Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.
To support decisions relating to the use and conservation of protected areas and surrounds, the EU-fundedBIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO SOS) project has developedthe Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and mon-itoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization LandCover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Cate-gories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM systemuses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation(EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat typemaps are derived. An additional module quantifies changes in the LCCS classes and their components,indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e.,GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protectedsites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India
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