49The Geographical Bulletin 54: 49-57©2013 by Gamma Theta Upsilon

Sky-View Factor Estimation: A Case Study of Athens, GeorgiaNeil DebbageDepartment of GeographyUniversity of GeorgiaAthens, GA 30602E-mail: debbage@uga.edu

ABSTRACT

A better understanding of the urban heat island (UHI) effect can be facilitated by ac-curately measuring urban geometry . One commonly utilized approach is to calculate the sky-view factor (SVF), which represents the proportion of the sky visible from a given location . The overall purpose of this study is to assess the spatial distribution of SVF values for various land use classes in Athens, GA . Since fish-eye lens imagery can effectively capture the complex nature of the urban canyon, I used a simplistic photographic methodology . I took fish-eye photographs within four land use classes (downtown, commercial, residential and rural/open space) and used graphics soft-ware to calculate the SVF values from the imagery . It was discovered that the lowest SVF values were located close to the urban core in the residential and downtown areas while the highest values were observed in the more peripheral commercial and rural/open space areas . These results suggest that an UHI could potentially exist in downtown Athens .

Key Words: urban heat island, sky-view factor, fish-eye photography, Athens (GA)

INTRODUCTION

Urban surface geometry has a significant but complex influence on the urban atmo-sphere . The taller buildings characteristic of urbanized areas often act to restrict out going long-wave radiation, which can thwart nocturnal cooling and contribute to an urban heat island (UHI) effect (Oke 1981) . The UHI phenomenon is important because it partially explains why cities can sometimes experience higher temperatures when com-pared with the surrounding countryside (Coutts et al . 2007) . High levels of urban heating have been linked to poor urban air quality (Sarrat et al . 2006) as well as altered precipitation (Shepherd 2006) and snowmelt patterns (Todhunter et al . 1992; Semádeni-

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Davies & Bengtsson 1998) . Furthermore, as the areal extent of cities expands (Lang & Knox 2009), the impact of urban landscapes and the subsequent UHI effect may no longer be confined to just the urban core, but spread to more suburban regions (Stone et al . 2010; Stone and Rodgers 2001) .

Consequently, a better understanding of the UHI effect can be facilitated by accurately measuring urban geometry . One commonly utilized approach is to calculate the sky-view factor (SVF), which can be simply defined as “a measure of the degree to which the sky is obscured by the surroundings for a given point” (Grimmond et al . 2001, 903) . SVF can be defined more technically as the ratio of radiation received (or emitted) by a planar surface compared to the radiation emitted (or received) by the entire hemispheric environ-ment (Watson & Johnson 1987) . SVF is a dimensionless parameter with values ranging from 0 to 1 (Chapman & Thornes 2004) . A value of 0 means the sky is completely ob-structed, such as in a tunnel, while a value of 1 would occur in an entirely open and unobstructed space . Since the SVF provides a parameterization of the urban geometry’s effect on the radiation balance, it is often considered a relevant variable when analyz-ing the formation of nocturnal heat islands (Oke 1981) . Indeed, both Gal et al . (2009) and Svensson (2004) have found that a fairly strong negative relationship exists between SVF and UHI intensity, especially during calm, clear nights . These two studies used regression models to analyze the relationship between SVF values and the urban-rural tem-perature gradient, which yielded R2 values of 0 .63 (Gal et al . 2009) and 0 .58 (Svensson 2004) respectively .

The overall purpose of this study is to assess the spatial distribution of SVF val-ues throughout various land use classes in Athens, GA . The chosen methodology is based on an assessment of the existing SVF literature as it relates to UHI effect . After evaluating the potential implications of the collected SVF values with regard to various land use patterns, I discuss the usefulness

of SVF for evaluating the magnitude of the UHI effect in Athens .

Over the past few decades, various techniques for calculating SVF have been explored . These efforts began with the de-velopment of analytical methods, which used geometric equations based on the height and width of the urban canyon to calculate SVF . Oke (1981) and Johnson and Watson (1984) conducted some of the most influential re-search, although their methodologies were fairly simplistic and only incorporated build-ing structure and form . These early equations frequently overlooked vegetation and other obstacles, which in some cases may produce inflated SVF values, because all obstructions are not considered .

In more current work, researchers have adopted optical and photographic methods, since they theoretically provide a more ac-curate measurement of SVF (Chapman & Thornes 2004) . The photographic methodol-ogy uses a fish-eye lens to capture an image of the urban canyon (Grimmond et al . 2001) . Fish-eye lenses purposely distort the edge of an image, which causes the slightly curved appearance, in order to achieve a wide-angle panoramic perspective . For SVF studies, the wide-angle enables researchers to view the entire hemisphere of an urban canyon at a given location . From the imagery, various ap-proaches can then be used to derive a SVF value . Some techniques use image histograms to establish a threshold pixel value, which is then applied to separate the sky portion of the image, usually characterized by larger pixel values (Chapman and Thornes 2004), from the obstructions . Once this binary image is created, various equation algorithms that divide the image into concentric annuli, or a series of rings, have been used to calculate the SVF value . Grimmond et al . (2001) used a Fortran program to automate the equations of Johnson and Watson (1984), while others have slightly modified the equations of the traditional Steyn (1980) paper-and-pencil method to automate the process and make it applicable to digital imagery (Chapman & Thornes 2004; Gal et al . 2007) .

