Summary

The natural and scenic views of marine and coastal seascapes can contribute to the well-being of local communities in a number of ways. Scenic amenities play an important role in augmenting local economies by attracting visitors who support local businesses. The value of local property partially depends on attributes of its location and scenic views often increase local property values (Sanders and Polasky 2009, Bourassa et al. 2004, Benson et al. 2004). Local communities and their residents often become strongly attached to views and show fervent opposition to new development that has the potential to threaten the integrity of existing views and diminish the benefits drawn from those views (Ladenburg and Dubgaard 2009, Haggett 2011). The InVEST scenic quality model allows users to determine the locations from which new nearshore or offshore features can be seen. It generates viewshed maps that can be used to identify the visual footprint of new offshore development and calculates the value of the impacted visibility. Inputs to the viewshed model include: topography and bathymetry, locations of offshore facilities of interest, and the locations of viewers (e.g. population centers or areas of interest such as parks or trails). The model does not quantify economic impacts of altering the viewshed, but it can be adapted to compute viewshed metrics for use in a more detailed valuation study. A key limitation of the model is that it does not currently account for the ways in which vegetation or land-based infrastructure may constrain land areas that are visually affected by offshore development.

Introduction

Coastal ecosystems are increasingly dominated by human activities. This rise in human activities can compromise the unique scenic qualities associated with coastal and marine areas. The coastline and ‘seascape’ is an important economic asset that attracts visitors for tourism and recreation and contributes to the general quality of life for people living near the coast. Near and offshore development projects often raise considerable concern within the local communities that value the natural seascape for its inherent beauty. Visual impacts are external effects that unless measured and accounted for, do not factor into the calculus of weighing the costs and benefits of new coastal development. Applications using viewshed analysis range from the siting of aquaculture facilities to minimize spatial competition with tourism activities (Perez 2003) to seascape and shoreline visibility assessment of offshore wind projects (Environmental Design and Research 2006). Because scenic beauty is an attribute generally considered to be important to people living near the coast and for those who visit coastal areas to enjoy the ocean and the marine environment, coastal planners can incorporate measures of visual amenities and/or disamenities into broader policy deliberations and planning exercises. Because most applications of viewshed analysis involve examining the negative impacts of new facilities, language within the InVEST scenic quality model assumes the objects viewed have a negative impact on views. However, positive interpretation of viewing these objects can be included with interpretation of model results.

The InVEST scenic quality model provides users with a simple way to provide information about potential tradeoffs between nearshore and offshore development proposals and the visual impacts of those projects. The viewshed maps produced by the model can be used to identify coastal areas that are most likely to be directly affected by additions to the seascape. They can serve as valuable input into broader analyses that consider a range of services provided by the marine environment.

This model can be used to compute the costs associated with offshore visual impacts, these costs are likely to decrease as the location of facilities moves further offshore, while the costs of installing and operating offshore facilities generally increase with distance from the shoreline. The few valuation studies that explore the economic magnitude of visual disamenities resulting from offshore development projects show a complex picture. One recent study found that individuals living along the coast have external costs ranging from $27 to $80 resulting from the visual disamenity of an offshore wind project (Krueger et al. 2010). In contrast, Firestone et al. (2009) found that public acceptance for offshore renewable energy projects is growing and may be less contentious than previously anticipated.

The Model

The scenic quality model provides information about the visibility of offshore objects from the surrounding landscape or seascape. Offshore and nearshore development projects, such as renewable wave energy facilities or aquaculture facilities, have the potential to impact the visual amenities that are an important feature of many coastal areas. The results of viewshed analysis will be useful for decision-makers who would like to identify areas where visual impacts may be an important factor to incorporate into planning.

The model inputs are divided into two groups: General has all the entries necessary to run the viewshed computation such as the location of a DEM and a point vector that identifies the locations of sites that contribute to visual impacts. Valuation allows the user to select the functional form of the valuation function and its parameters. The viewshed analysis is then computed over a user-defined area of interest (AOI).

The model will create three outputs that can be used to assess the visible impact of any type of facility added to the marine environment:

• vshed.tif is a raster containing the sum of how many viewpoints are visible from each pixel. If a WEIGHT column is provided in the structures/viewpoints vector, the visibility sum is weighted. If the valuation and weights are all set to 1, this raster reduces to merely a count of the number of sites that are visible from a pixel.

• vshed_value.tif is the sum of all individual valuation rasters calculated for each site. If a WEIGHT column is provided in the sites vector, the values will be weighted accordingly.

• vshed_qual.tif is a raster representing the visual quality of a given pixel. The cells of vshed_Value.tif are classified according to the following percentile breaks:

  1. Unaffected

  2. Low visual impact / High visual quality (< 25th percentile)

  3. Moderate visual impact / Medium visual quality (25-50th percentile)

  4. High visual impact / Low visual quality (50-75th percentile)

  5. Very high visual impact / Poor visual quality (> 75th percentile)

Additional files are created for each feature X at each step of the computation:

• visibility_X.tif indicates which pixels are visible from feature X

• auxiliary_X.tif is an intermediate raster written as part of the viewshed algorithm. Pixel values indicate the minimum height that must be reached for a pixel to be visible.

• value_X.tif is the value of the viewshed, weighted (if a WEIGHT column is provided), and in terms of the distance (in meters) between pixels and feature X.