Assembling Cultural and Natural Values in Vernacular Landscapes: An Experimental Analysis
[EN] Cultural landscapes can host natural and cultural areas. However, often, this distinction is not clear cut and the attempts to clarify this blur the character of landscapes. Vernacular landscapes today act as a living legacy, subject to transformation, preservation, or abandonment. This study a...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/231265 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/231265 |
| Access Level: | acceso abierto |
| Palavra-chave: | Cultural Natural heritage GIS Spatial databases Vernacular landscape Heritage database Landscape conservation |
| Resumo: | [EN] Cultural landscapes can host natural and cultural areas. However, often, this distinction is not clear cut and the attempts to clarify this blur the character of landscapes. Vernacular landscapes today act as a living legacy, subject to transformation, preservation, or abandonment. This study analyses these legacies in order to evaluate elements and interactions. The research uses GIS with spatial and thematic databases of cultural heritage and natural habitats, as well as open data, historical cartography, citizen participation, and fieldwork information sources. In combination with GIS tools, LiDAR images helped in the dataset evaluation process. A priority scale of conservation for different areas was outlined through a process cataloguing the natural and cultural assets with conservation and intervention rubrics. These settings are classified according to their cultural and natural value, conservation, surrounding environment, and potential threats. The experimental methodology of this study aims to add new options for characterising vernacular landscapes by adding soft participatory values to datasets. These prove to be reliable complementary information, improving accuracy. |
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