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Key is the challenge to develop a biodiversity observation system that is transmissible and cost effective. Measuring and reliable reporting of trends and changes in biodiversity requires that data and indicators are collected and analysed in a standard and comparable way. LiDAR is an alternative remote sensing technology that allows to increase the accuracy of biophysical measurements and to extend spatial analysis into the third dimension. The BIO_SOS project shows alternatives to measure habitat diversity as a proxy for biodiversity on the basis of plant life forms. The objective of our study is to assess to what extent LiDAR can be used to map and monitor plant life forms and associated General Habitat Categories (GHCs). The conclusions are that LiDAR provides accurate height measurements on shrubs and trees, even in early spring when no leaves are present. Canopy height models as derived from LiDAR and in combination with very high resolution satellite imagery provides a powerful tool with for the identification of plant life forms and as a direct input for spatial modelling of species distribution. Since LiDAR data are not everywhere available, finding alternatives for height feature extraction from optical imagery for might be worthwhile.

The EODHaM system offers the right mix of site specific configurability and pre-engineered processing modules and workflows, which allow to offer a habitat monitoring service, particularly suited to support the multi-annual monitoring of Natura 2000 sites. The service provisioning foresees for each site an inception phase, during which the service provider collaborates with domain experts from the site in order to setup together the optimal service chain and processing configuration, customizing the workflow steps from the range of prebuilt, configurable processing modules.

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.

Focusing on the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) and the recently proposed General Habitat Categories (GHCs) classifycation system, this paper illustrates how expert knowledge concerning class spatial arrangement in the scene at hand class, class phenology and class spectral signature in multitemporal EO images can fill the gaps between the two classification systems and provide LC/LU to habitat translation. An application to a Natura 2000 site in Southern Italy which includes a wetland costal area is discussed.

The BIO_SOS project (www.biosos.eu) focuses on developing a robust system for habitat mapping and monitoring, named EODHaM, with this based on prior generation of land cover/use (LC/LU) and change map maps and their subsequent translation to categories of habitat appropriate to support conservation agencies and land managers in decisions relating to protection of Natura 2000 sites. The input data sources are multi-seasonal EO measurements and on-site data, including ancillary information and in-field measurements. For this purpose, the Food and Agricultural Organisation (FAO) Land Cover Classification Scheme (LCCS) and the General Habitat Categories (GHCs), from which Annex I Habitats can be defined, have been proposed for describing LC/LU and habitat categories.