Identifying Ecological Vulnerability of Protected Complex of Touran via the Methods of Reciprocal Effects Matrix, AHP, and EA

Introduction
Ecological vulnerability is a common term that can be used in different hierarchical levels (animate, population,
community, ecosystem, and landscape). Ecological vulnerability evaluation has lots of applications in
environmental sciences such as EIA, risk assessment and environmental monitoring. This represents the
importance of the evaluation. This paper aimed at assessing ecological vulnerability of the protected area of
Touran (in East of Iran) using a combination of three methods of overlay, i.e., reciprocal effects matrix, AHP,
and EA.
So far, a large number of researches have been published about these methods around the world and Iran, as
well. Some works in Iran are “Degradation Model” and Jabbarian's work which has innovations in objectifying
ecological vulnerability assessment with reciprocal effects matrix approach. We can also point to zonation of
environmental vulnerable and sensitive areas in west of Fars Province with method of fuzzy logic approach and
AHP.
Different methods have been used around the world to assess ecological vulnerability. Some of these
methods are FAHP and compound the methods of AHP and GIS and also Multiway Data Analysis (MDA) for
detecting relations between indicators, Reciprocal of Fractal Dimension (SPCA) and compounding ecosystem
sensitivity and landscape pattern.
More diverse indices have been used in the field of ecological vulnerability, so far. Some of these indices are
ecological Sensitivity (ES), Natural and Social Pressure (NSP), Ecological Recovery Capacity (ERC) and the
others related with landscape such as Reciprocal of Fractal Dimension (FD), Isolation (FI) and Fragmentation
(FN). In this paper, indices of Ecological Sensitivity are used because these data are available in Iran.
Materials & Methods
Protected complex of Touran is in southeast of Shahroud City, southwest of Sabzevar City and in the north of
great plain of Kavir in Semnan Province (from 55 to 57 E and from 34° 44' to 36° 22' latitude).
For calculating ecological vulnerability, first of all the reciprocal effects m atrix must be prepared. In this
method, a matrix of ecological factors in which if the points of an ecological factor effects on other factors they
are given figure of one and otherwise figure of zero. In the next steps, the summation of rows and columns and
the degree of importance of ecological factors is calculate based on following equation.
n
ij j i
1
S 􀀠􀂦( X 􀀐 X )
Where Sij is the degree of importance of ecological factor, Xi is the number of ones in the row of i and Xj is the
number of ones in column of j. Then by comparing the degrees of importance of ecological factors, table of AHP
of ecological layers was made for identifying the degree of preferences of layers. In fact, using reciprocal effects
matrix, process of preferences turned into objective method in AHP.
Extent Analysis Method used for calculating preferences degree of layers, the preferences for some of the
layers would be negative; so simple AHP was used for calculating preferences degree of ecological factors. The
Extent Analysis Method was used in FAHP for scaling classes of layers.
In the Extent Analysis Method after supplying hierarchy decision tree, pairwise comparisons was
accomplished; then, using Extent Analysis Method these qualities converted into quantitative values. Numbers
used in this method is Triangular Fuzzy Numbers. In this method for each row of pairwise comparisons matrix, 
value of Sk which is Triangular Fuzzy Number, is calculated.
􀂦 􀂦􀂦
􀀠
􀀐
􀀠 􀀠
􀂻 􀂼
􀂺
􀂫 􀂬
􀂪
􀀠 􀁵
n
j
m
i
n
j
k kj ij S M M
1
1
1 1
(1)
Where k is the number of rows and i and j are alternatives and indices, respectively. After calculating S k s,
magnitude degrees of them are obtained into each other. Generally if M1 and M2 are two Triangular Fuzzy
Numbers, magnitude degree of M1 into M2 is calculated as:
( ) ( )
( ) 1
1 2 1 2
1 2 1 2
V M M hgt M M
otherwise
V M M ifM M
􀁴 􀀠 􀂈
􀁴 􀀠 􀀡
(2)
Magnitude degree of a Triangular Fuzzy Number into K Triangular Fuzzy Numbers is calculated by this
equation:
V M M M V M M and and ( ,..., k ) ( ) ... 1 2 1 2 􀁴 􀀠 􀁴 ( ) 1 k V M 􀁴 M (3)
Also calculating weight of indices is obtained from pairwise comparisons matrix, thus:
( ) min 1 W x 􀀠 􀁞 ( i k )􀁠 V S 􀁴 S (4)
After scaling classes of layers and calculating preferences degree of layers, the obtained values are applied in
maps by GIS and using weighted overlay. Ecological vulnerability map of the area was provided. Ecological
layers used in this work are elevation and aspect with five classes and slope with eight classes gotten from 50
meter DEM, climatology with one class supplied by revised De Martonne, land use with seven classes,
vegetation density eith six classes, soil depth with five classes, erodibility of soil with five classes, water erosion
with three classes, and finally wind erosion also with five classes.
Results and Discussion
The most effective factor in ecological vulnerability that obtained through reciprocal effects matrix and AHP
method (Fig. 1) was erodibility of soil.This factor affects extremely other factors such as soil depth, water
erosion and wind erosion. The weight of this factor in AHP was obtained about 0.371. The climatology and
elevation factors are lower than the erodibility of soil. They are with preference degrees of 0.161 and 0.137,
respectively. In the end of the list both layers of soil depth and vegetation density are affected by other factors,
with preferences degree of 0.16.
Finally, the map of ecological vulnerability was obtained by weighted overlay of the layers and also by
applying scales of classes for each layer. It is remarkable that location of the areas in sensitive geological zones,
zones of deep soils, arid and warm climate, and wind erosion zones is determinant in vulnerability degree of
those areas. These layers are converted into raster format and then overlaid by their weights; finally the map of
ecological vulnerability was obtained as a result raster layer. For better land management, the area was classified
by natural breaks method (Fig. 2) into four classes of resistant, subsensitive, sensitive, and vulnerable. 
Fig. 2. The map of ecological vulnerability for protected area of Touran
Conclusions
According to vulnerability map of the area, western parts are more vulnerable relative to other areas.
Furthermore, most of the areas are placed in class of sensitive. Therefore, the protected area must be recognized
in different managerial levels for more conservational acts.