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Incorporating vegetation into visual exposure modelling in urban environments

P. Bartie,F. Reitsma,S. Kingham,S. Mills

Published 2011 in International Journal of Geographical Information Science

ABSTRACT

Visual exposure modelling establishes the extent to which a nominated feature may be seen from a specified location. The advent of high-resolution light detection and ranging (LiDAR)-sourced elevation models has enabled visual exposure modelling to be applied in urban regions, for example, to calculate the field of view occupied by a landmark building when observed from a nearby street. Currently, visual exposure models access a single surface elevation model to establish the lines of sight (LoSs) between the observer and the landmark feature. This is a cause for concern in vegetated areas where trees are represented as solid protrusions in the surface model totally blocking the LoSs. Additionally, the observer's elevation, as read from the surface model, would be incorrectly set to the tree top height in those regions. The research presented here overcomes these issues by introducing a new visual exposure model, which accesses a bare earth terrain model, to establish the observer's true elevation even when passing through vegetated regions, a surface model for the city profile and an additional vegetation map. Where there is a difference between terrain and surface elevations, the vegetation map is consulted. In vegetated areas the LoS is permitted to continue its journey, either passing under the canopy with clear views or partially through it depending on foliage density, otherwise the LoS is terminated. This approach enables landmark visual exposure to be modelled more realistically, with consideration given to urban trees. The model's improvements are demonstrated through a number of real-world trials and compared to current visual exposure methods.

PUBLICATION RECORD

  • Publication year

    2011

  • Venue

    International Journal of Geographical Information Science

  • Publication date

    2011-05-01

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1080/13658816.2010.512273
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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