2.2. Composition and Landscape Configuration
To be able to quantify and value the different land use and land cover changes produced between 1957 and 2009 with a greater accuracy and certainty, detailed cartographic material was required. For the first period, we worked with black and white aerial photographs captured by the United States Army Map Service between 1956 and 1957 (known as the
American flight) and available from the Spanish Army Geographical Service, and these were enlarged to an approximate scale of 1:7500. These aerial photographs had to be corrected using ERDAS IMAGINE (Orthobase module) and then, through photointerpretation, the different land uses and covers were digitalized. The polygons corresponding to the different classes were individualized following the criterion shown in
Table 1 [
27]. We have differentiated between 10 different types of land uses and covers (
Figure 2). For the second period (2009), we used a black and white ICGC (Cartographic and Geological Institute of Catalonia) orthophotomap (scale 1:5000) from Cartographic and Geographic Institute of Catalonia. To verify the orthophoto map interpretation, the information for 2009 was complemented with 2013 fieldwork.
We have mapped, quantified, and described the composition and configuration of the landscape with a high accuracy (25 m pixel) through digital mapping and landscape indexes.
In ecology, the term ‘landscape metrics’ refers to the quantitative methods used to characterize classes of patches or entire landscape mosaics. The indexes of a landscape contribute interesting numerical information concerning the composition and the configuration of landscapes, the proportion of each land cover type, and the shape of the elements in the landscape. In addition, landscape indices allow useful and interesting comparisons to be made between different landscape configurations, for instance, the same area at different temporary moments or a definition of future scenes [
28]. The indices used to characterize landscape patterns between 1957 and 2009 were the following (See
Table 2): mean patch size (MPS), patch density (PD), edge density (ED), radius of gyration (GYRATE), perimeter-area fractal dimension (PAFRAC), Euclidean Nearest-Neighbour Distance (ENN), patch cohesion index (COHESION), and Shannon’s evenness index (SHEI) [
25,
29].
2.3. Creating New Agrarian Spaces Using Multi-Criteria Analysis and Geographic Information Systems (GIS)
Multi-criteria Analysis can be defined as a collection of procedures for structuring decision problems and designing, evaluating, and prioritizing alternative decisions [
30,
31,
32]. Multi criteria Analysis can be defined as a collection of procedures for structuring decision problems and designing, evaluating, and prioritizing alternative decisions. Multi criteria suitability analysis has been used in this study. Multicriteria analysis (MCA) is the integration of attribute measures for criteria relevant to decision makers’ objectives and measures of decision-makers’ preferences. A common aggregation function that combines preference weights (
wi) and criterion scores (
xi) is known as the suitability index
S. Weighted linear combination is a common means of calculating the suitability index [
33]:
MCA includes formulating an evaluation matrix E consisting of
I*
J standardized criterion scores (
e) for
I criteria across
J alternatives and a group preference weight vector
W consisting of preference weights (
w) for each criterion
i. The basic form of the weighted linear combination model can be expressed as equation 2. The weighted linear combination method is a straightforward application and can easily be spatially integrated in a geographic information system (GIS) by using raster-based map algebra.
While different techniques for multi-criteria evaluation have been developed, the most commonly accepted method is Thomas Saaty’s [
34,
35] Analytic Hierarchy Process (AHP), which has also been widely incorporated into different GIS applications to analyze aptitude [
36,
37]. The AHP first decomposes the decision problem into a hierarchy of subproblems. Then, the decision-maker evaluates the relative importance of its various elements by pairwise comparisons. The AHP converts these evaluations to numerical values (weights or priorities), which are used to calculate a score for each alternative (see
Table 3). A consistency index measures the extent to which the decision-maker has been consistent in their responses. The Analytical Hierarchy Process (AHP) approach in the GIS-MCDA can handle such soft information (preferences, priorities, judgments …). The AHP extension developed by Marinoni in 2004 [
38] was used with the Spatial Analyst extension of ArcGIS.
