Background: The aim of the maps was to develop one such “new
geography” which supports their territorial identity through the identification
of natural and environmental assets. The characterisation of territories
provides baseline information about the environmental “value” of a specific
region, i.e. if the region owns environmental assets that make it unique and
that hence could support the development of the region by properly and
sustainably exploiting the asset item.
Input
Data:
- Urban – rural typologies / Rural
typologies
- High nature value farmlands
- Proximity to natural areas (CLC
semi-natural classes, N2000, CLC water)
- Air quality (PM10)
- Degree of soil sealing
The
range of values in the different input data sets was standardised to five
classes. These five classes were assumed to represent a gradient of “natural
and environmental assets” for each grid cell.
The
distribution of input data values to the five output classes were based on the
median of the original data and their standard deviation. Scores are attributed
to each class.
Processing
and results:
The
degree of “natural and environmental assets” is calculated from the sum of the
five individual input layers. The range of values is between 5 (= 5 x 1) and 75
(= 5 x 15). The resulting layer was classified into five classes:
Class name
|
Definition
|
1 Very low natural assets
|
5 – 22 points
|
2 Low natural assets
|
23 – 35 points
|
3 Average natural assets
|
36 – 48 points
|
4 High natural assets
|
49 – 60 points
|
5 Very high natural assets
|
61 – 75 points
|
In a
next step, the “natural and environmental assets”-classification was clipped to
the mountain-layer.
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