Assessing the Integrity of the Historical Landscapes

Three Case Studies in Some Terraced Areas
Chapter
Part of the Environmental History book series (ENVHIS, volume 1)

Abstract

Considering the importance of terraced cultivation for economic in the areas surveyed by this research, it has been decided to develop an investigation in order to analyse their features and assess the historical integrity of some areas considered as having a high level of integrity. The study is useful also in view of the funds available for the restoration of terracing according to the Common Agricultural Policy and the initiatives taken by the Ministry of Agriculture Food and Forestry, but also for the establishment of the national register of traditional landscapes. The latter will require a definition of the level of integrity in order to include the selected areas in the register. In this research three case studies were identified, Lamole, Valdobbiadene and Costa Viola, located respectively in the north, the center and the south of Italy, choosing an extension of about 800–1,000 ha for each one of them. For each area a methodology defined “Historical Cultural Evaluation Approach” was applied, already developed as part of a project for the monitoring of the Tuscan landscape (Agnoletti in Il paesaggio agro-forestale toscano. Strumenti per l’analisi, la gestione e la conservazione, ARSIA, Firenze, 2002; Agnoletti in The conservation of cultural landscapes, CAB International, New York, 2006). The project for Tuscany considered three dates starting from the early nineteenth century, but in our case, to simplify the analysis we adopted as a starting point the landscape in the 1950s. The aerial photos taken in this period represent Italy before the industrialization of agriculture, allowing us to understand the traditional features of the agricultural landscape and make a comparison with recent aerial photos representing the situation after industrialization.

Keywords

Secondary Succession Olive Grove Stone Wall Arable Crop Wooded Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Mauro Agnoletti
    • 1
  • Antonio Santoro
    • 2
  • Lorenzo Gardin
    • 3
  1. 1.DEISTAFUniversity of FlorenceFlorenceItaly
  2. 2.DEISTAFUniversity of FlorenceFlorenceItaly
  3. 3.Studio GardinFlorenceItaly

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