban forests and anthropogenic surfaces influences urban climate at several scales. Trees and buildings existing singly or in clusters create strong spatial variability in local heat transfer fluxes that define urban microclimates within the urban canopy layer (Oke, 1989). The UHI is a mesoscale climate phenomenon that at the urban boundary layer reflects an integration of a heterogeneous matrix of microclimates within the urban canopy layer (Schmid and Oke, 1990). Although microclimates at the urban canopy layer scale are of interest in urban planning because they most affect levels of human comfort, urban microclimate heterogeneity also has a direct effect on other biotic processes such as microbial, plant and animal respiration and plant photosynthesis (Baker et al., 2002). Urban forests might have the greatest impact on heat transfer fluxes in hot, arid regions with mild winters due to effects of irrigation-enhanced evapotranspiration during dry summer seasons and continuous metabolic activity of plants throughout the year (Avissar, 1996; Barradas and Tejeda-Martinez, 1999). The metropolitan area of Phoenix, Arizona, is situated in the Sonoran Desert in the southwestern United States. Diversion of water from the Salt and Colorado Rivers for irrigation usage has facilitated creation of an urban forest with tree canopy coverage of about 13% of total urban surface area (Stabler, 2003; Walker and Briggs, 2004). Although the UHI effect has been well documented in the Phoenix area (Brazel et al., 2000), the classic urban to rural increase in plant cover typical of urban forests in more mesic climates (Miller, 1997) does not apply there, where urban forest density is greater than in the surrounding desert habitats (Martin and Stabler, 2002). Within Phoenix where virtually all managed landscapes are irrigated, evapotranspiration might be expected to especially influence microclimates relative to those in more mesic cities where supplemental water is not continuously added to the landscape. An analysis of 107 randomly chosen study sites within the Phoenix metropolitan area showed that land cover by surfaces such as asphalt, concrete or soil, as well as plant canopy area, was a function of urban land use (Stabler, 2003). The purpose of this study was to examine spatial patterns of land use, plant density, and microclimate in the Phoenix metropolitan area, a mixed desert, agricultural, and urban area. Because Phoenix is in the Sonoran Desert and subject to arid climatic conditions, it was hypothesized that (1) spatial heterogeneity in temperature and atmospheric moisture would be related to urban land use; (2) land use would influence urban plant density; (3) plant densities and associated latent heat fluxes would be the most important factor influencing microclimates; and (4) changes in spatial patterns of land use and plant density over time would influence urban microclimates. To test
these hypotheses, spatial patterns of microclimate conditions, land use, and urban plant density derived from a normalized differential vegetation index (NDVI) were studied along four transects consisting of major arterial roadways within the Phoenix metropolitan area. These transects traversed a broad range of urban and suburban land uses and included both the urban core and urban fringe. In addition, historical NDVI and microclimate datasets (Brazel and Johnson, 1980) from two of the four transects were used to evaluate how land use conversion over time affected NDVI and microclimate.
Methods Description of the study area Phoenix, Arizona (331260 N, 112110 W) is situated within the lower Salt River basin on the northeastern edge of the Sonoran Desert in the southwestern United States and is one of the most rapidly expanding metropolitan areas in the United States. Phoenix metropolitan is the fifth largest urban region in the United States with an estimated population of 3.3 million and urban surface area of 2223 km2 (United States Environmental Protection Agency, 2004). Archeological evidence indicates that over 500 years ago, native Americans used land along the Salt River extensively for agricultural purposes via constructed canals (Gammage, 1999). During the late 19th century, European American settlers refurbished those canals, enabling Phoenix to grow rapidly as an agricultural community. During the last half of the 20th century, much of these formerly agricultural lands were displaced by residential and commercial development. In recent years, the Phoenix metropolitan area has expanded at a rate of over ‘‘an acre an hour’’ (Morrison Institute Public Policy, 1998), new construction encroaches on desert lands previously undisturbed by agricultural activities, and land use changes within the city boundaries are common. The metropolitan area includes Phoenix and several contiguous smaller city centers and surrounding suburban areas. Mean annual precipitation in the Phoenix area is 180 mm with approximately 50% occurring as late summer thunderstorms, the remainder normally associated with winter frontal systems originating in the Pacific Ocean (Sellers and Hill, 1974). Mean summer air temperature (T) is 30.8 1C and winters are mild with average T of 11.3 1C. During the summer data collection period (8–18 June 1999) T and relative humidity (RH) at the United States National Weather Service reporting station at Phoenix Sky Harbor International Airport ranged from 21.1 to 40.6 1C and 11–43%, respectively.
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During the winter period (13–18 December 1999), T and RH ranged from 2.8 to 21.7 1C and 6–49%, respectively.
Transect selection and land use evaluation Four transects along major arterial asphalt roads were selected for study of urban microclimates. The four transects were generally over flat topography and were selected to cover the spread of the greater Phoenix metropolitan area, include heterogeneous patterns of land use, evaluate both the urban core and the urban fringe, and include areas previously studied in 1975–76 and currently in land use transition. Fig. 1 shows a map of the Phoenix metropolitan area with coarse-scale land use/land cover characterization (Stefanov et al., 2001)
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and the locations of each transect. Major arterial asphalt roads used for all four transects had an average width of 22 m. Transects 1 and 2 were chosen to repeat work conducted by Brazel and Johnson (1980). The northern half of transect 1 was an area of mixed commercial and residential land uses that were developed prior to 1976, while the southern half of transect 1 was predominantly agricultural land during 1976 but has since been developed into residential and commercial land uses. Transect 2 had little historical change in land use. The eastern portion of transect 2 traversed a portion of the Pima Indian Reservation that had remained agricultural, while the western portion of transect 2 had remained a mixture of commercial and residential urban land use. Transect 3 traversed the Phoenix urban core along its
Fig. 1. Map of study area within Phoenix, AZ, USA metropolitan showing location of transects and major land cover as classified by the Maricopa Association of Governments (2004).
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southern extent and a mature, densely vegetated residential area to the north that was predominately flood irrigated. Transect 4 traversed a variety of urban land uses, and along its western extent crossed the urban fringe into the Sonoran Desert. Land use along each transect was evaluated and classified based on socioeconomic use and function of structures present, degree of site development, and observed human activity visible from the transect centerline using typology of the American Planning Association (2004). Land use along each transect was categorized in 161 m (0.1 mile) increments because Phoenix is laid out in gridded blocks defined by main street intersections at 1609 m (1 mile) intervals. While some heterogeneity existed at the 1609 m scale, land use within a developed block tended to be homogenous to a distance of 285 m in either direction away from the transect center point. When no single land use category could be applied to a segment due to heterogeneity, land use was classified as mixed. For example, a mixed land use classification was assigned to a portion of the southern extent of transect 1 (1999 data) at the urban fringe where on one side of a road was residential housing and on another side was agricultural fields. The details of land and plant cover for transects were not evaluated from the ground due to the impracticality of undertaking such a task for over 600,161 m segments. However, earlier studies found that land and plant cover within Phoenix metropolitan was correlated to land use, and building and tree heights were similar across land uses (Hope et al., 2003; Stabler, 2003). These studies also found that mean building and tree heights in Phoenix were only 5.6 and 4.4 m, respectively. Because the Phoenix metropolitan area is comprised of mostly low buildings and small trees with canopies that do not extend over road surfaces, variations in sky view factors were not considered to be an important determinant of our microclimate data.
Microclimate measurements Near-surface temperature (T) and RH were measured at approximately 161 m intervals along each transect using a moving (average speed 48 km/h) vehicle outfitted with meteorological sensors. This measurement regimen enabled us to collect multiple measurements within a land use classification. All data were collected from approximately 0500–0630 HR (early morning) and 1500–1630 HR on days of clear, calm (windless) anticyclonic conditions during June and December 1999. For transect 2, data were also collected at 2200 HR during March 1999 for comparison to data collected there during March 1976. Two shielded copper constantan thermocouples positioned in front of the moving vehicle at 0.5 m above ground were used to measure T.
