Hello, I am running the Scenic Quality model for a Mediterranean region, I have a couple of questions:
Why this model is not suitable for inland as well but only for the coastal scenery? I mapped the features Impacting Scenic Quality in the Inland, and the model runs anyway, where is the catch?
What are the criteria to decide on a linear or non-linear valuation?
It can be run in any location. Apologies if any of the guidance suggests it is only appropriate for coastal settings.
The “valuation” feature can be used to integrate a visual impact function into the viewshed. Sometimes these are denominated in monetary terms, but you’ll also see impact indices. See figure 4 and the discussion around it from this paper for more info.
Thank you Dr. Griffith for your answer and for the example. We are actually looking for a non-economic valuation, and the impact index might fit our purpose.
Some more questions though:
is the cumulative viewshed mentioned in Figure 4 the vshed_value result in the InVEST model?
Is still not clear to me how to decide to use a linear or non-linear function, I did not find an explanation on the model documentation. Could you clarify this, or direct me to further references on the topic?
Thank you again!
We do not provide documentation on this because it is domain, and potentially even site, specific. Visual impact indices may vary for different types of features. The example from that paper is for wind farms, but you might expect something different for aquaculture, landfills, or other large infrastructure. My professional judgement is that your results will likely be more robust if you do not use a visual impact function and stick to visibility. You’ll note that the remainder of that paper uses the framing of visibility without assigning values to it. If you do choose to use an impact function, it should be derived from other studies that investigate the features of interest. Importantly, it needs to be formulated as a function of distance to be used in this model.
The previously linked paper also uses the “weight” feature (an attribute of the point vector layer input) of the model to weight the viewsheds by the number of viewer-days at each point. This can be a useful hybrid approach where you can say something more about the density of viewers across the landscape, yet stopping short of assigning a value. I’d read that paper carefully, to get a sense about how the model can be used from the perspective of viewers, or the viewed, with different interpretations of model outputs.