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Sky-View Factor Estimation: A Case Study of Athens, Georgia

An emerging photographic method is the use of thermal fish-eye photography, which has been shown to accurately estimate SVF values largely because the thermal images clearly distinguish between the colder sky and warmer obstructions such as buildings (Chapman et al . 2007) . Another common methodology uses an automobile as a mobile platform . By attaching a fish-eye lens camera to a sunroof, the photography necessary to estimate SVF values for a large urban transect can be collected very quickly (Grimmond et al . 2001) . Chapman and Thornes (2004) refined the mobile platform approach by fully automating the image processing, enabling them to calculate SVF values in real-time .

Other alternative approaches include Chapman and Thornes (2004) who used GPS data to estimate SVF values based on satellite signal strengths . Since the Chapman and Thornes (2004) methodology does not require photography, it can accurately predict SVF values under a wide range of conditions such as nighttime or rain . Despite the ver-satility of this method, it requires a complex artificial neural network (ANN) to predict the SVF values from the GPS data . The ANN was used because it can handle nonlinear and noisy datasets better than simple regression techniques .

An additional method, based heavily on geographic information systems (GIS), is the use of 3D building databases to calculate a continuous surface of SVF values for an en-tire study area . The 3D building datasets can be created from building footprint data and aerial photography (Gal et al . 2007; Gal et al . 2009) as well as high resolution LIDAR data (Matuschek & Matzarakis 2010; Kidd & Chapman 2012) . Automated equation algorithms (Gal et al . 2007; Souza et al . 2003; Matzarakis and Matuschek 2011) and shadow-casting algorithms (Ratti & Richens 1999; Brown et al . 2001) have both been used to calculate the SVF values from these 3D building datasets . Although this method does have useful applications for urban plan-ning (Ratti & Richens 1999), modeling com-plex obstacles such as vegetation and non-flat

roofs is very challenging and 3D building datasets are often not readily available .

METHODS

Overall, since fish-eye lens photography can effectively capture the complex nature of the urban canyon, a simplistic photographic method similar to that of Grimmond et al . (2001) and Shaker and Drezner (2010) was adopted in this study . The most common equipment used for collecting SVF photo-graphs is the Nikon Coolpix camera with the Nikon fish-eye lens attachment (Grimmond et al . 2001; Chapman & Thornes 2004; Gal et al . 2007) . However, since the budget of this study was fairly modest, I borrowed a Cannon Powershot camera and purchased a compatible Opteka fish-eye lens . I took photographs of the sky at sample locations within four different settings that represented each of the major land use classes being tested: downtown, commercial, residential, and rural/open space (Figure 1) . Since no elevation standard for capturing SVF imag-ery has been established (Svensson 2004), the pictures of Athens were taken vertically at shoulder height in an attempt to ensure consistency . The photographs were collected in early autumn under clear blue skies . GPS measurements were simultaneously collected at each point where a photograph was cap-tured . The GPS accuracy was maintained between 10 and 15 feet .

In order to calculate the SVF values from the individual photographs, the graphics software Adobe Photoshop was used (Shaker & Drezner 2010) . I utilized the magic wand tool in Photoshop to separate the visible sky region of the photographs from the obstructions (Figure 2) . The magic wand tool automatically selects an area of an im-age based on its pixel values . For example, by clicking on a blue pixel in the sky por-tion of a photograph the remainder of the sky is automatically selected, since it shares a similar pixel value . During the selection process, considerable care was taken to assure that the automatic delineation was accurate .

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Figure 1 . Location of the SVF Sample Sites within Athens, GA .

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Occasionally errors, such as parts of buildings that shared the same or a similar color to the sky, had to be manually adjusted . The selec-tion can be non-contiguous, which allowed the photographs involving many tiny por-tions of sky, such as those taken underneath trees, to be accurately analyzed . After the sky was accurately selected, I calculated the total number of pixels visible within the fish-eye lens . In essence, this represented the entire visible area of the photograph (β) . Then I measured the total number of pixels that were in the sky selection (α) and exported all the data to Microsoft Excel, where the SVF value was calculated as α / β . While the basic method for calculating SVF values outlined above shares much in common with the work of Shaker and Drezner (2010), this study focuses more on the spatial implica-tions of SVF values and how they differ with various land use patterns .

To better analyze the spatial distribution of the SVF values, I imported the data into a GIS . I extracted the coordinates of the pic-ture locations from the GPS and geo-coded them in ArcMap . I added the SVF values calculated in Microsoft Excel and land use classification information to the coordinate point layer’s attribute table . The land use

data was gathered from the National Land Cover Database (NLCD) and aggregated to minimize the total number of land use classes . Graduated symbols were used to provide a meaningful visualization of the SVF values and their spatial distribution . Finally, I used an inverse distance weighted (IDW) interpolation to estimate the SVF values for the entire downtown and resi-dential areas .

