The thresholds of fuzzy suitability values were defined on the basis of a

The thresholds of fuzzy suitability values were

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The thresholds of fuzzy suitability values were defined on the basis of a ground survey covering 34 SRF plantations of northern and central Italy (Fig. 11.1), es- tablished between 2002 and 2006. The productive performances of the SRF planta- tions were assessed and compared to the predicted fuzzy values, giving rise to the
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204 P. Corona et al. Fig. 11.1 Geographical location of the SRF plantations surveyed following classification: (i) suitable land [fuzzy value = 0.9–1]; (ii) marginally suit- able land [fuzzy value = 0.7–0.89]; (iii) not suitable land [fuzzy value = 0–0.69]. The last elaboration phase concerned the derivation of a vector map of land suit- ability compatible with the standard land use/cover national Corine Land Cover 2000 map (CLC, minimum mapping unit = 25 ha; see European Environmental Agency 2000). The CLC classes whose conversion to SRF plantations is unlikely or impossible (urban settlements, forests, rocky outcrops, water or agricultural crops more profitable than biomass crops like vineyards) were set as constraints of the analysis (cf. Section 11.2.2). Hence, suitability classes were assigned only to pix- els falling within polygons of the following farmland classes: non irrigated arable land (CLC code = 211); pastures (CLC code = 231); annual crops associated with permanent crops (CLC code = 241); complex cultivation patterns (CLC code = 242); agro-forestry areas (CLC code = 244). Given the high management intensity of SRF, farmland areas included within designated protected areas (national and regional natural parks, Natura 2000 sites, etc.) were also excluded from the LSA. Suitability class statistics were calculated for each eligible CLC polygon. The poly- gons without suitable or marginally suitable pixels were eliminated. The polygons with all the pixels belonging to a given suitability class (pure polygons) were as- signed to that class. Polygons including suitable and marginally suitable areas of at least 10 ha, were split into pure sub-polygons; otherwise, the polygon was assigned to the suitability class occurring most often on a pixel-by-pixel basis.
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11 Land Suitability for Short Rotation Coppices Assessed 205 11.4.3 Results and Discussion The fuzzy maps of land suitability for SRF plantations in Italy are reported in Fig. 11.2. Suitable farmland for SRF plantations in Italy, resulting from the overlap of the suitable areas for all the target species, amounted to 1301051 ha, whereas marginally suitable farmland was 5380431 ha. The geographical distribution of such figures is summarized in Table 11.4. This case study demonstrates how the fuzzy approach can address the criti- cal issue of modelling the response of target species to environmental qualities and limitations of the land. The LSA fuzzy digital maps are products that can be used by land planners as decision-support tools. For instance, they can be eas- ily used by regional agricultural and forest services and rural planning decision- makers to outline the most suitable land areas for subsidising SRF plantations.
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  • Summer '20
  • Dr joseph
  • Test, Sula, Land use planning, Piermaria Corona

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