Sky view factors in forest canopies calculated with IDRISI

Holmer, Björn; Postgård, U; Eriksson, Marie

doi:10.1007/s007040170051

Cited by 57 publications

(29 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To avoid the photographer appearing on the images, the ground-level fish-eye photographs were taken with a timer. The sky view factor at each height was calculated from the fish-eye photographs using a GIS-based method developed by Holmer et al (2001). Table 1 shows the land use classification and the computed sky view factors for all permanent stations, where SVFg refers to the measurements taken at ground level and SVFs refers to those taken at sensor height.…”

Section: Sky View Factormentioning

confidence: 99%

Sky view factor analysis – implications for urban air temperature differences

Svensson¹

2004

Meteorological Applications

229

View full text Add to dashboard Cite

show abstract

Section: Sky View Factormentioning

confidence: 99%

Sky view factor analysis – implications for urban air temperature differences

Svensson¹

2004

Meteorological Applications

229

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Sky View Factor Calculationmentioning

confidence: 99%

“…This error is minimal and we conclude that the rendered output of Google Earth yields adequate results. We evaluated the accuracy of the SVF calculations by computing the SVFs of 527 randomly selected Google Earth fisheye images in the Phoenix metropolitan area using our implementation of Chapman et al (2001), the RayMan Pro model v2.1 by Matzarakis et al (2007Matzarakis et al ( , 2010, the SkyViewFactor-Calculator v1.1 by Holmer et al (2001), and the unweighted, naive approach of counting pixels. Since the SkyViewFactor-Calculator uses the well-established Steyn-method (Steyn, 1980) showing that the RayMan model significantly underestimates SVFs, especially in the midrange, when Lambert's law is not considered for the pixel weighting.…”

Section: Evaluation Of Sky View Factorsmentioning

confidence: 99%

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

Middel

Lukasczyk

Maciejewski

2017

View full text Add to dashboard Cite

The Sky View Factor (SVF) is a dimension-reduced representation of urban form and one of the major variables in radiation models that estimate outdoor thermal comfort. Common ways of retrieving SVFs in urban environments include capturing fisheye photographs or creating a digital 3D city or elevation model of the environment. Such techniques have previously been limited due to a lack of imagery or lack of full scale detailed models of urban areas. We developed a web based tool that automatically generates synthetic hemispherical fisheye views from Google Earth at arbitrary spatial resolution and calculates the corresponding SVFs through equiangular projection. SVF results were validated using Google Maps Street View and compared to results from other SVF calculation tools. We generated 5-meter resolution SVF maps for two neighborhoods in Phoenix, Arizona to illustrate fine-scale variations of intra-urban horizon limitations due to urban form and vegetation. To demonstrate the utility of our synthetic fisheye approach for heat stress applications, we automated a radiation model to generate outdoor thermal comfort maps for Arizona State University's Tempe campus for a hot summer day using synthetic fisheye photos and on-site meteorological data. Model output was tested against mobile transect measurements of the six-directional radiant flux density. Based on the thermal comfort maps, we implemented a pedestrian routing algorithm that is optimized for distance and thermal comfort preferences. Our synthetic fisheye approach can help planners assess urban design and tree planting strategies to maximize thermal comfort outcomes and can support heat hazard mitigation in urban areas.

show abstract

“…There are however several methods for modelling and assessing SVF manually such as scale model (Oke, 1981), angle measurements, (Bottyan & Unger, 2003;Johnson, 1985;Johnson & Watson, 1984), evaluation of fisheye photos (Blankenstein & Kuttler, 2004;Bradley, Thornes, & Chapman, 2001;Holmer, Postgård, & Eriksson, 2001) and evaluation using GPS signals (Chapman & Thornes, 2004). SVF can also be calculated computationally using digital elevation model (DEM) databases describing surface geometric elements (Brown et al, 2001;Lindberg, 2005;Souza, Rodrigues, & Mendes, 2003), and raster-based three-dimensionalisation of two-dimensional data using Digital Elevation Models in GIS (Kokalj, Zakšek, & Oštir, 2011;Ratti, Baker, & Steemers, 2005;Ratti & Richens, 1999).…”

Section: Svf Modelling: Current Methodsmentioning

confidence: 99%

Cool City Design: Integrating Real-Time Urban Canyon Assessment into the Design Process for Chinese and Australian Cities

White

Langenheim

et al. 2016

View full text Add to dashboard Cite

Many cities are undergoing rapid urbanisation and intensification with the unintended consequence of creating dense urban fabric with deep 'urban canyons'. Urban densification can trap longwave radiation impacting on local atmospheric conditions, contributing to the phenomena known as the Urban Heat Island (UHI). As global temperatures are predicted to increase, there is a critical need to better understand urban form and heat retention in cities and integrate analysis tools into the design decision making process to design cooler cities. This paper describes the application and validation of a novel three-dimensional urban canyon modelling approach calculating Sky View Factor (SVF), one important indicator used in the prediction of UHI. Our modified daylighting system based approach within a design modelling environment allows iterative design decision making informed by SVF on an urban design scale. This approach is tested on urban fabric samples from cities in both Australia and China. The new approach extends the applicability in the design process of existing methods by providing 'real-time' SVF feedback for complex three-dimensional urban scenarios. The modelling approach enables city designers to mix intuitive compositional design modelling with dynamic canyon feedback. The approach allows a greater understanding of existing and proposed urban forms and identifying potential canyon problem areas, improved decision making and design advocacy, and can potentially have an impact on cities' temperature.

show abstract

Sky view factors in forest canopies calculated with IDRISI

Cited by 57 publications

References 16 publications

Sky view factor analysis – implications for urban air temperature differences

Sky view factor analysis – implications for urban air temperature differences

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

Cool City Design: Integrating Real-Time Urban Canyon Assessment into the Design Process for Chinese and Australian Cities

Contact Info

Product

Resources

About