In our study, we established five types of criteria related to biodiversity, landscape structure and perception, cultural heritage, fire hazard, and management cost (see
Figure 3). Different scenario maps were projected using multi criteria evaluation and geospatial information available for the study area. We classified each scenario map into seven suitability categories (1 low suitability and 7 high suitability for agrarian land recovery).
In the initial analysis, a first suitability map is developed in terms of biological biodiversity linked to agrarian lands (i.e., crops, livestock, bush, or open forests). In other words, we studied the zones that roughly contained “non-forest” diversity. To estimate this biodiversity, we used the available vectorial data layers relating to the key species and key habitats linked to these agrarian spaces, obtained from the Consortium of Alta Garrotxa, the local administration of the study area.
The perception and aesthetics of the landscapes reveal that, to a great extent, a number of structures are preferred over others [
2,
39,
40]. According to Palmer [
39], we can evaluate the perception of a landscape by calculating the ecological landscape indices [
25,
29] which refer to their configuration and composition. To carry out this analysis, six indices were chosen: ED (edge density), PD (patch density), LPI (large patch index), LSI (landscape shape index), PRD (patch richness density index), and SHDI (Shannon’s diversity index). To calculate these metrics, the option “Moving window” from Fragstats
® software (version 3.3, McGarigal and Marks, Oregon State University, Corvallis, OR, United States) was used to create a raster map for each variable from the 2009 map of land uses and land covers. Finally, these raster layers were combined into one (reclassified into seven suitability categories).
Evidence that human activity had intensified in the area forming local cultures [
40] includes the vast archaeological and architectural heritage that exists in the Alta Garrotxa today. Thus, to qualify and value the cultural heritage in the study zone, we used two recently conducted studies, which recorded two types of geographical information data: monumental heritage in the Alta Garrotxa [
41] and archaeological heritage in the Alta Garrotxa, documentation obtained from Alta Garrotxa local administration. Espunya and Gallart [
41] catalogued and georeferenced each monumental heritage element in the study area indicating its conservation status, while zones of archaeological interest were delimited based on the presence of archaeological remains.
Further to this, and in an attempt to prevent high-intensity fires, we identified areas of high fire risk where reducing the vegetation may help to contain fires. To assess potential fire hazards, a variety of information sources were used [
42], along with the map of fuel models from the Fire Prevention Plan for the Alta Garrotxa (2006) [
43], all of which were modified with the information concerning the evolution of woodlands during 1957–2009. The map models’ flammability factor was developed by the Ecological and Forestry Applications Research Center in 2003 [
44]. The global radiation data (annual average in MJ/m
2) came from the Climate Atlas of Catalonia [
45], a collection of solar radiation data in Catalonia, which takes into account the variation in radiation depending on altitude and relief. The areas of greater affluence have been digitalized and a frequency map created by collecting information on crowded areas such as recreational areas, parking lots, the most popular visitor routes, or areas where people participate in adventure sports, as well as certain residential areas. We have used overlay analysis to combine the characteristics of several raster datasets into one raster layer [
46].
Finally, we have classified the study area by taking into consideration the cost for a farmer to maintain the agrarian space that is to be regained in the future. Therefore, it is paramount to ensure that any new spaces recovered are in areas of low expenditure and are affordable to those farmers who want to cultivate them. Naturally, the most remote of the inhabited areas, water bodies, and roads which were on steep slopes on private property and in areas that were not agrarian lands in 1957, will have a much higher management cost.
Apart from the previously mentioned criteria, it was thought that including a restriction, understood as a set of relationships and limitations that certain types of criteria are delimited by, would be appropriate. These restrictions have been established with the intention of not recovering any open space within or in close proximity to a mature forest or a forest that has protected species (Taxus baccata). Once the criteria were established, the subsequent step was to consider them based on their degree of importance to different social agents. Depending on the degree of importance assigned to each of the criteria, we produced very different scenarios. The core of Saaty’s process (1977) is the mechanism used to weight each of the criteria on each level of the hierarchy. This is done by making a comparison (pairwise), taking into account the contribution of each element to this hierarchy for each of the vertices immediately above that with which it is linked.