Improved landscape sampling method for landscape character assessment

Abstract

Landscape character assessment (LCA) is an effective tool for understanding people’s perceptions and preferences of landscape characteristics. Other than the assessment indicators and subjects, the reliability of photos as assessment objects is equally important for the LCA result. However, the commonly used onsite photos are mainly obtained at randomly selected locations by the researchers. We can neither know whether those photos represent the researchers’ own preferences, nor, to our best knowledge, can their reliability be tested scientifically. This method is also difficult to apply in large-scale geographical areas. To address these issues, we (1) propose an improved method including the protocols of photography and the sampling of photography locations, in which the fractal principle and stratified random sampling method were combined to minimize the effects of the researchers’ preferences and other factors; (2) apply the method to the Guanzhong region as an example, and obtain sampling photos and their geographical coordinates, which can be used as a data package for LCA; (3) use Fractalyse to test the sampled result and receive good validity. In conclusion, this study extends the methodological chain of the LCA and supports the application of LCA in large-scale regions.

Key words： LCA; Photography location sampling; Fractals; Stratified random sampling; Guanzhong region in Shaanxi