Effective and timely biodiversity monitoring within protected sites and their surroundings is critical for detecting landscape changes which might impact site’s conservation status, quality and resources and to evaluate the effectiveness of conservation policies in protecting biodiversity and ecosystems from human activities. The most commonly used Land Cover/Land Use (LC/LU) or habitat classification systems are limited in their ability to read all aspects of the landscape. The Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy (Di Gregorio and Jansen, 2005) was identified as the most appropriate for providing a common language for harmonizing different LC/LU legends. One of the basic principles of this system is that a given land-cover class is defined by a dynamic combination of classifiers, thus allowing the more complex semantics of each land-cover class may be described. FAO/LCCS has been also found to be effective for translating EO-derived LC/LU classes to habitat types (Tomaselli et al., 2013; Adamo et al 2014), since it allows a better description of natural habitats in comparison to other classification systems. Furthermore, LCCS has proven to be a valid tool in change detection, both at the level of conversion and modification. In fact, changes become immediately identifiable by a difference in classifier, or through the use of additional classifiers, although maintaining the same class type. In this contribution LC and habitat mapping have been performed on a site belonging to the Natura 2000 and located in Southern Apulia (Italy), characterized by coastal environments, Mediterranean maquis and extensive pine forests. The mapping was performed by means of photo interpretation and on-site survey, in years 2007 and 2015. Different LC and habitat classification systems were used and results compared. The LCCS turned out to be the most effective in detecting changes in forest types, highlighting changes such as height and density which were not evidenced with other classification systems.

Mapping and monitoring in protected natural areas: the use of the FAO LCCS as an effective tool for habitat mapping and change detection

Tomaselli V.;Blonda P.
2017

Abstract

Effective and timely biodiversity monitoring within protected sites and their surroundings is critical for detecting landscape changes which might impact site’s conservation status, quality and resources and to evaluate the effectiveness of conservation policies in protecting biodiversity and ecosystems from human activities. The most commonly used Land Cover/Land Use (LC/LU) or habitat classification systems are limited in their ability to read all aspects of the landscape. The Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy (Di Gregorio and Jansen, 2005) was identified as the most appropriate for providing a common language for harmonizing different LC/LU legends. One of the basic principles of this system is that a given land-cover class is defined by a dynamic combination of classifiers, thus allowing the more complex semantics of each land-cover class may be described. FAO/LCCS has been also found to be effective for translating EO-derived LC/LU classes to habitat types (Tomaselli et al., 2013; Adamo et al 2014), since it allows a better description of natural habitats in comparison to other classification systems. Furthermore, LCCS has proven to be a valid tool in change detection, both at the level of conversion and modification. In fact, changes become immediately identifiable by a difference in classifier, or through the use of additional classifiers, although maintaining the same class type. In this contribution LC and habitat mapping have been performed on a site belonging to the Natura 2000 and located in Southern Apulia (Italy), characterized by coastal environments, Mediterranean maquis and extensive pine forests. The mapping was performed by means of photo interpretation and on-site survey, in years 2007 and 2015. Different LC and habitat classification systems were used and results compared. The LCCS turned out to be the most effective in detecting changes in forest types, highlighting changes such as height and density which were not evidenced with other classification systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/258694
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