|dc.description.abstract||Many studies of natural landscape preference have demonstrated that qualities such
as 'complexity' and 'naturalness' are associated with preference, but have struggled to
define the key characteristics of these qualities. Recently, the development of software
programs and digital techniques has offered researchers new ways of quantifying the
landscape qualities associated with preference. Among them fractal geometry offers
the most promising approach.
Fractals have been defined as mathematical models of organic objects and patterns
as opposed to the straight lines and perfect circles of Euclidean geometry found in
man-made environments. Fractal patterns are mainly characterized by their dimension,
which could be described as a statistical quantification of complexity. By applying this
mathematical concept to digital images, several studies claim to have found a correlation
between the fractal dimensions of a set of images and the images' preference ratings.
Such studies have particularly focussed on demonstrating support for the hypothesis
that patterns with a fractal dimension of around 1.3 induce better responses than
However, much of this research so far has been carried out on abstract or computer-generated
images. Furthermore, the most commonly used method of fractal analysis,
the box-counting method, has many limitations in its application to digital images
which are rarely addressed. The aim of this thesis is to explore empirically the suggestion
that landscape preference could be influenced by the fractal characteristics of landscape photographs.
The first part of this study was dedicated to establishing the robustness and validity
of the box-counting method, and apply it to landscape images. One of the main
limitations of the box-counting method is its need for image pre-processing as it can
only be applied to binary (black and white) images. Therefore, to develop a more reliable
method for fractal analysis of landscapes, it was necessary to compare different
methods of image segmentation, i.e the reduction of greyscale photographs into binary
images. Each method extracted a different structure from the original photograph:
the silhouette outline, the extracted edges, and three different thresholds of greyscale.
The results revealed that each structure characterized a different aspect of the landscape:
the fractal dimension of the silhouette outline could quantify the height of the
vegetation, while the fractal dimension of the extracted edges characterized complexity.
The second part of the study focused on collecting preference ratings for the landscape
images previously analysed, using an online survey disseminated in France and
the UK. It was found that different groups of participants reacted differently to the fractal
dimensions, and that some of those groups were significantly influenced by those
characteristics while others were not. Unexpectedly, the variable most correlated with
preference was the fractal dimension of the image's extracted edges, although this variable's
predictive power was relatively low.
The study concludes by summarising the issues involved in estimating the fractal
dimensions of landscapes in relation to human response. The research offers a set of
reliable and tested methods for extracting fractal dimensions for any given image. Using
such methods, it produces results which challenge previous hypotheses and findings
in relation to fractal dimensions that predict human preference, identifying gaps in
understanding and promising future areas of research.||en