Also, RH and T were measured with a HMP 45 1C temperature and relative humidity probe (Campbell Scientific, Logan, UT, USA) positioned over the moving vehicle 2.0 m above ground. From these data, dew point temperatures (Td) were calculated. Reported T values are the mean of the three T measurements. All data were collected every 12 s with a 21 micrologger (Campbell Scientific, Logan, UT, USA). Each transect was assessed at least twice, traveling in opposite directions, and means for each 161 m spatial increment along each transect were calculated to reduce the effects of temporal changes in microclimate which may have occurred during the course of each run. Iterations of measurements were numbered and recorded on detailed maps of each transect at the time of data collection, and data recorded during periods when the vehicle was stopped at traffic lights were removed from the analysis. Voice recordings were also made to confirm the exact location of each microclimate data point. The relatively broad and unshaded asphalt roads were a surface common to all transects, thus effectively negating any concerns that differences in the data were representative of variations in the immediate (a few meters) microclimate around the sensors.
Vegetation index Plant cover density along each transect was evaluated using a NDVI map produced from a Landsat thematic mapper (TM) image of the Phoenix area (spheroid Clark 1866, zone 12, Datum NAD27, georeferenced to UTM), taken in April 1998, approximately 14 months prior to collection of microclimate data. The NDVI image was obtained from researchers evaluating the spatial and temporal distribution of land cover in the Phoenix urban area (Stefanov et al., 2001). The spatial resolution of the image was 28.5 m (0.018 mi) per pixel. All NDVI data were converted from metric units of distance to miles to conform to land use classifications. To accurately locate each transect on the NDVI image, the map coordinates of the beginning and end of each transect were determined using a standard topographical and quadrangular map of the Phoenix metropolitan area (scale of 1:250,000), with crosshairs corresponding to USGS 15-minute maps (Delorme Mapping, 1993). Based on those coordinates, areas of interest were created using ERDAS Imagines software with transect widths of 20 Landstat TM pixels (570 m), with the road defining the transect at its central axis. The 20 pixels associated with transect width were selected based on the height of T and RH sensors at 0.5–2.0 m. Most of the footprint of land influencing fluxes at the sensor height range was expected to be within 20–450 m of the sensors (Burba, 2001).
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Table 1. Mean daily values of air temperature (T) in degrees Celsius, sea-level pressure (SLP) in mbs, specific humidity (SH) in g/ kg, % relative humidity (RH), geopotential height (GPH) of the 500 mb surface in m, and wind direction (WD) interpolated from synoptic mapping for microclimate sampling dates during March and June of 1976 and 1999, Phoenix, AZ USA
March 1976 March 1999 June 1976 June 1999
T
SLP
SH
RH
GPH
WD
11 15 27 26
1017 1015 1011 1011
0.006 0.006 0.004 0.004
50 50 20 23
5600 5650 5825 5775
W N S NE
Historical comparison Brazel and Johnson (1980) collected microclimate data along transect 1 at 0500 HR during June 1976 and along transect 2 at 2200 HR during March 1976 using similar car-mounted apparati. In their study, T was measured at approximately 1 m above the ground for both transects, and atmospheric moisture was measured using a thermister psychometric system on transect 1. 1975 NDVI values were calculated for transects 1 and 2 from a 1975 Landsat TM image (Stefanov et al., 2001) as described above for the 1998 NDVI data. Regional-scale climatic conditions for the appropriate sampling periods during 1976 and 1999 are shown in Table 1. During the two June microclimate sampling dates for transect 1, synoptic-scale conditions of temperature and atmospheric moisture were similar during 1976 and 1999. Synoptic-scale conditions differed slightly during the sampling dates for transect 2 during March of 1976 and 1999. Mean daily temperature and sea-level pressure were somewhat lower during the 1976 sampling period, while relative humidity was similar. Wind direction varied for both historical comparison dates, but wind speed was low for all.
Statistical analyses Analyses of variance were conducted for 1999 transect data using a general linear model procedure (PC SAS, version 6.03, Cary, NC, USA) with land use as the independent treatment variable and T, RH, Td, and NDVI as response variables. Type IV sums of squares were used because land use classifications had unequal sample sizes for vegetation indices and microclimate observations. Duncan’s multiple range tests were employed to compare treatment means, with a significance level of a ¼ 0:05: Pearson correlation coefficients were calculated to measure degree of association between vegetation and microclimate parameters using a regression model with NDVI as the independent variable and T, RH, or Td as the dependent variable. Residuals of Type IV sums of squares for each observation of the dependent variables
were plotted against the dependent variables to check model assumptions and look for significant statistical patterns. Regression analyses of NDVI and pooled T values for early morning June transects adjusted to cotemporal temperatures at the United States National Weather Service reporting station at Phoenix Sky Harbor International Airport were made as a function of distance from the urban core (D) to detect the presence of any urban to rural gradients for NDVI and/ or NDVI. Due to lack of a consistent and reliable reference weather station in the Phoenix area, no statistical comparisons between the 1976 and 1999 data were made, and only patterns are discussed within the context of this study.
Results Land use effects on microclimate Land use affected microclimate along all four transects (P4F ¼ 0:01; Tables 2 and 3), but land use effects on microclimate were generally more pronounced at 0500 than 1500 HR for both June and December. During June 1999, the greatest effect of land use on microclimate occurred along transect 1 where nearsurface T in commercial land uses was on average 8.5 1C higher than in agricultural land uses (Table 2) and nearsurface RH and Td were 16.2% and 5.9 1C, respectively, higher in agricultural than in commercial land uses (Table 2). Variations in land use least affected microclimate along transects at 1500 HR during December when the range of T, Td and RH were only 2 1C, 4 1C and 5%, respectively (Table 3).
Vegetation index Land use affected NDVI in all transects (P4F ¼ 0:01; Table 4). The range of NDVI for all transects was from 0.506 in the dry river bottom, to 0.665 in a recreational, flood control greenbelt with a golf course and small lakes. Commercial land uses had NDVI values that ranged from 0.533 to 0.541,
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Table 2. Effects of land use on near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects during early morning (0500 HR) and afternoon (1500 HR) of June 1999, Phoenix, AZ, USA metropolitan 0500 HR
1500 HR
T (1C)
RH
Td (1C)
T (1C)
RH
Td (1C)
Transect 1 Commercial Residential Industrial Mixedb Vacant Agricultural
21.5aa 20.8ab 20.2bc 18.0cd 17.5d 13.0e
17.5d 19.1d 18.7d 24.9c 30.9b 33.7a
4.8d 3.9d 4.7d 1.8c 0.0b 1.1a
35.7a 35.5ab 34.8c 35.4ab 35.2b 35.3b
6.0b 6.4b 6.7b 6.1b 6.4b 7.8a
7.4b 6.8b 6.7b 7.5b 6.9b 4.9a
Transect 2 Commercial Residential Recreational Fallow agricultural
22.6a 21.1b 21.1b 17.9c
25.4c 31.2b 33.6ab 36.0a
0.7c 2.5b 4.3a 2.4b
36.1a 35.9a 36.2a 35.4b
9.4b 9.6ab 10.1a 9.9ab
1.2b 1.0ab 0.2a 0.9ab
Transect 3 Commercial Industrial Residential
30.0a 29.8a 27.8b
22.1b 21.2b 27.4b
6.0b 5.7b 7.4a
41.6a 41.7a 40.9b
8.8b 8.6b 10.1a
2.1b 1.7b 3.4a
Transect 4 Commercial Vacant Residential Agricultural Mixed Desert
27.2a 25.8b 25.7b 25.2c 24.8c 24.8c
35.3a 35.8a 35.2a 28.7b 35.3a 35.3a
10.2a 9.3b 9.0b 5.3d 8.0c 8.0c
41.0a 40.8a 41.0a 40.3b 40.4b 40.5b
10.0a 8.6b 9.5ab 8.6b 8.6b 7.3c
3.0a 0.8b 2.2a 0.5b 0.7b 1.5c
a Values are treatment means (n ¼ 6 –320), values followed by the same letter within a column by transect are not statistically different at a ¼ 0.05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
residential from 0.541 to 0.619, and agricultural from 0.534 (fallow) to 0.656 (active). The greatest range of NDVI within a single transect occurred along the shortest, with a low value of 0.534 and a high of 0.665 on transect 2.
Correlation of vegetation index to microclimate Microclimate was most significantly correlated to NDVI along transect 3 which crossed through the urban core along its southern extent and a mature, densely vegetated residential area to the north that was predominately flood irrigated (Table 5). When significant, T was negatively correlated to NDVI (r ¼ 0:32 to –0.78) and RH was positively correlated to NDVI (r ¼ 0:2220:77). Also when significant, Td was positively correlated to NDVI (r ¼ 0:4820:72) except along transect 4 where Td was negatively correlated to NDVI during June 0500 HR. There were no significant correlations between microclimate and NDVI along transects 1 and 2 during June 1500 HR.
