Europes ecological backbone.pdf

Integrated approaches to understanding mountain regions
Wilderness attribute maps are combined using a
simple weighted linear summation MCE model as
follows:
I
=
sum
∑ j( )
W w e ij
j=
n
where:
W sum = position on wilderness continuum
w j = j th user-specified attribute weight
e ij = standardised score
n = number of attributes
Other, more complex, MCE algorithms exist
(Carver, 1991), but the weighted linear summation
model has the advantages of simplicity and
transparency. By applying different attribute maps
and weights, different continuum maps can be
produced reflecting different model and policy
requirements.
A reconnaissance-level wilderness map was
produced for the Prague conference in May 2009.
Map 10.4 is an updated version of this map using
more up-to-date information supplied by the EEA,
and has been developed using established methods
of combining wilderness attributes as GIS data
layers based on MCE techniques (Voogd, 1983;
Carver, 1991; Fritz et al., 2000; Carver et al., 2002).
The wilderness attributes used to inform the
production of Map 10.4 were each mapped
individually using the best available spatial datasets
and are as follows:
• Population density: data were derived from the
Landscan global dataset (ORNL, 2010) see also
Chapter 3. Population density is used here as an
indicator of likely population pressure on the
landscape;
• Road density: derived from the Digital Chart
of the World (DCW). This is the United States
Defense Mapping Agency's (DMA) Operational
Navigation Chart (ONC) 1:1 000 000 scale paper
map series (DCW, 1992). While this dataset is
not the most current, it has the advantage of
being consistent across all European states.
Road density was calculated using a 25 km
radius kernel density filter and is used here as
an indicator of not just road density, but also
the likelihood of encountering other human
structures such as bridges, dams, power lines,
etc., as these are most often found alongside the
road network;
• Rail density: derived from the DCW, calculated
using a 25 km radius kernel density filter
and used here, as with road density, as an
indicator of the density of the transportation
infrastructure and associated human artefacts;
• Distance from nearest road and railway line:
individually derived from the DCW as separate
attributes. Linear distance to the nearest road
link and railway line are used as indicators of
local remoteness and a proxy for likely visual
influence on the landscape from modern human
artefacts;
• Naturalness of land cover: derived by
reclassifying Corine land cover 2000 data (see
Chapter 7) into a series of five naturalness
classes. The 2000 dataset was used because,
unlike the 2006 dataset, it includes data for all
countries in Europe. The naturalness of land
cover is used as an indicator of the likely level
of human disturbance of natural ecosystem
function and vegetation patterns;
• Terrain ruggedness: derived from NASA's
Shuttle Radar Telemetry Mission (SRTM) digital
elevation model data at a resolution of 250 m.
The Topographic Ruggedness Index (TRI) (Riley
et al., 1999; Evans, 2004) was used to describe the
difference in elevation between adjacent cells of
a digital elevation grid. Terrain ruggedness is
used here as a likely indicator of difficulty of the
terrain and associated inaccessibility as well as
an indicator of scenic grandeur.
10.3.3 Wilderness in Europe's mountain areas
Numerous permutations of the above wilderness
attributes are possible and can be combined using
MCE using any number of weighting schemes
to reflect particular desired outcomes or policies.
The map shown in Map 10.4 is based on a simple
equal‐weighted combination of population density,
road density, distance from nearest road, naturalness
of land cover and terrain ruggedness. The top 10 %
wildest areas are defined on a simple equal area
percentile basis and highlighted in blue. Comparing
the resulting map against the distribution of
mountain massifs (Map 10.5) demonstrates a high
degree of correlation in the general pattern of the
core wild areas. This is perhaps unsurprising given
the inclusion of ruggedness, which is normally
associated with mountainous landscapes. The
alternative wilderness continuum map (Map 10.6)
leaves out ruggedness and therefore may be more
discriminating in its identification of wilderness
mountain landscapes, but the underlying pattern of
core high latitude and high-altitude areas remains,
together with the more fragmented pattern of
wildland areas dispersed across the remainder of
Europe.
The differences between Maps 10.5 and 10.6 appear
mainly in the local detail in that the wilderness
196 Europe's ecological backbone: recognising the true value of our mountains