Coastal Scenery Assessment by Means of a Fuzzy Logic Approach

Abstract

Landscape is a major element affecting people’s life quality and coastal landscape evaluation is strongly rooted in the man-environment tradition. Coastal areas, all over the world, are under threat due to the conflicting requirements that rely on natural scenery of such as habitation, recreation, and industry. Since ‘coastal scenery’ is a natural resource, it has to be evaluated in an objective and quantitative way to provide a means of comparison against coastal activities and for environmental impact assessments. This chapter presents an evidence-based methodology called ‘Coastal Scenic Evaluation System (CSES)’. It is a technique that can be used not only for landscape preservation and protection, but also as scientific tool for envisaged coastal management and future development based upon plans formulated by an evidence-based approach. The results provide base-line information for a sound coastal management decision especially regarding intensive urban and industrial developments. CSES uses fuzzy logic to reduce subjectivity on decisions and obtain a quantitative evaluation of public survey research on 26 coastal scenic parameters having both physical and human perceptual characteristics. The weights of the scenic parameters were estimated by public survey questionnaires for Turkey, UK, Malta and Croatia and via consultations with coastal experts from the above mentioned four countries and Australia, Ireland, USA and Japan. Fuzzy logic mathematics was used to calculate a coastal scenic evaluation index (D) from the checklist of 26 scenic parameters by using the attributed weights of the parameters which enabled to categorize scenic values of the coastal areas into five distinct classes.

Appendices

CSES Open-Source Computational Tool

Coastal scenic evaluation system (CSES) is re-implemented in MATLAB environment in Ergin et al. (2018). The developed code ‘CSES 2018 V1’ is presented as an open-source computational tool for the coastal scenic evaluation system with User’s Manual and with an example case study on the following website: http://cses.ce.metu.edu.tr/

Addendum

For different coast sites the results of final assessment matrices (R) obtained depend on membership grading via membership matrices and weights of parameters as well as graded attributes. The entry with maximum entry value of the final assessment matrix R, as membership degree can be accepted as an evaluation state of the coastal site. Yet, considering all sites together for further comparison and for normal (Gaussian) analysis a D-value is defined. D-Values to categorize the scenery of coastal sites are evaluated on statistically described attribute values in terms of weighted areas. So, D value does not change linearly with respect to change of attributes directly when weights of parameters and membership grading remain constant for some situations. Since, D values are evaluated on areas formed by the entries of the final assessment matrices where ticked attributes with 1 and 5 are given weights as –2 and 2, respectively. The ticked attributes 3 and 4 have assumed weights as –1 and 1; and for ticked attribute 3, weight is 0. The idea here is to enhance the results on both ends firstly for preservation and protection of the coastal sites.

D – Values defined in this study depend on weights, and the membership matrices, M_i of the parameters obtained after expert decisions during the BCR and are listed in Appendix 1. Site assessments are time dependent, so D- value is not an intrinsic quantity. They are open to changes in future with further researches and coastal assessment surveys.

Appendices

4.1.1 Appendix 1: Membership-Grade Matrices (Mi) of the 26 Scenic Parameters

4.1.1.1 Scenic Parameters

M	Physical parameters
1	Cliff	Height (H)
2		Slope
3		Special features
4	Beach face	Type
5		Width (W)
6		Colour
7	Rocky shore	Slope
8		Extent
9		Roughness
10	Dunes
11	Valley
12	Skyline landforms
13	Tides
14	Coastal landscape features
15	Vistas
16	Water colour & clarity
17	Vegetation cover
18	Vegetation debris
	Human parameters
19	Disturbance factor (noise)
20	Litter
21	Sewage (discharge evidence)
22	Non-built environment
23	Built environment
24	Access type
25	Skyline
26	Utilities

4.1.2 Appendix 2: Membership-Grade Matrices

${M}_1=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.3& 0.0& 0.0\\ {}0.0& 0.3& 1.0& 0.3& 0.0\\ {}0.0& 0.0& 0.5& 1.0& 0.5\\ {}0.0& 0.0& 0.0& 0.5& 1.0\end{array}\right]$	${M}_2=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.5& 0.0& 0.0\\ {}0.0& 0.5& 1.0& 0.5& 0.0\\ {}0.0& 0.0& 0.5& 1.0& 0.5\\ {}0.0& 0.0& 0.0& 0.5& 1.0\end{array}\right]$
${M}_3=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.3& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.3& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.3\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$	${M}_4=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$
${M}_5=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.2& 1.0& 0.6\\ {}0.0& 0.0& 0.0& 0.6& 1.0\end{array}\right]$	${M}_6=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.6& 0.0\\ {}0.0& 0.0& 0.6& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$
${M}_7=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.5& 0.0& 0.0\\ {}0.0& 0.5& 1.0& 0.5& 0.0\\ {}0.0& 0.0& 0.5& 1.0& 0.5\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$	${M}_8=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.5& 0.0\\ {}0.0& 0.0& 0.5& 1.0& 0.4\\ {}0.0& 0.0& 0.0& 0.4& 1.0\end{array}\right]$
${M}_9=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.1& 0.0& 0.0\\ {}0.0& 0.1& 1.0& 0.6& 0.0\\ {}0.0& 0.0& 0.6& 1.0& 0.5\\ {}0.0& 0.0& 0.0& 0.5& 1.0\end{array}\right]$	${M}_{10}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$
${M}_{11}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.1\\ {}0.0& 0.0& 0.0& 0.1& 1.0\end{array}\right]$	${M}_{12}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.3& 0.0& 0.0\\ {}0.0& 0.6& 1.0& 0.6& 0.0\\ {}0.0& 0.0& 0.6& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$
${M}_{13}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$	${M}_{14}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$
${\mathrm{M}}_{15}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 1.0& 0.3\\ {}0.0& 0.0& 0.0& 0.3& 1.0\end{array}\right]$	${M}_{16}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.5& 1.0& 0.5& 0.0\\ {}0.0& 0.0& 0.5& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$
${M}_{17}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.2& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$	${M}_{18}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.2& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$
${M}_{19}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.0& 0.2& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.2& 0.0& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$	${M}_{20}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.2& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$
${M}_{21}=\left[\begin{array}{ccccc}1.0& 0.0& 0.2& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.3& 0.0& 1.0& 0.0& 0.1\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.2& 0.0& 1.0\end{array}\right]$	${M}_{22}=\left[\begin{array}{ccccc}1.0& 0.0& 0.2& 0.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.2& 0.0& 1.0& 0.0& 0.2\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.0& 0.2& 0.0& 1.0\end{array}\right]$
${M}_{23}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.3& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$	${M}_{24}=\left[\begin{array}{ccccc}1.0& 0.2& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.0& 0.2& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 0.0\\ {}0.0& 0.2& 0.0& 1.0& 0.2\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$
${M}_{25}=\left[\begin{array}{ccccc}1.0& 0.4& 0.0& 0.0& 0.0\\ {}0.4& 1.0& 0.2& 0.0& 0.0\\ {}0.0& 0.4& 1.0& 0.2& 0.0\\ {}0.0& 0.0& 0.4& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.0& 1.0\end{array}\right]$	${M}_{26}=\left[\begin{array}{ccccc}1.0& 0.0& 0.0& 0.0& 0.0\\ {}0.2& 1.0& 0.0& 0.0& 0.0\\ {}0.0& 0.2& 1.0& 0.0& 0.0\\ {}0.0& 0.0& 0.2& 1.0& 0.0\\ {}0.0& 0.0& 0.0& 0.2& 1.0\end{array}\right]$