An analysis of the relationship between residual Type IV sums of squares of adjusted T and adjusted T at June 0500 HR showed a distinct pattern when NDVI was used as the independent variable (Fig. 2). Numerical transformation of the data failed to alter the residual plot pattern. This pattern is suggestive of some independent variable missing from the regression model (Gomez and Gomez, 1984.)
Urban to rural gradients The weak relationship between NDVI and D (R2 ¼ 0:003; P4F ¼ 0:001; data not shown) indicated a general lack of an urban to rural vegetation gradient in Phoenix. Conversely, adjusted T at 0500 HR in June was negatively correlated to D (Fig. 3). Using Pearson’s correlation coefficients within each transect, the degree of association between T and D ranged from an r ¼ 0:51 near the urban core (transect 3) to r ¼ 0:97 at the urban fringe (transect 4), confirming a UHI effect unrelated to vegetation cover. Multivariate regression of
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Table 3. Effects of land use on near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects during early morning (0500 HR) and afternoon (1500 HR) of December 1999, Phoenix, AZ, USA metropolitan 0500 HR
1500 HR
T (1C)
RH
Td (1C)
T (1C)
RH
Td (1C)
Transect 1 Industrial Commercial Residential Mixedb Vacant Agricultural
8.5aa 7.1ab 6.0abc 4.3bc 2.9c 2.6c
36.8b 41.4a 42.3a 41.9a 40.9a 41.4a
9.2c 8.3ab 8.1a 8.6b 9.8d 9.5cd
18.2c 19.2a 18.9ab 19.1a 19.1a 18.6bc
12.0a 11.5b 11.5b 11.3b 10.8c 11.3b
12.0a 12.0a 12.1ab 12.4ab 13.1c 12.5b
Transect 2 Commercial Recreational Residential Fallow agricultural
5.1a 4.7a 4.0b 3.1c
33.8b 39.0a 37.2a 33.9b
9.5b 8.1a 9.3b 11.3c
17.4a 17.3a 17.3a 17.3a
13.4b 15.9a 13.4b 12.5c
10.9b 8.8a 11.0b 11.9c
Transect 3 Industrial Commercial Residential
7.3a 6.9a 4.9b
40.4b 42.8b 56.6a
5.3b 5.0b 3.1a
22.3a 22.0a 21.6b
12.4b 13.0b 13.9a
8.1b 7.7ab 7.2a
Transect 4 Commercial Residential Vacant Desert Mixed Agricultural
7.9a 6.3b 6.0b 3.8c 3.0c 1.7d
19.0d 20.8c 21.2c 23.1b 25.3a 26.1a
14.3a 14.9b 14.7ab 15.5c 15.1b 15.7c
18.4a 18.3a 18.2a 17.5b 17.5b 17.4b
12.0bc 12.0bc 11.8c 12.2b 12.6a 12.5a
11.5a 11.5a 11.8bc 12.0c 11.6ab 11.9c
a Values are treatment means (n ¼ 6–320), values followed by the same letter within a column by transect were not statistically different at a ¼ 0:05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
T on D and NDVI yielded a significant R2 value of 0.49 (P4F ¼ 0:001).
Historical comparison In general, NDVI and T had both decreased and increased, respectively, along both transects 1 and 2 between 1976 and 1999 (Fig. 4A and B). For transect 1, conversion of vacant and agricultural lands to residential and commercial uses reduced a steep temperature gradient that was observed in 1976 (Fig. 4A). Within transect 2 land use changed relatively little between 1976 and 1999, while the temperature gradient between developed and agricultural areas increased slightly (Fig. 4B). Conversion of a dry wash in 1976 to a greenbelt flood control area in 1999 (Fig. 4B, 1.2 mile marker) resulted in an abrupt increase in NDVI along that short segment of transect 2. This increase in NDVI was not accompanied by a similar abrupt decrease in T or increase in Td or RH.
Discussion UHIs are a characteristic climate signature of cities and are created by a heterogeneous patchwork mosaic of soil, vegetation, and built surfaces that result from a variety of urban land uses. A novel approach of correlating local-scale microclimate measurements along mobile transects with the larger-scale NDVI was used in this study. Any confounding effects of microenvironment on microclimate were circumvented because Phoenix metropolitan with its flat topography has a systematic matrix of broad, asphalt-surfaced, arterial roads with sky view factors that are unobstructed by either tall buildings or street trees. Similar methodologies to the one used in this study may not be appropriate in other cities that have topographical variation and/or narrower roads and sky view factors affected by tall buildings or large street trees with spreading canopies. Additional support for the veracity of the multi-scalar approach used in this study was found in Fig. 4B. As reported above and shown in Fig. 4B, little variation
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Table 4. Effect of land use on normalized differential vegetation index (NDVI) along four 1999 transects in Phoenix, AZ, USA metropolitan Land use
NDVI
Transect 1 Agricultural Mixedb Vacant Residential Commercial Industrial
0.617aa 0.575b 0.573b 0.572b 0.537c 0.525c
Transect 2 Recreational Residential Commercial Fallow Agricultural
0.665a 0.552b 0.541bc 0.534c
Transect 3 Residential Commercial Industrial
0.619a 0.541b 0.506c
Transect 4 Agricultural Mixed Desert Residential Vacant Commercial
0.597a 0.559b 0.555b 0.549bc 0.549bc 0.536c
a Values are treatment means (n ¼ 6–320), values followed by the same letter within a column by transect are not statistically different at a ¼ 0:05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
was recorded in T or RH when crossing a greenbelt flood control area marked by an abrupt narrow increase in NDVI. The lack of any effect of the greenbelt flood control area on microclimate indicates that the footprint of land influencing fluxes at sensor height range was at least as large or larger than the footprint of the greenbelt wash itself. Therefore, we have a high degree of confidence that variations in microclimate that were detected were related to larger-scale changes in land use and vegetation index. Similar to Brazel and Johnson (1980), this study found that microclimate patterns of temperature and humidity in the Phoenix area were related to land use and vegetation index, particularly during calm, early morning conditions. Like other arid, low elevation, midlatitude continental cities, Phoenix summers are characterized by strong diurnal radiational heat loading because of high insolation intensity. Even though air temperatures and latent heat fluxes associated with water evaporation are usually highest during Phoenix summer afternoon hours, there were fewest significant correlations between microclimate, land use, and
vegetation index during this time. It is possible that increased daytime atmospheric mixing dissipated any cooling effects caused by increases in NDVI at the scale our measurements were made. During nighttime hours, dissipation of stored heat by the built urban fabric might be expected to most influence microscale temperature patterns and is most likely the main reason why industrial and commercial land uses generally had the highest T during the early morning hours of June and December 1999. In general, these findings support the hypothesis that changes in spatial patterns of land use and plant density over time would influence urban microclimates. However, there were unexpected relationships between NDVI and temperature and humidity during 1999 for portions of transects 2 and 4 that were likely caused by a temporal disparity between the NDVI image that was available for analysis, taken in April 1998, and collection of the microclimate data during June and December of 1999. The eastern portion of transect 2 was comprised almost exclusively of fallow agricultural fields when the NDVI image was captured during April 1998. However, these same fields were cultivated with cotton plants during June 1999 when microclimate data were collected. The western portion of transect 4 was at the urban fringe in an area of rapid development during 1998–99 (Fig. 1). Land surfaces that were actively agricultural during April 1998, but had changed to residential land use by June and December 1999. Urban forest cover normally mitigates urban heating by shading and evapotranspiration (Grimmond et al. 1996; Grimmond and Oke, 1999). Residuals plots of adjusted T (Fig. 2) suggests that a univariate model using only NDVI to predict microclimate within the Phoenix metropolitan area tends to underestimate T at relatively low NDVI and overestimate T at relatively high NDVI. These residual plot patterns coupled with correlations between NDVI, D, and microclimate (Table 5; Fig. 3) suggest that microclimate patterns in the Phoenix metropolitan area are caused by more than just variations in NDVI and are likely an interaction of urban forest cover with non-vegetative surfaces related to degree of urbanization near the urban core. Moreover, the presence of an urban to rural temperature gradient and the trend toward increased urban heating over time as evidenced by the comparison of 1976 and 1999 microclimate data, suggests that the UHI effect in desert cities like Phoenix is more likely caused by increased anthropogenic surface covers than relative decreases in vegetation density. Local meteorological records show the existence of a UHI in Phoenix since at least 1948 (Baker et al., 2002). Evidence of the relatively sharp urban to rural temperature gradient that was recorded by Brazel and Johnson (1980) during 1976 was less apparent in 1999 because former agricultural lands in parts of the
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Table 5. Pearson’s correlation coefficients (r-values) showing the degree of associations between normalized differential vegetation index and near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects in Phoenix, Arizona, USA metropolitan during early morning (0500 HR) and afternoon (1500 HR) of June and December 1999 June
December
0500 HR
1500 HR
0500 HR
1500 HR
r-value
P4F
r-value
P4F
r-value
P4F
r-value
P4F
Transect 1 T RH Td
0.54 0.58 0.57
0.01 0.01 0.01
0.01 0.02 0.02
0.87 0.82 0.78
0.43 0.22 0.11
0.01 0.01 0.20
0.01 0.26 0.01
0.95 0.01 0.98
Transect 2 T RH Td
0.05 0.25 0.58
0.75 0.10 0.01
0.14 0.09 0.01
0.40 0.58 0.97
0.13 0.40 0.48
0.42 0.01 0.01
0.08 0.79 0.76
0.63 0.01 0.01
Transect 3 T RH Td
0.78 0.77 0.67
0.01 0.01 0.01
0.54 0.69 0.62
0.01 0.01 0.01
0.65 0.72 0.72
0.01 0.01 0.01
0.34 0.54 0.48
0.01 0.01 0.01
Transect 4 T RH Td
0.37 0.12 0.32
0.01 0.11 0.01
0.33 0.08 0.13
0.01 0.30 0.09
0.44 0.44 0.05
0.01 0.01 0.53
0.32 0.46 0.15
0.01 0.01 0.04
Fig. 2. The relationship between residual Type IV sums of squares of adjusted near-surface air temperature (T) and adjusted T for all four transects during early morning (0500 HR) of June 1999. NDVI was used as the independent variable.
