Identification of Agricultural Areas to Restore Through Nature-Based Solutions (NbS)

Beatrice Petti and Marco Ottaviano ()
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Beatrice Petti: Department of Biosciences and Territory, University of Molise, Cda Fonte Lappone Snc, 86090 Pesche, Italy
Marco Ottaviano: Department of Biosciences and Territory, University of Molise, Cda Fonte Lappone Snc, 86090 Pesche, Italy

Land, 2024, vol. 13, issue 11, 1-21

Abstract: This study aims to present a methodological approach based on the objectives of the Nature Restoration Law and the concept of Forest Landscape Restoration to identify areas that are best suited for the implementation of Nature-based Solutions for the improvement of landscape and habitat status in the city of Campobasso (1028.64 km 2 ). Using open data (ISPRA ecosystem services and regional land use capability), an expert based approach (questionnaire), and a multicriteria analysis (Analytical Hierarchy Process), the Total Ecosystem Services Value index was determined as a weighted additive sum of the criteria considered. The index was then classified into eight clusters, and the land use “Cropland” was extracted. Cluster 1 croplands (740.09 Ha) were identified as the areas to be allocated to Nature-based Solutions since they were those characterized by fewer ecosystem services provisioning, while Cluster 8 croplands (482.88 Ha) were identified as valuable areas to be preserved. It was then possible to compare the “Forest” areas currently present in the study area with those of a possible future scenario, represented by the areas occupied today by forest with the addition of Cluster 1 croplands. A landscape analysis was conducted; it showed greater dispersion and fragmentation of forest patches in the future scenario, but also greater connectivity and thus greater ecological functionality of the patches.

Keywords: forest; multicriteria analysis; Urban Atlas; Nature-based Solutions; croplands; ecosystem services; landscape analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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