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Predicting Scenic Quality for Urban Forests Using Vegetation Measurements

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Abstract:

Scenic quality models were formulated for urban residential areas. The focus of the study was on determining the predictive ability of measurable parameters of tree and other vegetation. Two types of data bases were used: (1) site inventory measurements and (2) photo measurements of urban forest related variables. Regression results indicate that visual preference for urban forest scenes increases with increasing average dbh, basal area per stem, and crown enclosure. There are tentative indications that the relationship between preference and amount of vegetation may be nonmonotonic and also that people prefer scenes with fewer, larger stems to those with many smaller stems. It is hypothesized that these scenic quality models have potential for integration with urban tree inventory data bases for determining planting prioritization. Forest Sci. 30:71-82.

Keywords: Landscape preference; urban forestry; visual quality prediction

Document Type: Journal Article

Affiliations: Associate Professor of Forestry, Virginia Polytechnic Institute and State University

Publication date: March 1, 1984

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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