Phoenix area had been displaced by suburban development. These data suggest that amplification of the Phoenix UHI, a direct result of urban expansion, is spatially variable along an urban to rural gradient. Compared to UHI patterns in more mesic climates (Oke and Maxwell, 1975; McDonnell and Pickett, 1990;
Fig. 3. The relationship between adjusted near-surface air temperature (T) and distance (D) from the Phoenix, AZ, USA urban core for all four transects during early morning (0500 HR) of June 1999; Y ¼ 0:88420:163X ; R2 ¼ 0:403:
Miller, 1997; Bornstein and Lin, 2000), desert cities like Phoenix may actually have an oasis or cooling effect during summer daytime hours caused by patchy urban forest cover densities that are higher than vegetation densities found in surrounding desert habitats.
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Fig. 4. Historical comparison of near-surface air temperature (T), NDVI, and land use along (A) transect 1 and (B) transect 2 in Phoenix, AZ, USA metropolitan. T ¼ F=E; NDVI ¼ - - - -/n.
In summary, findings from this study show that urban plant cover is a modifier of microclimate in Phoenix, a desert city, and supports use of urban greening to mitigate urban heating (McPherson, 2001). Evidence was obtained from this study of a heterogeneous patchwork of microclimates related to socioeconomic land use, as well as an urban to rural decrease in T not associated with an increased density of urban forest cover at the urban fringe as is more typical of urban areas in mesic climates. Though urban forest cover can modify microclimate in Phoenix, these findings were less supportive of the hypothesis that urban forest cover and latent heat fluxes are the principle determinants of microclimate in the Phoenix area. Instead, this study suggests that urban microclimates are more a result of vegetation densities interacting with other factors of the urban fabric such as parking lots (Celestian and Martin, 2004) and buildings. More intensive evaluation of variations in land surface cover type and their effect on urban heating is needed, and strategies of urban greening to mitigate UHI effects in desert cities also need to be counterbalanced against the need to conserve water resources.
Acknowledgments This research was funded, in part, by the National Science Foundation’s Central Arizona Phoenix LongTerm Ecological Research Grant No. DEB-9714833.
References American Planning Association, 2004. http://www.planning.org/lbcs/, last accessed 7 July 2004. Avissar, R., 1996. Potential effects of vegetation of the urban thermal environment. Atmospheric Environment 30, 437–448. Baker, L.A., Brazel, A.J., Selovar, N., Martin, C.A., McIntyre, N., Steiner, F.R., Nelson, A., Musacchio, L., 2002. Urbanization and warming of Phoenix (Arizona, USA): impacts, feedbacks and mitigation. Urban Ecosystems 6, 188–203. Barradas, V.L., Tejeda-Martinez, A., 1999. Energy balance measurements in a suburban vegetated area in Mexico City. Atmospheric Environment 33, 4109–4113. Bornstein, R.D., Lin, Q., 2000. Urban heat islands and summertime convective thunderstorms in Atlanta: three case studies. Atmospheric Environment 34, 507–516.
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Brazel, A., Johnson, D., 1980. Land use effects on temperature and humidity in the Salt River Valley, Arizona. Journal of Arizona-Nevada Academy of Science 15, 54–61. Brazel, A., Selover, N., Vose, R., Heisler, G., 2000. A tale of two climates: BALTIMORE and Phoenix urban LTER sites. Climate Research 15, 123–135. Burba, G., 2001. Illustration of flux footprint estimates affected by measurement height, surface roughness, and thermal stability. In: Hubbard, K., Sivakumar, M. (Eds.), Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Use and Future Perspectives. World Meteorological Organization, Lincoln, NE. Celestian, S.B., Martin, C.A., 2004. Rhizosphere, surface and air temperature patterns at parking lots in Phoenix, Arizona, US. Journal of Arboriculture 30, 245–252. Delorme Mapping, 1993. Arizona Atlas and GazetteerTM, first ed. Freeport, ME, USA. de Schiller, S., Evans, J., 1996. Training architects and planners to design with urban microclimates. Atmospheric Environment 30, 449–454. Eliasson, I., 2000. The use of climate knowledge in urban planning. Landscape and Urban Planning 48, 31–44. Gammage, G., 1999. Phoenix in Perspective: Reflection on Developing the Desert. Herberger Center for Design Excellence, Arizona State University, Tempe, AZ. Gomez, A., Gomez, K., 1984. Statistical Procedures for Agricultural Research, second ed. Wiley, New York, p. 288. Grimmond, C.S., Oke, T., 1999. Heat storage in urban areas: local-scale observations and evaluation of a simple model. Journal of Applied Meteorology 38, 922–940. Grimmond, C.S., Souch, C., Hubble, M., 1996. Influence of tree cover on summertime surface energy balance fluxes, San Gabriel Valley, Los Angeles. Climate Research 6, 45–57. Hope, D., Gries, C., Carroll, S., Zhu, W., Redman, C.L., Grimm, N.B., Nelson, A., Kinzig, A., Martin, C.A., 2003. Socio-economics drive urban plant diversity. Proceedings of the National Academy of Sciences 100 (15), 8788–8792. Jo, H., McPherson, E.G., 2001. Indirect carbon reduction by residential vegetation and planting strategies in Chicago, USA. Journal of Environmental Management 61, 165–177. Maricopa Association of Governments, 2004. http:// www.mag.maricopa.gov/display.cms, last accessed 7 July 2004. Martin, C.A., Stabler, L.B., 2002. Plant gas exchange and water status in urban desert landscapes. Journal of Arid Environments 51, 235–254.
