Nature of vegetation and building morphology characteristics across a city: Influence on shadow patterns and mean radiant temperatures in London
Vegetation and building morphology characteristics are investigated at 19 sites on a north-south LiDAR transect across the megacity of London. Local maxima of mean building height and building plan area density at the city centre are evident. Surprisingly, the mean vegetation height (zv3) is also found to be highest in the city centre. From the LiDAR data various morphological parameters are derived as well as shadow patterns. Continuous images of the effects of buildings and of buildings plus vegetationon sky view factor (Ψ) are derived. A general reduction of Ψ is found, indicating the importance of including vegetation when deriving Ψ in urban areas. The contribution of vegetation to the shadowing at ground level is higher during summer than in autumn. Using these 3D data the influence on urban climate and mean radiant temperature (T mrt ) is calculated with SOLWEIG. The results from these simulations highlight that vegetation can be most effective at reducing heat stress within dense urban environments in summer. The daytime average T mrt is found to be lowest in the densest urban environments due to shadowing; foremost from buildings but also from trees. It is clearly shown that this method could be used to quantify the influence of vegetation on T mrt within the urban environment. The results presented in this paper highlight a number of possible climate sensitive planning practices for urban areas at the local scale (i.e. 102- 5 × 103 m).
KeywordsLiDAR Shadow patterns Mean radiant temperature Sky view factor Urban vegetation Urban morphology SOLWEIG Spatial variability Urban trees
This work is supported by FORMAS—the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, European Community’s Seventh Framework Programme FP/2007–2011 BRIDGE (211345), NERC Airborne Remote Sensing Facility (GB08/19), the Centre for Ecology & Hydrology (CEH) and King’s College London (in particular all those who contribute to the urban micrometeorology group).
- Brown MJ, Grimmond CSB, Ratti CF (2001) Comparison of methodologies for computing sky view factor in urban environments. In International Society of Environmental Hydraulics Conference, Tempe, AZ, December 2001, LA-UR-01-4107Google Scholar
- Heisler GM (1990) Mean wind speed below building height in residential neighborhoods with different tree densities. ASHRAE Transactions 96:1389–1396Google Scholar
- Höppe P (1992) A new procedure to determine the mean radiant temperature outdoors. Wetter und Leben 44:147–151Google Scholar
- IPCC (2007) AR4 Synthesis report, full report, intergovernmental panel on climate change, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf
- Lindberg F (2005) Towards the use of local governmental 3-D data within urban climatology studies. Mapping and Image Science 2005(2):4–9Google Scholar
- Lindberg F, Grimmond CSB (2011) The influence of vegetation and building morphology on shadow patterns and mean radiant temperatures in urban areas: model development and evaluation. Theoretical and Applied Climatology doi: 10.1007/s00704-010-0382-8
- McGaughey RJ (2009) FUSION/LDV: Software for LiDAR data analysis and visualization. United States Department of Agriculture, SeattleGoogle Scholar
- Oke TR (1987) Boundary layer climates. Routledge, Cambridge, p 435Google Scholar
- Ordnance Survey (2010) © Crown database right 2010. An Ordnance Survey/EDINA supplied service. Assessed 2009-10-13. http://www.ordnancesurvey.co.uk/oswebsite/
- Ratti CF, Richens P (1999) Urban texture analysis with image processing techniques. Proc CAADFutures99. Atlanta, GAGoogle Scholar
- Shashua-Bar L, Pearlmutter D, Erell E (2010) The influence of trees and grass on outdoor thermal comfort in a hot-arid environment. Int J Climatol doi: 10.1002/joc.2177
- WHO/WMO/UNEP (1996) Climate and health: the potential impacts of climate change. Swizterland, GenevaGoogle Scholar