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Chaos in Motion: Measuring Visual Pollution with Tangential View Landscape Metrics

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  • Szymon Chmielewski

    (Department of Grassland and Landscape Studies, University of Life Sciences in Lublin, Akademicka 15 St., 20-950 Lublin, Poland)

Abstract

Visual pollution (VP) in the form of outdoor advertisements (OA) is a threat to landscape physiognomy. Despite their proven usefulness in landscape aesthetic studies, landscape metrics have not yet been applied to address the phenomenon of VP. To fill this knowledge gap, a methodological framework for the measurement of VP using tangential view landscape metrics is proposed, which is accompanied by statistically significant proofs. Raster products derived from aerial laser scanning data were used to characterize two study areas with different topographic conditions in the city of Lublin, East Poland. The visibility of the cityscape in motion was simulated through viewshed measurements taken at equal intervals in the forwards and backwards directions along pedestrian walkways. The scrutinized tangential view landscape metrics (visible area, maximum visible distance, skyline, Shannon depth, view depth line) was the object of a two-fold interpretation wherein the spatial occurrence of VP as well as its impacts on the visual landscape character (VLC) were examined. The visible area metrics were found to be highly sensitive VP indicators. The maximum visible distance metrics provided evidence for the destructive effect of OA on view corridors. The Shannon depth and depth line metrics were not found to be statistically significant indicators of VP. Results from directional viewshed modelling indicate that distortion in the analyzed cityscape physiognomy depends on the view direction. The findings allow for particular recommendations with practical implementations in land use planning, which are discussed along with limitations to our proposed methods.

Suggested Citation

  • Szymon Chmielewski, 2020. "Chaos in Motion: Measuring Visual Pollution with Tangential View Landscape Metrics," Land, MDPI, vol. 9(12), pages 1-21, December.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:12:p:515-:d:461047
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    3. Mohammad AlElaiwi & Mugahed A. Al-antari & Hafiz Farooq Ahmad & Areeba Azhar & Badar Almarri & Jamil Hussain, 2022. "VPP: Visual Pollution Prediction Framework Based on a Deep Active Learning Approach Using Public Road Images," Mathematics, MDPI, vol. 11(1), pages 1-26, December.
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    5. S. Mahdi Hashemi Kashani & Mahdieh Pazhouhanfar, 2024. "Role of physical attributes of preferred building facades on perceived visual complexity: a discrete choice experiment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 13515-13534, May.

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