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McDonnell, M., Pickett, S., 1990. Ecosystem structure and function along urban–rural gradients: an unexploited opportunity for ecology. Ecology 71, 1232–1237. McPherson, E.G., 2001. Sacramento’s parking lot shading ordinance: environmental and economic costs of compliance. Landscape and Urban Planning 57, 105–123. Miller, R., 1997. Urban Forestry: Planning and Managing Urban Greenspaces, second ed. Prentice Hall, New Jersey. Morrison Institute for Public Policy, 1998. Urban Growth Management in Phoenix. School of Public Affairs, Arizona State University, Tempe, AZ. Oke, T.R., 1989. The micrometeorology of the urban forest. Philosophical Transactions of the Royal Society of London 324, 335–349. Oke, T.R., Maxwell, G.B., 1975. Urban heat island dynamics in Montreal and Vancouver. Atmospheric Environment 9, 191–200. Sailor, D.J., 1998. Simulations of annual degree-day impacts of urban vegetation augmentation. Atmospheric Environment 32, 43–52. Schmid, H., Oke, T., 1990. A model to estimate the source area contributing to turbulent exchange in the surface layer over patchy terrain. Quarterly Journal of the Royal Meteorological Society 116, 95–988. Sellers, W., Hill, R., 1974. Arizona climate, 1931–1972, second ed. University of Arizona Press, Tucson, AZ. Simpson, J., McPherson, E., 1998. Simulation of tree shade impacts on residential energy use for space conditioning in Sacramento. Atmospheric Environment 32, 69–74. Stabler, L.B., 2003. Ecosystem function of urban plants in response to landscape management. Ph.D. Dissertation, Arizona State University, Tempe, AZ. Stefanov, W., Ramsey, M., Christensen, P., 2001. Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of the Environment 77, 173–185. Thamm, H.P., Gossmann, H., Richter, C.J., Ro¨ckle, R., 1999. Conflicting interests of planners, politicians and scientists in urban climatology as an example of Freiburg. I. Berlin, Germany. In: Proceedings of the Conference ICB-ICUC ’99, 8–11 August, Sydney. Biometeorology and Urban Climatology at the Turn of the Millennium, Conference Abstract, ICUC12.2. United States Environmental Protection Agency, 2004. http:// www.epa.gov/urban/phx/indicators.htm, last accessed 7 July 2004. Walker, J., Briggs, J., 2004. Remote sensing of Phoenix’s urban forest using high-resolution aerial photography. In: Central Arizona Phoenix Long Term Ecological Research Symposium, pp. 41–42.
Urban Forestry & Urban Greening 3 (2005) 137–147 www.elsevier.de/ufug
Microclimates in a desert city were related to land use and vegetation index Linda B. Stablera, Chris A. Martina,, Anthony J. Brazelb a Department of Applied Biological Sciences, Arizona State University East, 7001 East William Field Road, Building 130, Mesa, AZ 85212, USA b Department of Geography, Arizona State University, AZ, USA
Abstract A heterogeneous patchwork mosaic of soil, vegetation, and built surfaces that result from a variety of urban land uses cause urban microclimates within cities. We studied the seasonal relationships of land use, urban plant cover and microclimate in Phoenix, Arizona, USA, metropolitan. Early morning (0500 HR) and afternoon (1500 HR) nearsurface temperatures and humidities were measured along multiple transects in this desert city and outlying areas during June and December 1999. A Landsat thematic mapper normalized differential vegetation index (NDVI) image was used to quantify spatial patterns of plant density. Land use had the most pronounced effect on microclimate during the early morning hours of summer. Agricultural and residential land uses had the highest relative humidities, dew point temperatures, and NDVI, and the lowest air temperatures. Commercial and industrial land uses had highest temperatures and lowest NDVI. Temperatures were generally negatively correlated to NDVI, while humidity and dew point temperatures were generally positively correlated to NDVI. Distance from the urban core did not affect NDVI but had a significant negative effect on adjusted air temperature. In addition, a historical comparison of land use, NDVI and microclimate data collected during 1976 and again during 1999 along two transects revealed overall decreases in NDVI and relative increases in air temperature indicative of urban expansion. These findings show that microclimates in this desert city are caused by more than just variations in plant cover, and are likely an interactive effect of vegetation density and other non-vegetative urban surfaces. r 2004 Elsevier GmbH. All rights reserved. Keywords: Humidity; Landscape; Land cover; Temperature; Urban heat island
Introduction The urban heat island (UHI) effect is a welldocumented phenomenon and the need to incorporate knowledge of urban climate in the management of urban forests has been acknowledged (de Schiller and Evans, 1996; Thamm et al., 1999; Eliasson, 2000). Built Corresponding author.
E-mail address: [email protected] (C.A. Martin). 1618-8667/$ - see front matter r 2004 Elsevier GmbH. All rights reserved. doi:10.1016/j.ufug.2004.11.001
surfaces such as asphalt and concrete absorb, store, and reradiate more thermal energy per unit area than do plants and soil. Also, loss of pervious soil surfaces in densely urbanized areas increase surface water runoff, reduce soil water storage capacity, and reduce evaporative cooling of the air. Conversely, urban forest plantings can modify urban microclimates by shading and evapotranspiration, and use of plants to ameliorate urban heating is one strategy that has generated significant interest (Sailor, 1998; Simpson and McPherson, 1998; Jo and McPherson, 2001).
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The relative cover of urban forests and anthropogenic surfaces influences urban climate at several scales. Trees and buildings existing singly or in clusters create strong spatial variability in local heat transfer fluxes that define urban microclimates within the urban canopy layer (Oke, 1989). The UHI is a mesoscale climate phenomenon that at the urban boundary layer reflects an integration of a heterogeneous matrix of microclimates within the urban canopy layer (Schmid and Oke, 1990). Although microclimates at the urban canopy layer scale are of interest in urban planning because they most affect levels of human comfort, urban microclimate heterogeneity also has a direct effect on other biotic processes such as microbial, plant and animal respiration and plant photosynthesis (Baker et al., 2002). Urban forests might have the greatest impact on heat transfer fluxes in hot, arid regions with mild winters due to effects of irrigation-enhanced evapotranspiration during dry summer seasons and continuous metabolic activity of plants throughout the year (Avissar, 1996; Barradas and Tejeda-Martinez, 1999). The metropolitan area of Phoenix, Arizona, is situated in the Sonoran Desert in the southwestern United States. Diversion of water from the Salt and Colorado Rivers for irrigation usage has facilitated creation of an urban forest with tree canopy coverage of about 13% of total urban surface area (Stabler, 2003; Walker and Briggs, 2004). Although the UHI effect has been well documented in the Phoenix area (Brazel et al., 2000), the classic urban to rural increase in plant cover typical of urban forests in more mesic climates (Miller, 1997) does not apply there, where urban forest density is greater than in the surrounding desert habitats (Martin and Stabler, 2002). Within Phoenix where virtually all managed landscapes are irrigated, evapotranspiration might be expected to especially influence microclimates relative to those in more mesic cities where supplemental water is not continuously added to the landscape. An analysis of 107 randomly chosen study sites within the Phoenix metropolitan area showed that land cover by surfaces such as asphalt, concrete or soil, as well as plant canopy area, was a function of urban land use (Stabler, 2003). The purpose of this study was to examine spatial patterns of land use, plant density, and microclimate in the Phoenix metropolitan area, a mixed desert, agricultural, and urban area. Because Phoenix is in the Sonoran Desert and subject to arid climatic conditions, it was hypothesized that (1) spatial heterogeneity in temperature and atmospheric moisture would be related to urban land use; (2) land use would influence urban plant density; (3) plant densities and associated latent heat fluxes would be the most important factor influencing microclimates; and (4) changes in spatial patterns of land use and plant density over time would influence urban microclimates. To test
these hypotheses, spatial patterns of microclimate conditions, land use, and urban plant density derived from a normalized differential vegetation index (NDVI) were studied along four transects consisting of major arterial roadways within the Phoenix metropolitan area. These transects traversed a broad range of urban and suburban land uses and included both the urban core and urban fringe. In addition, historical NDVI and microclimate datasets (Brazel and Johnson, 1980) from two of the four transects were used to evaluate how land use conversion over time affected NDVI and microclimate.
Methods Description of the study area Phoenix, Arizona (331260 N, 112110 W) is situated within the lower Salt River basin on the northeastern edge of the Sonoran Desert in the southwestern United States and is one of the most rapidly expanding metropolitan areas in the United States. Phoenix metropolitan is the fifth largest urban region in the United States with an estimated population of 3.3 million and urban surface area of 2223 km2 (United States Environmental Protection Agency, 2004). Archeological evidence indicates that over 500 years ago, native Americans used land along the Salt River extensively for agricultural purposes via constructed canals (Gammage, 1999). During the late 19th century, European American settlers refurbished those canals, enabling Phoenix to grow rapidly as an agricultural community. During the last half of the 20th century, much of these formerly agricultural lands were displaced by residential and commercial development. In recent years, the Phoenix metropolitan area has expanded at a rate of over ‘‘an acre an hour’’ (Morrison Institute Public Policy, 1998), new construction encroaches on desert lands previously undisturbed by agricultural activities, and land use changes within the city boundaries are common. The metropolitan area includes Phoenix and several contiguous smaller city centers and surrounding suburban areas. Mean annual precipitation in the Phoenix area is 180 mm with approximately 50% occurring as late summer thunderstorms, the remainder normally associated with winter frontal systems originating in the Pacific Ocean (Sellers and Hill, 1974). Mean summer air temperature (T) is 30.8 1C and winters are mild with average T of 11.3 1C. During the summer data collection period (8–18 June 1999) T and relative humidity (RH) at the United States National Weather Service reporting station at Phoenix Sky Harbor International Airport ranged from 21.1 to 40.6 1C and 11–43%, respectively.
