Modeling aesthetics to support an ecosystem services approach for natural resource management decision making

This paper reviews literature on aesthetics and describes the development of vista and landscape aesthetics models. Spatially explicit variables were chosen to represent physical characteristics of natural landscapes that are important to aesthetic preferences. A vista aesthetics model evaluates the aesthetics of natural landscapes viewed from distances of more than 1000 m, and a landscape aesthetics model evaluates the aesthetic value of wetlands and forests within 1000 m from the viewer. Each of the model variables is quantified using spatially explicit metrics on a pixel-specific basis within EcoAIM™, a geographic information system (GIS)-based ecosystem services (ES) decision analysis support tool. Pixel values are "binned" into ranked categories, and weights are assigned to select variables to represent stakeholder preferences. The final aesthetic score is the weighted sum of all variables and is assigned ranked values from 1 to 10. Ranked aesthetic values are displayed on maps by patch type and integrated within EcoAIM. The response of the aesthetic scoring in the models was tested by comparing current conditions in a discrete area of the facility with a Development scenario in the same area. The Development scenario consisted of two 6-story buildings and a trail replacing natural areas. The results of the vista aesthetic model indicate that the viewshed area variable had the greatest effect on the aesthetics overall score. Results from the landscape aesthetics model indicate a 10% increase in overall aesthetics value, attributed to the increase in landscape diversity. The models are sensitive to the weights assigned to certain variables by the user, and these weights should be set to reflect regional landscape characteristics as well as stakeholder preferences. This demonstration project shows that natural landscape aesthetics can be evaluated as part of a nonmonetary assessment of ES, and a scenario-building exercise provides end users with a tradeoff analysis in support of natural resource management decisions. Integr Environ Assess Manag 2017;13:926-938. © 2017 SETAC.

[1]  J. Westphal,et al.  The Psychological Utility of Visual Penetration in near-view Forest Scenic-Beauty Models , 1989 .

[2]  T. Daniel,et al.  REPRESENTATIONAL VALIDITY OF LANDSCAPE VISUALIZATIONS: THE EFFECTS OF GRAPHICAL REALISM ON PERCEIVED SCENIC BEAUTY OF FOREST VISTAS , 2001 .

[3]  Holmes Rolston,et al.  Does aesthetic appreciation of landscapes need to be science based , 1995 .

[4]  Jennifer A. Miller,et al.  Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data , 2003 .

[5]  Thomas R. Herzog,et al.  The Role of Mystery in Perceived Danger and Environmental Preference , 1998 .

[6]  Alena Bleicher,et al.  Spatially explicit computation of sustainability indicator values for the automated assessment of land-use options , 2013 .

[7]  A. Salmanmahiny,et al.  Measuring the relationships between landscape aesthetics suitability and spatial patterns of urbanized lands: an informed modelling framework for developing urban growth scenarios , 2017 .

[8]  Erich Tasser,et al.  Predicting scenic beauty of mountain regions , 2013 .

[9]  R. O'Neill,et al.  A factor analysis of landscape pattern and structure metrics , 1995, Landscape Ecology.

[10]  A. Kearney,et al.  The Effects of Viewer Attributes on Preference for Forest Scenes: Contributions of Attitudes, Knowledge, Demographic Factors, and Stakeholder Group Membership , 2011 .

[11]  Adrienne Grêt-Regamey,et al.  Predicting the Scenic Beauty Value of Mapped Landscape Changes in a Mountainous Region through the Use of GIS , 2007 .

[12]  R. Ribe.  In-stand scenic beauty of variable retention harvests and mature forests in the U.S. Pacific Northwest: the effects of basal area, density, retention pattern and down wood. , 2009, Journal of environmental management.

[13]  J. Nasar,et al.  Landscape mirror: the attractiveness of reflecting water , 2004 .

[14]  Qihao Weng.  Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. , 2002, Journal of environmental management.

[15]  Paul H. Gobster,et al.  The dimensions of aesthetic preference: a quantitative analysis. , 1989 .

[16]  T. R. Herzog,et al.  Mystery and Preference in Within-Forest Settings , 2007 .

[17]  Franz Makeschin,et al.  Assessment of landscape aesthetics—Validation of a landscape metrics-based assessment by visual estimation of the scenic beauty , 2013 .

[18]  T. Daniel.  Whither scenic beauty? Visual landscape quality assessment in the 21st century , 2001 .

[19]  R. Ulrich.  Visual landscapes and psychological well‐being , 1979 .

[20]  Peter F. Fisher,et al.  Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping* , 1996 .

[21]  M. E. Patterson,et al.  Identifying and predicting visual preference of southern Appalachian forest recreation vistas , 1994 .

[22]  Paul H. Gobster,et al.  An Ecological Aesthetic for Forest Landscape Management , 1999, Landscape Journal.

[23]  Joan Iverson Nassauer,et al.  Messy Ecosystems, Orderly Frames , 1995, Landscape Journal.

[24]  P. Bolund,et al.  Ecosystem services in urban areas , 1999 .

[25]  Ian D. Bishop,et al.  VISUAL THRESHOLDS FOR DETECTION, RECOGNITION AND VISUAL IMPACT IN LANDSCAPE SETTINGS , 2000 .

[26]  Gary Fry,et al.  Capturing Landscape Visual Character Using Indicators: Touching Base with Landscape Aesthetic Theory , 2008 .

[27]  A. Stamps.  Effects of Permeability on Perceived Enclosure and Spaciousness , 2010 .

[28]  R. Ribe.  The aesthetics of forestry: What has empirical preference research taught us? , 1989 .

[29]  Athanassios I. Sfougaris,et al.  electing landscape metrics as indicators of spatial heterogeneity — A omparison among Greek landscapes , 2013 .

[30]  Ian D. Bishop,et al.  Prediction of scenic beauty using mapped data and geographic information systems , 1994 .

[31]  Michael A. Wulder,et al.  An accuracy assessment framework for large‐area land cover classification products derived from medium‐resolution satellite data , 2006 .

[32]  Stephan Pauleit,et al.  Benefits and uses of urban forests and trees , 2005 .