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    ABSTRACT: Monitoring land cover and habitat change is a key issue for conservation managers because of its poten-tial negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two sys-tems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS clas-sification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.
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    ABSTRACT: While the identification of High Nature Value (HNV) farmland is possible using the different types of spatial information categories available at European scale, most data used is still too coarse and therefore only provides an approximate estimate of the presence of HNV farmland. This paper describes two promising methods using remote sensing – one for HNV farmland identification and one for change detection within HNV farmland. The performance of the two methods is demonstrated by detailed results for two case studies – the Netherlands for the HNV farmland identification, and Bulgaria for change detection within HNV farmland. An estimation of the presence of HNV farmland or of HNV farmland change can well be based on high-resolution satellite imagery, but the classification method must be adapted to regional characteristics such as field size and type of landscape. The temporal variability and bio-climatological characteristics across Europe do not allow for a simple European classification of HNV farmland. Also comparison between years is complicated because of the large impact of seasonal variation in the land cover expression and the complexity of the HNV farmland definitions. Although HNV farmland detection methods are promising, remote sensing alone does not yet provide the appropriate tools for adequate monitoring.
    International Journal of Applied Earth Observation and Geoinformation 08/2014; 30:98–112. · 2.54 Impact Factor


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May 22, 2014