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During the winter period (13–18 December 1999), T and RH ranged from 2.8 to 21.7 1C and 6–49%, respectively.
Transect selection and land use evaluation Four transects along major arterial asphalt roads were selected for study of urban microclimates. The four transects were generally over flat topography and were selected to cover the spread of the greater Phoenix metropolitan area, include heterogeneous patterns of land use, evaluate both the urban core and the urban fringe, and include areas previously studied in 1975–76 and currently in land use transition. Fig. 1 shows a map of the Phoenix metropolitan area with coarse-scale land use/land cover characterization (Stefanov et al., 2001)
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and the locations of each transect. Major arterial asphalt roads used for all four transects had an average width of 22 m. Transects 1 and 2 were chosen to repeat work conducted by Brazel and Johnson (1980). The northern half of transect 1 was an area of mixed commercial and residential land uses that were developed prior to 1976, while the southern half of transect 1 was predominantly agricultural land during 1976 but has since been developed into residential and commercial land uses. Transect 2 had little historical change in land use. The eastern portion of transect 2 traversed a portion of the Pima Indian Reservation that had remained agricultural, while the western portion of transect 2 had remained a mixture of commercial and residential urban land use. Transect 3 traversed the Phoenix urban core along its
Fig. 1. Map of study area within Phoenix, AZ, USA metropolitan showing location of transects and major land cover as classified by the Maricopa Association of Governments (2004).
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southern extent and a mature, densely vegetated residential area to the north that was predominately flood irrigated. Transect 4 traversed a variety of urban land uses, and along its western extent crossed the urban fringe into the Sonoran Desert. Land use along each transect was evaluated and classified based on socioeconomic use and function of structures present, degree of site development, and observed human activity visible from the transect centerline using typology of the American Planning Association (2004). Land use along each transect was categorized in 161 m (0.1 mile) increments because Phoenix is laid out in gridded blocks defined by main street intersections at 1609 m (1 mile) intervals. While some heterogeneity existed at the 1609 m scale, land use within a developed block tended to be homogenous to a distance of 285 m in either direction away from the transect center point. When no single land use category could be applied to a segment due to heterogeneity, land use was classified as mixed. For example, a mixed land use classification was assigned to a portion of the southern extent of transect 1 (1999 data) at the urban fringe where on one side of a road was residential housing and on another side was agricultural fields. The details of land and plant cover for transects were not evaluated from the ground due to the impracticality of undertaking such a task for over 600,161 m segments. However, earlier studies found that land and plant cover within Phoenix metropolitan was correlated to land use, and building and tree heights were similar across land uses (Hope et al., 2003; Stabler, 2003). These studies also found that mean building and tree heights in Phoenix were only 5.6 and 4.4 m, respectively. Because the Phoenix metropolitan area is comprised of mostly low buildings and small trees with canopies that do not extend over road surfaces, variations in sky view factors were not considered to be an important determinant of our microclimate data.
Microclimate measurements Near-surface temperature (T) and RH were measured at approximately 161 m intervals along each transect using a moving (average speed 48 km/h) vehicle outfitted with meteorological sensors. This measurement regimen enabled us to collect multiple measurements within a land use classification. All data were collected from approximately 0500–0630 HR (early morning) and 1500–1630 HR on days of clear, calm (windless) anticyclonic conditions during June and December 1999. For transect 2, data were also collected at 2200 HR during March 1999 for comparison to data collected there during March 1976. Two shielded copper constantan thermocouples positioned in front of the moving vehicle at 0.5 m above ground were used to measure T.
Also, RH and T were measured with a HMP 45 1C temperature and relative humidity probe (Campbell Scientific, Logan, UT, USA) positioned over the moving vehicle 2.0 m above ground. From these data, dew point temperatures (Td) were calculated. Reported T values are the mean of the three T measurements. All data were collected every 12 s with a 21 micrologger (Campbell Scientific, Logan, UT, USA). Each transect was assessed at least twice, traveling in opposite directions, and means for each 161 m spatial increment along each transect were calculated to reduce the effects of temporal changes in microclimate which may have occurred during the course of each run. Iterations of measurements were numbered and recorded on detailed maps of each transect at the time of data collection, and data recorded during periods when the vehicle was stopped at traffic lights were removed from the analysis. Voice recordings were also made to confirm the exact location of each microclimate data point. The relatively broad and unshaded asphalt roads were a surface common to all transects, thus effectively negating any concerns that differences in the data were representative of variations in the immediate (a few meters) microclimate around the sensors.
Vegetation index Plant cover density along each transect was evaluated using a NDVI map produced from a Landsat thematic mapper (TM) image of the Phoenix area (spheroid Clark 1866, zone 12, Datum NAD27, georeferenced to UTM), taken in April 1998, approximately 14 months prior to collection of microclimate data. The NDVI image was obtained from researchers evaluating the spatial and temporal distribution of land cover in the Phoenix urban area (Stefanov et al., 2001). The spatial resolution of the image was 28.5 m (0.018 mi) per pixel. All NDVI data were converted from metric units of distance to miles to conform to land use classifications. To accurately locate each transect on the NDVI image, the map coordinates of the beginning and end of each transect were determined using a standard topographical and quadrangular map of the Phoenix metropolitan area (scale of 1:250,000), with crosshairs corresponding to USGS 15-minute maps (Delorme Mapping, 1993). Based on those coordinates, areas of interest were created using ERDAS Imagines software with transect widths of 20 Landstat TM pixels (570 m), with the road defining the transect at its central axis. The 20 pixels associated with transect width were selected based on the height of T and RH sensors at 0.5–2.0 m. Most of the footprint of land influencing fluxes at the sensor height range was expected to be within 20–450 m of the sensors (Burba, 2001).
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Table 1. Mean daily values of air temperature (T) in degrees Celsius, sea-level pressure (SLP) in mbs, specific humidity (SH) in g/ kg, % relative humidity (RH), geopotential height (GPH) of the 500 mb surface in m, and wind direction (WD) interpolated from synoptic mapping for microclimate sampling dates during March and June of 1976 and 1999, Phoenix, AZ USA
March 1976 March 1999 June 1976 June 1999
T
SLP
SH
RH
GPH
WD
11 15 27 26
1017 1015 1011 1011
0.006 0.006 0.004 0.004
50 50 20 23
5600 5650 5825 5775
W N S NE
Historical comparison Brazel and Johnson (1980) collected microclimate data along transect 1 at 0500 HR during June 1976 and along transect 2 at 2200 HR during March 1976 using similar car-mounted apparati. In their study, T was measured at approximately 1 m above the ground for both transects, and atmospheric moisture was measured using a thermister psychometric system on transect 1. 1975 NDVI values were calculated for transects 1 and 2 from a 1975 Landsat TM image (Stefanov et al., 2001) as described above for the 1998 NDVI data. Regional-scale climatic conditions for the appropriate sampling periods during 1976 and 1999 are shown in Table 1. During the two June microclimate sampling dates for transect 1, synoptic-scale conditions of temperature and atmospheric moisture were similar during 1976 and 1999. Synoptic-scale conditions differed slightly during the sampling dates for transect 2 during March of 1976 and 1999. Mean daily temperature and sea-level pressure were somewhat lower during the 1976 sampling period, while relative humidity was similar. Wind direction varied for both historical comparison dates, but wind speed was low for all.
Statistical analyses Analyses of variance were conducted for 1999 transect data using a general linear model procedure (PC SAS, version 6.03, Cary, NC, USA) with land use as the independent treatment variable and T, RH, Td, and NDVI as response variables. Type IV sums of squares were used because land use classifications had unequal sample sizes for vegetation indices and microclimate observations. Duncan’s multiple range tests were employed to compare treatment means, with a significance level of a ¼ 0:05: Pearson correlation coefficients were calculated to measure degree of association between vegetation and microclimate parameters using a regression model with NDVI as the independent variable and T, RH, or Td as the dependent variable. Residuals of Type IV sums of squares for each observation of the dependent variables
were plotted against the dependent variables to check model assumptions and look for significant statistical patterns. Regression analyses of NDVI and pooled T values for early morning June transects adjusted to cotemporal temperatures at the United States National Weather Service reporting station at Phoenix Sky Harbor International Airport were made as a function of distance from the urban core (D) to detect the presence of any urban to rural gradients for NDVI and/ or NDVI. Due to lack of a consistent and reliable reference weather station in the Phoenix area, no statistical comparisons between the 1976 and 1999 data were made, and only patterns are discussed within the context of this study.
