Landscapes Forest and Global Change - ESA - Escola Superior ...

E.R. Diaz-Varela et al. 2010. Multiscale analysis of land use heterogeneity and dissimilarity
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the extension of the map (Saura, 2003). We generated artificial landscapes for three values of p:
the value above which the size of the largest cluster in the map starts to vary (0.45), an
intermediate value (0.50), and the value immediately below the percolation threshold (0.58).
Values beyond the percolation threshold generated almost complete dominance of one of the
land cover classes in preliminary tests.
We considered five land cover classes, the proportion of each class following two types of
distributions: “equiprobable” (each class 20%), and “non-equiprobable” (with classes
distributed 50%; 30%; 10%; 5% and 5%). In the latter case, the aim was to resemble a real
distribution of land cover with one land cover clearly, but not completely, dominant, being the
respective modeled land cover types Planted forest, Agriculture, Urban, Natural forest, and
Shrubland. The “equiprobable” distribution is aimed to obtain results for an even distribution of
results which can be used as a theoretical. In the “non-equiprobable” distributions, a more
realistic landscape response is expected. For each of these cases, four different MMUs were
considered, for 1 pixel, 10 pixels, 50 pixels and 99 pixels, i.e. a total of 24 different landscape
cases. Each case was coded with a sequential number representing the generation parameters,
e.g.: “N5m1-58a” for 5 classes, MMU=1; p= 0.58; “a” type of class proportion, being “a”
equiprobable, and “b” non-equiprobable.
2.3. Analysis of landscape heterogeneity and dissimilarity
A common approach for the analysis of landscape heterogeneity is the application of landscape
pattern metrics to a categorical map (O’Neill et al., 1988; Botequilha-Leitao and Ahern, 2002;
McGarigal et al., 2002; Botequilha-Leitao et al., 2006). Although landscape metrics are
extensively used, careful consideration is required as to which are the most adequate as regards
the aims of the study and the spatial data available (Li and Wu, 2004; Corry and Nassauer,
2005; Diaz-Varela et al., 2009b; Dramstad, 2009). One of the problems with application of
landscape metrics to an entire study area lies in that metrics do not reveal the spatial distribution
of the variables studied, and their scale-dependence, making necessary a system for subdivision
into intermediate scales. Previous studies on the subject made by the authors revealed the utility
of moving-window approaches in calculation of landscape indices (Botequilha-Leitao and
Díaz-Varela, 2009; Díaz-Varela et al., 2009a), namely those related to information theory, like
the Shannon-Wiener index. The Shannon-Wiener index was developed as a measure of the
information content in a code (Shannon and Weaver, 1949), and is calculated by the expression
(1):
SHDI
m
= −∑
p i
⋅ log p
i=
1
i
(1)
Where p i is the proportion of the landscape occupied by the class type i, and m the total number
of classes.
Dissimilarity was analysed by means of contrast metrics, namely the Contrast Weighted Edge
Density (CWED). CWED is calculated following the expression (2) (McGarigal et al., 2002):
CWED =
m
m
∑∑
( e ik
dik
)
= i
A
(10000)
(2)
i= 1 k + 1
Where e ik is the total length (m) of edge in landscape between patch types (classes) i and k;
includes landscape boundary segments involving patch type i; d ik is the dissimilarity (edge
contrast weight) between patch types i and k; and A, the total landscape area (m 2 ). d ik was
estimated by our subjective criteria over the dissimilarity among land cover types, and
represented in the following table:
Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology
Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.)
2010, Instituto Politécnico de Bragança, Bragança, Portugal.