Rapid methods to estimate sky‐view factors applied to urban areas

Abstract

A common parameter used to characterize the geometry of urban canyons is the sky-view factor ( s ). Here, two simple alternatives are presented that can be used both objectively and rapidly to estimate s . The first method uses a Nikon CoolPix 950 digital camera fitted with a Nikon FC-E8 fisheye lens. The second method involves a LI-COR LAI-2000 Plant Canopy Analyzer to measure automatically diffuse non-interceptance (DIFN) light using a fisheye optical sensor. Through a series of intercomparisons for urban canyons of known geometry, the digital camera is shown to provide accurate estimates of s . The LAI-2000 also performs well, although it tends to over-estimate s (for the conditions considered here, the mean absolute error is 0.04), and has a more restricted set of sky conditions under which it performs well (ideally, uniform overcast skies). For both methods, data collection and post-processing is rapid, and storage of data is straightforward. Thus, mobile data collection is possible which allows detailed information on the spatial variability of s in urban areas to be determined. An example of such an application for a small US city, Bloomington, IN, is presented.

Sky view factor analysis – implications for urban air temperature differences

“…Several authors discuss the time-consuming process of gathering spatial sky view information and the development of mobile continuous measurement techniques (Postgård 2000;Chapman et al 2001a;Bradley et al 2001;Grimmond et al 2001). The energy exchange surface is basically the ground surface and to make mobile data collection possible a fish-eye camera lens needs to be mounted on some kind of vehicle, i.e., above ground level.…”

“…Different methods have also been used to express urban geometry or calculate the SVF. It has been calculated as height/width ratio (Oke 1981;Johnson & Watson 1984), graphically (Watson & Johnson 1987), from fish-eye photographs (Steyn 1980;Bärring et al 1985;Holmer et al 2000;Chapman et al 2001a;Grimmond et al 2001), with the aid of a three-dimensional (3-D) building database , and recently using GPS receivers and satellite data (Chapman et al 2002). Fish-eye photographs have the advantage of the 3-D perspective as opposed to the 2-D SVF factors derived from height/width ratios or, in asymmetric canyons, the multiple calculations due to segmentations of the hemisphere (Bradley et al 2001).…”

Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

“…SVF calculation is also conducted in the same hemispherical space [44]. Hence, the equirectangular panoramas are transformed into fisheye images for visual analysis and SVF calculation ( Figure 5).…”

Sky View Factors from Synthetic Fisheye Photos for Thermal Comfort Routing—A Case Study in Phoenix, Arizona

“…Several methods are available to calculate the SVF based on fisheye images: The SVF can be calculated using analytical methods that derive the horizon limitation from geometric properties of the urban canyon (Johnson & Watson, 1984); vector-based methods that calculate the SVF from projected building envelopes on the sky using a 3D building database (Chen et al, 2012;Gál et al 2009;Gál & Unger, 2014;Unger, 2009); raster-based methods that use digital elevation models (DEMs) or DSMs to estimate SVFs based on pixel heights or shadow casting (Gál et al, 2009;Lindberg & Grimmond, 2011;Lindberg et al, 2008;Ratti, Baker, & Steemers, 2005;Zakšek, Oštir, & Kokalj, 2011), and photographic methods that use fisheye imagery of the upper hemisphere Chapman & Thornes, 2004;Grimmond, Potter, Zutter, & Souch, 2001;Holmer, Postgård, & Eriksson, 2001). The hemispheric horizon limitation is usually projected on a 2D plane to calculate the amount of visible sky in the scene.…”