Results Land use effects on microclimate Land use affected microclimate along all four transects (P4F ¼ 0:01; Tables 2 and 3), but land use effects on microclimate were generally more pronounced at 0500 than 1500 HR for both June and December. During June 1999, the greatest effect of land use on microclimate occurred along transect 1 where nearsurface T in commercial land uses was on average 8.5 1C higher than in agricultural land uses (Table 2) and nearsurface RH and Td were 16.2% and 5.9 1C, respectively, higher in agricultural than in commercial land uses (Table 2). Variations in land use least affected microclimate along transects at 1500 HR during December when the range of T, Td and RH were only 2 1C, 4 1C and 5%, respectively (Table 3).
Vegetation index Land use affected NDVI in all transects (P4F ¼ 0:01; Table 4). The range of NDVI for all transects was from 0.506 in the dry river bottom, to 0.665 in a recreational, flood control greenbelt with a golf course and small lakes. Commercial land uses had NDVI values that ranged from 0.533 to 0.541,
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Table 2. Effects of land use on near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects during early morning (0500 HR) and afternoon (1500 HR) of June 1999, Phoenix, AZ, USA metropolitan 0500 HR
1500 HR
T (1C)
RH
Td (1C)
T (1C)
RH
Td (1C)
Transect 1 Commercial Residential Industrial Mixedb Vacant Agricultural
21.5aa 20.8ab 20.2bc 18.0cd 17.5d 13.0e
17.5d 19.1d 18.7d 24.9c 30.9b 33.7a
4.8d 3.9d 4.7d 1.8c 0.0b 1.1a
35.7a 35.5ab 34.8c 35.4ab 35.2b 35.3b
6.0b 6.4b 6.7b 6.1b 6.4b 7.8a
7.4b 6.8b 6.7b 7.5b 6.9b 4.9a
Transect 2 Commercial Residential Recreational Fallow agricultural
22.6a 21.1b 21.1b 17.9c
25.4c 31.2b 33.6ab 36.0a
0.7c 2.5b 4.3a 2.4b
36.1a 35.9a 36.2a 35.4b
9.4b 9.6ab 10.1a 9.9ab
1.2b 1.0ab 0.2a 0.9ab
Transect 3 Commercial Industrial Residential
30.0a 29.8a 27.8b
22.1b 21.2b 27.4b
6.0b 5.7b 7.4a
41.6a 41.7a 40.9b
8.8b 8.6b 10.1a
2.1b 1.7b 3.4a
Transect 4 Commercial Vacant Residential Agricultural Mixed Desert
27.2a 25.8b 25.7b 25.2c 24.8c 24.8c
35.3a 35.8a 35.2a 28.7b 35.3a 35.3a
10.2a 9.3b 9.0b 5.3d 8.0c 8.0c
41.0a 40.8a 41.0a 40.3b 40.4b 40.5b
10.0a 8.6b 9.5ab 8.6b 8.6b 7.3c
3.0a 0.8b 2.2a 0.5b 0.7b 1.5c
a Values are treatment means (n ¼ 6 –320), values followed by the same letter within a column by transect are not statistically different at a ¼ 0.05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
residential from 0.541 to 0.619, and agricultural from 0.534 (fallow) to 0.656 (active). The greatest range of NDVI within a single transect occurred along the shortest, with a low value of 0.534 and a high of 0.665 on transect 2.
Correlation of vegetation index to microclimate Microclimate was most significantly correlated to NDVI along transect 3 which crossed through the urban core along its southern extent and a mature, densely vegetated residential area to the north that was predominately flood irrigated (Table 5). When significant, T was negatively correlated to NDVI (r ¼ 0:32 to –0.78) and RH was positively correlated to NDVI (r ¼ 0:2220:77). Also when significant, Td was positively correlated to NDVI (r ¼ 0:4820:72) except along transect 4 where Td was negatively correlated to NDVI during June 0500 HR. There were no significant correlations between microclimate and NDVI along transects 1 and 2 during June 1500 HR.
An analysis of the relationship between residual Type IV sums of squares of adjusted T and adjusted T at June 0500 HR showed a distinct pattern when NDVI was used as the independent variable (Fig. 2). Numerical transformation of the data failed to alter the residual plot pattern. This pattern is suggestive of some independent variable missing from the regression model (Gomez and Gomez, 1984.)
Urban to rural gradients The weak relationship between NDVI and D (R2 ¼ 0:003; P4F ¼ 0:001; data not shown) indicated a general lack of an urban to rural vegetation gradient in Phoenix. Conversely, adjusted T at 0500 HR in June was negatively correlated to D (Fig. 3). Using Pearson’s correlation coefficients within each transect, the degree of association between T and D ranged from an r ¼ 0:51 near the urban core (transect 3) to r ¼ 0:97 at the urban fringe (transect 4), confirming a UHI effect unrelated to vegetation cover. Multivariate regression of
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Table 3. Effects of land use on near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects during early morning (0500 HR) and afternoon (1500 HR) of December 1999, Phoenix, AZ, USA metropolitan 0500 HR
1500 HR
T (1C)
RH
Td (1C)
T (1C)
RH
Td (1C)
Transect 1 Industrial Commercial Residential Mixedb Vacant Agricultural
8.5aa 7.1ab 6.0abc 4.3bc 2.9c 2.6c
36.8b 41.4a 42.3a 41.9a 40.9a 41.4a
9.2c 8.3ab 8.1a 8.6b 9.8d 9.5cd
18.2c 19.2a 18.9ab 19.1a 19.1a 18.6bc
12.0a 11.5b 11.5b 11.3b 10.8c 11.3b
12.0a 12.0a 12.1ab 12.4ab 13.1c 12.5b
Transect 2 Commercial Recreational Residential Fallow agricultural
5.1a 4.7a 4.0b 3.1c
33.8b 39.0a 37.2a 33.9b
9.5b 8.1a 9.3b 11.3c
17.4a 17.3a 17.3a 17.3a
13.4b 15.9a 13.4b 12.5c
10.9b 8.8a 11.0b 11.9c
Transect 3 Industrial Commercial Residential
7.3a 6.9a 4.9b
40.4b 42.8b 56.6a
5.3b 5.0b 3.1a
22.3a 22.0a 21.6b
12.4b 13.0b 13.9a
8.1b 7.7ab 7.2a
Transect 4 Commercial Residential Vacant Desert Mixed Agricultural
7.9a 6.3b 6.0b 3.8c 3.0c 1.7d
19.0d 20.8c 21.2c 23.1b 25.3a 26.1a
14.3a 14.9b 14.7ab 15.5c 15.1b 15.7c
18.4a 18.3a 18.2a 17.5b 17.5b 17.4b
12.0bc 12.0bc 11.8c 12.2b 12.6a 12.5a
11.5a 11.5a 11.8bc 12.0c 11.6ab 11.9c
a Values are treatment means (n ¼ 6–320), values followed by the same letter within a column by transect were not statistically different at a ¼ 0:05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
T on D and NDVI yielded a significant R2 value of 0.49 (P4F ¼ 0:001).
Historical comparison In general, NDVI and T had both decreased and increased, respectively, along both transects 1 and 2 between 1976 and 1999 (Fig. 4A and B). For transect 1, conversion of vacant and agricultural lands to residential and commercial uses reduced a steep temperature gradient that was observed in 1976 (Fig. 4A). Within transect 2 land use changed relatively little between 1976 and 1999, while the temperature gradient between developed and agricultural areas increased slightly (Fig. 4B). Conversion of a dry wash in 1976 to a greenbelt flood control area in 1999 (Fig. 4B, 1.2 mile marker) resulted in an abrupt increase in NDVI along that short segment of transect 2. This increase in NDVI was not accompanied by a similar abrupt decrease in T or increase in Td or RH.
