Historic gardens are important parts of humanity’s built heritage within the designed landscape, providing many environmental, economic and socio-cultural benefits. Management is a key part of their conservation, perhaps the most difficult because it is costly, must be continual, and requires a skilled workforce. This systematic review looks at the literature addressing historic garden management, with special attention regarding the social, economic and environmental aspects of sustainability. Academic studies on this subject come from many different disciplines, making it both stimulating and fragmented. It is now time to consolidate these interdisciplinary efforts into a clear vision, including a framework of key themes and research methods so as to better coordinate efforts and make the information and innovation generated more accessible to the garden managers “in the trenches”. With this aim, reviewed studies are classified according to 10 criteria: supply or demand orientation; management phase involved; primary sustainability processes addressed; geographic criteria; number of sites covered; policy documents referred to; kind of data collected; study methods employed; possibility of bias specifically regarding historic gardens; garden use. An analysis of these criteria shows that historic garden management literature focuses on describing the gardens themselves, with few studies interested in the people supporting them. Future research should follow recent policy documents’ lead and pay more attention to community value and involvement.

This study attempts to measure the capacity of the EU geographical certification scheme to positively influence the price of certified products and the incomes of their producers. A comparison of the economic results of two cheese-producing dairy farm enterprises with different business strategies and locations within the Sicilian hinterlands is performed in order to determine the transformation value of each dairy’s sheep milk into pecorino cheese (with and without the Protected Designation of Origin, or PDO, certification) and the related joint products (ricotta). The economic convenience of the total transformation of sheep milk into Pecorino Siciliano PDO and ricotta is also appraised. The results suggest that producing and commercializing Pecorino Siciliano PDO is a promising strategy for differentiating and promoting dairy farm products and improving the financial performance of producers, with foreseeable positive repercussions in the socioeconomically less favored rural areas where they are located.

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data’s spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments.

Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Mission), hyperspectral data in narrow bands spanning visible/near infrared to shortwave infrared are now available. In this study, hyperspectral data from PRISMA were used with the aim of testing the applicability of PRISMA with different band sizes to classify tree species in highly biodiverse forest environments. The Serre Regional Park in southern Italy was used as a case study. The classification focused on forest category classes based on the predominant tree species in sample plots. Ground truth data were collected using a global positioning system together with a smartphone application to test its contribution to facilitating field data collection. The final result, measured on a test dataset, showed an F1 greater than 0.75 for four classes: fir (0.81), pine (0.77), beech (0.90), and holm oak (0.82). Beech forests showed the highest accuracy (0.92), while chestnut forests (0.68) and a mixed class of hygrophilous species (0.69) showed lower accuracy. These results demonstrate the potential of hyperspectral spaceborne data for identifying trends in spectral signatures for forest tree classification.