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
Coastal sand dune systems across temperate Europe are presently characterized by a high level of ecological stabilization and a subsequent loss of biological diversity. The use of continuous monitoring within these systems is vital to the preservation of species richness, particularly with regard to the persistence of early stage pioneer species dependent on a strong sediment supply. Linear spectral unmixing was applied to archived Landsat data and historical aerial photography for monitoring bare sand (BS) cover dynamics as a proxy for ecological dune stabilization. Using this approach, a time series of change was calculated for Kenfig Burrows, a 6-km 2 stabilized dune system in South Wales, during 1941-2014. The time series indicated that a rapid level of stabilization had occurred within the study area over a period of 75 years. Accuracy assessment of the data indicated the suitability of medium-resolution imagery with an RMSE of <10% across all images and a difference of <3% between observed and predicted BS area. Temporal resolution was found to be a significant factor in the representation of BS cover with fluctuations occurring on a sub-decadal scale, outside of the margin of error introduced through the use of medium-resolution Landsat imagery. This study demonstrates a tractable approach for mapping and monitoring ecologically sensitive regions at a subLandsat pixel level.
Reversing the global biodiversity crisis requires not only conservation and management of species, but the habitats in which they live. However, while there is a long history of biodiversity recording, especially in Europe, information on habitats is less frequently recorded meaning knowledge of their extent and quality is generally poor. In part, this is because recording of the sometimes complex features that differentiate habitats has traditionally been done by trained professionals at a limited number of sites. However, both Earth Observation methods and citizen scientists provide opportunities to expand the range and scale of habitat recording. We provide a framework for determining how citizen scientists, particularly those that are already collecting biodiversity data, can contribute to monitoring of habitats and discuss the opportunities and challenges associated with this. We illustrate the application of our framework with reference to existing habitat recording by biodiversity recorders in the UK, both to assess the extent/quality of existing habitat, but also as a tool for validating Earth Observation data.
As part of the Biodiversity Multi-Source Monitoring System (BIO_SOS), a new approach to the classification of Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) classes from very high resolution (VHR) remote sensing data has been developed. These classes are also translated to General Habitat Categories (GHCs). Examples of the classification are presented for Cors Fochno in Wales but can be generated for any site where appropriate remote sensing data have been acquired. The system has been developed for operational monitoring of protected areas and their surrounds.
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