Discussion UHIs are a characteristic climate signature of cities and are created by a heterogeneous patchwork mosaic of soil, vegetation, and built surfaces that result from a variety of urban land uses. A novel approach of correlating local-scale microclimate measurements along mobile transects with the larger-scale NDVI was used in this study. Any confounding effects of microenvironment on microclimate were circumvented because Phoenix metropolitan with its flat topography has a systematic matrix of broad, asphalt-surfaced, arterial roads with sky view factors that are unobstructed by either tall buildings or street trees. Similar methodologies to the one used in this study may not be appropriate in other cities that have topographical variation and/or narrower roads and sky view factors affected by tall buildings or large street trees with spreading canopies. Additional support for the veracity of the multi-scalar approach used in this study was found in Fig. 4B. As reported above and shown in Fig. 4B, little variation
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Table 4. Effect of land use on normalized differential vegetation index (NDVI) along four 1999 transects in Phoenix, AZ, USA metropolitan Land use
NDVI
Transect 1 Agricultural Mixedb Vacant Residential Commercial Industrial
0.617aa 0.575b 0.573b 0.572b 0.537c 0.525c
Transect 2 Recreational Residential Commercial Fallow Agricultural
0.665a 0.552b 0.541bc 0.534c
Transect 3 Residential Commercial Industrial
0.619a 0.541b 0.506c
Transect 4 Agricultural Mixed Desert Residential Vacant Commercial
0.597a 0.559b 0.555b 0.549bc 0.549bc 0.536c
a Values are treatment means (n ¼ 6–320), values followed by the same letter within a column by transect are not statistically different at a ¼ 0:05 by Duncan’s Multiple Range Test. b Mixed ¼ residential, commercial, and/or vacant.
was recorded in T or RH when crossing a greenbelt flood control area marked by an abrupt narrow increase in NDVI. The lack of any effect of the greenbelt flood control area on microclimate indicates that the footprint of land influencing fluxes at sensor height range was at least as large or larger than the footprint of the greenbelt wash itself. Therefore, we have a high degree of confidence that variations in microclimate that were detected were related to larger-scale changes in land use and vegetation index. Similar to Brazel and Johnson (1980), this study found that microclimate patterns of temperature and humidity in the Phoenix area were related to land use and vegetation index, particularly during calm, early morning conditions. Like other arid, low elevation, midlatitude continental cities, Phoenix summers are characterized by strong diurnal radiational heat loading because of high insolation intensity. Even though air temperatures and latent heat fluxes associated with water evaporation are usually highest during Phoenix summer afternoon hours, there were fewest significant correlations between microclimate, land use, and
vegetation index during this time. It is possible that increased daytime atmospheric mixing dissipated any cooling effects caused by increases in NDVI at the scale our measurements were made. During nighttime hours, dissipation of stored heat by the built urban fabric might be expected to most influence microscale temperature patterns and is most likely the main reason why industrial and commercial land uses generally had the highest T during the early morning hours of June and December 1999. In general, these findings support the hypothesis that changes in spatial patterns of land use and plant density over time would influence urban microclimates. However, there were unexpected relationships between NDVI and temperature and humidity during 1999 for portions of transects 2 and 4 that were likely caused by a temporal disparity between the NDVI image that was available for analysis, taken in April 1998, and collection of the microclimate data during June and December of 1999. The eastern portion of transect 2 was comprised almost exclusively of fallow agricultural fields when the NDVI image was captured during April 1998. However, these same fields were cultivated with cotton plants during June 1999 when microclimate data were collected. The western portion of transect 4 was at the urban fringe in an area of rapid development during 1998–99 (Fig. 1). Land surfaces that were actively agricultural during April 1998, but had changed to residential land use by June and December 1999. Urban forest cover normally mitigates urban heating by shading and evapotranspiration (Grimmond et al. 1996; Grimmond and Oke, 1999). Residuals plots of adjusted T (Fig. 2) suggests that a univariate model using only NDVI to predict microclimate within the Phoenix metropolitan area tends to underestimate T at relatively low NDVI and overestimate T at relatively high NDVI. These residual plot patterns coupled with correlations between NDVI, D, and microclimate (Table 5; Fig. 3) suggest that microclimate patterns in the Phoenix metropolitan area are caused by more than just variations in NDVI and are likely an interaction of urban forest cover with non-vegetative surfaces related to degree of urbanization near the urban core. Moreover, the presence of an urban to rural temperature gradient and the trend toward increased urban heating over time as evidenced by the comparison of 1976 and 1999 microclimate data, suggests that the UHI effect in desert cities like Phoenix is more likely caused by increased anthropogenic surface covers than relative decreases in vegetation density. Local meteorological records show the existence of a UHI in Phoenix since at least 1948 (Baker et al., 2002). Evidence of the relatively sharp urban to rural temperature gradient that was recorded by Brazel and Johnson (1980) during 1976 was less apparent in 1999 because former agricultural lands in parts of the
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Table 5. Pearson’s correlation coefficients (r-values) showing the degree of associations between normalized differential vegetation index and near-surface air temperature (T), percent relative humidity (RH), and dew point temperature (Td) along four transects in Phoenix, Arizona, USA metropolitan during early morning (0500 HR) and afternoon (1500 HR) of June and December 1999 June
December
0500 HR
1500 HR
0500 HR
1500 HR
r-value
P4F
r-value
P4F
r-value
P4F
r-value
P4F
Transect 1 T RH Td
0.54 0.58 0.57
0.01 0.01 0.01
0.01 0.02 0.02
0.87 0.82 0.78
0.43 0.22 0.11
0.01 0.01 0.20
0.01 0.26 0.01
0.95 0.01 0.98
Transect 2 T RH Td
0.05 0.25 0.58
0.75 0.10 0.01
0.14 0.09 0.01
0.40 0.58 0.97
0.13 0.40 0.48
0.42 0.01 0.01
0.08 0.79 0.76
0.63 0.01 0.01
Transect 3 T RH Td
0.78 0.77 0.67
0.01 0.01 0.01
0.54 0.69 0.62
0.01 0.01 0.01
0.65 0.72 0.72
0.01 0.01 0.01
0.34 0.54 0.48
0.01 0.01 0.01
Transect 4 T RH Td
0.37 0.12 0.32
0.01 0.11 0.01
0.33 0.08 0.13
0.01 0.30 0.09
0.44 0.44 0.05
0.01 0.01 0.53
0.32 0.46 0.15
0.01 0.01 0.04
Fig. 2. The relationship between residual Type IV sums of squares of adjusted near-surface air temperature (T) and adjusted T for all four transects during early morning (0500 HR) of June 1999. NDVI was used as the independent variable.
Phoenix area had been displaced by suburban development. These data suggest that amplification of the Phoenix UHI, a direct result of urban expansion, is spatially variable along an urban to rural gradient. Compared to UHI patterns in more mesic climates (Oke and Maxwell, 1975; McDonnell and Pickett, 1990;
Fig. 3. The relationship between adjusted near-surface air temperature (T) and distance (D) from the Phoenix, AZ, USA urban core for all four transects during early morning (0500 HR) of June 1999; Y ¼ 0:88420:163X ; R2 ¼ 0:403:
Miller, 1997; Bornstein and Lin, 2000), desert cities like Phoenix may actually have an oasis or cooling effect during summer daytime hours caused by patchy urban forest cover densities that are higher than vegetation densities found in surrounding desert habitats.
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Fig. 4. Historical comparison of near-surface air temperature (T), NDVI, and land use along (A) transect 1 and (B) transect 2 in Phoenix, AZ, USA metropolitan. T ¼ F=E; NDVI ¼ - - - -/n.
In summary, findings from this study show that urban plant cover is a modifier of microclimate in Phoenix, a desert city, and supports use of urban greening to mitigate urban heating (McPherson, 2001). Evidence was obtained from this study of a heterogeneous patchwork of microclimates related to socioeconomic land use, as well as an urban to rural decrease in T not associated with an increased density of urban forest cover at the urban fringe as is more typical of urban areas in mesic climates. Though urban forest cover can modify microclimate in Phoenix, these findings were less supportive of the hypothesis that urban forest cover and latent heat fluxes are the principle determinants of microclimate in the Phoenix area. Instead, this study suggests that urban microclimates are more a result of vegetation densities interacting with other factors of the urban fabric such as parking lots (Celestian and Martin, 2004) and buildings. More intensive evaluation of variations in land surface cover type and their effect on urban heating is needed, and strategies of urban greening to mitigate UHI effects in desert cities also need to be counterbalanced against the need to conserve water resources.
Acknowledgments This research was funded, in part, by the National Science Foundation’s Central Arizona Phoenix LongTerm Ecological Research Grant No. DEB-9714833.
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