This paper describes a new environmental composite indicator, which is based on two previously defined single indicators, related to land use (the Anthropentropy Factor) and to quality of forests (the Forest Status Quality Indicator). The framework for the definition of the composite indicator is an innovative formalization of the multidisciplinary approach, which connects knowledge and expertise of two different scientific fields: vegetation science and computer science. The proposed method for the indicator computation combines the classical algorithms of computer vision, to process data from Geographic Information Systems, and the phytosociological approach, to assess the floristic composition of the forests. The goal is to build a deep knowledge about the impact of land use and forest quality, at a landscape level, on biodiversity conservation, by studying the impact of anthropic activities, both inside (urban and rural areas) and outside (forests) the areas occupied by human activities. The knowledge is expressed by a single composite indicator and its assessment can be used for environmental preservation policy actions, to guide local government decisions for a biodiversity conservation in the landscape. The new indicator and the methodological approach is validated by presenting experimental results on two case studies in the North-West of Italy.
A New Biodiversity Composite Indicator Based on Anthropentropy and Forest Quality Assessment. Framework. Theory, and Case Studies of Italian Territory.
ASSINI, SILVIA PAOLA;ALBANESI, MARIA GRAZIA
2015-01-01
Abstract
This paper describes a new environmental composite indicator, which is based on two previously defined single indicators, related to land use (the Anthropentropy Factor) and to quality of forests (the Forest Status Quality Indicator). The framework for the definition of the composite indicator is an innovative formalization of the multidisciplinary approach, which connects knowledge and expertise of two different scientific fields: vegetation science and computer science. The proposed method for the indicator computation combines the classical algorithms of computer vision, to process data from Geographic Information Systems, and the phytosociological approach, to assess the floristic composition of the forests. The goal is to build a deep knowledge about the impact of land use and forest quality, at a landscape level, on biodiversity conservation, by studying the impact of anthropic activities, both inside (urban and rural areas) and outside (forests) the areas occupied by human activities. The knowledge is expressed by a single composite indicator and its assessment can be used for environmental preservation policy actions, to guide local government decisions for a biodiversity conservation in the landscape. The new indicator and the methodological approach is validated by presenting experimental results on two case studies in the North-West of Italy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.