RESULTS

Overall, the lowest SVF values were found close to the urban core in the residential and downtown areas while the highest values were located in the more peripheral commercial and rural/open space regions (Table 1) . In downtown Athens, the presence of urban obstacles most likely contributed to the low mean SVF value (0 .64) . The urban core is also a very heterogeneous environment where the building heights and orientations vary dramatically . For example, the nine-story Bank of America building is located directly across from low one- and two-story devel-opments . This complex variety of building structures resulted in the highest variability of SVF values (std . dev . = 0 .22) occurring

Figure 2 . Fish-Eye Photographs Before and After Photoshop Analysis .(SVF Values For Photos: Top Left - 0 .44, Top Right - 0 .51, Bottom Left - 0 .78, Bottom Right - 0 .63)

Sky-View Factor Estimation: A Case Study of Athens, Georgia

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Table 1 . SVF Values Summarized by Land Use Category .

Land Use Category Mean Median Max. Min. Std. Dev. Number of Samples

Residential 0.49 0.52 0.68 0.29 0.14 11

Downtown 0.64 0.70 0.95 0.26 0.22 20

Rural/Open Space 0.87 0.92 1.00 0.55 0.17 6

Commercial 0.92 0.91 0.97 0.89 0.03 4

Figure 3 . Map of the Interpolated SVF for downtown Athens, GA .

in downtown . Figure 3 illustrates through interpolation that the highest SVF values in downtown Athens were located in the pe-ripheral northern and western portions of the CBD, where building height was lower . The smallest SVF values were in the core of the CBD as expected . One caveat is that if additional sample points had been collected,

the IDW interpolation could more accurately predict the micro-variation of SVF values throughout downtown Athens .

Surprisingly, in Athens the average SVF was lowest in the residential area . The dense deciduous leaf cover found in the neighborhood was most likely responsible for the unusually low mean SVF value of

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0 .49 . Therefore, a study conducted using the same methodology but during winter when the trees have shed their leaves would most likely result in a drastically higher SVF value . Unlike downtown Athens, the interpolation of SVF values in the residen-tial area did not reveal a definitive spatial pattern (Figure 4) while the standard de-viation of the residential area (0 .14) was actually much lower than the downtown’s (0 .22) .

The rural/open space areas, as expected, had high SVF values (mean = 0 .87) because of the lack of obstructions and presence of vast open fields . The commercial areas had similarly high SVF values (mean = 0 .92) due in part to the large open parking lots com-monly present within big box commercial developments . The commercial area also had the lowest standard deviation (0 .03), although this may be partially explained by the smaller sample size .

DISCUSSION

The general pattern of these results paral-lels the outcome of Grimmond et al . (2001) who found lower SVF values around the urban core of Bloomington, Indiana . Grim-mond et al. (2001) similarly discovered that the SVF values of downtown Bloomington varied more than the other land use classes largely due to building height heterogene-ity . However, the average SVF value of downtown Athens (0 .64) was much smaller than that of Bloomington (0 .83) and more similar to the average found by Oke (1981) for North American cities (0 .66) . These discrepancies could perhaps be explained by the varying size and form of the individual urban centers . For example, the sample loca-tions in downtown Bloomington included an open park-like area that surrounded the local courthouse and slightly wider streets,

Figure 4 . Map of the Interpolated SVF for the Residential Neighborhood .

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both of which might have contributed to Bloomington’s higher SVF average .

Generally lower SVF values are associated with higher levels of urbanization, which in turn enhances UHI intensity (Svensson 2004) . Overall, the relatively low SVF val-ues of downtown Athens suggest that urban obstacles could indeed act to enhance the UHI effect . The results demonstrate that SVF can be calculated with a fairly simplistic methodology but can also produce mislead-ing measurements if interpreted incorrectly . For example, even though the commercial land use had the highest mean SVF value, this does not guarantee it is immune to po-tential urban heating particularly given the high percent of “black-top” land cover in this study area . Also, factors such as tree cover, which can actually reduce heat stress in an urban environment, can also substantially lower the SVF for a given location . Addi-tionally, Steeneveld et al . (2011) observed that in part due to the low SVF in urban centers, a shadow effect can occur, which actually results in urban cooling especially during periods of low sun angle in the early morning . Due to these discrepancies, great care must be taken to not associate low SVF values directly with high levels of urban heat-ing, even though some studies have found a correlation between SVF and urban air temperatures (Svensson 2004) . Instead, a comprehensive approach which analyzes a range of additional factors such as albedo, heat capacity, surface emissivity, and surface roughness in tandem with SVF values would be more appropriate for accurately evaluating the magnitude of the UHI effect .

ACKNOWLEDGEMENTS

This paper originated as a class project for Geography 4160–Applied Climatology in the Urban Environment–at the University of Georgia, which was completed with the assistance of Fred Galyean, Binita KC, and Kenny Moore . Thank you to Dr . J . Marshall Shepherd in the Department of Geography at the University of Georgia for providing

insightful feedback and equipment funding . Finally, I would like to thank the anonymous reviewers who provided helpful comments .

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Sky-View Factor Estimation: A Case Study of Athens, Georgia