BIO_SOS - GA N. 263435 Bio diversity Multi- S ource M o nitoring S ystem:

1. BIO_SOS - GA N. 263435 Biodiversity Multi-Source Monitoring System: From Space To Species. www.biosos.eu On behalf of BIO_SOS consortium Palma Blonda° and Richard Lucas* °CNR-ISSIA, Bari-Italy blonda@ba.issia.cnr.it *Aberystwyth University, UK 3rd Space call FP7-SPACE-2010-1 (Dic. 2010-Nov. 2013)

3. BIO_SOS working objectives • The development of pre-operational automatic HR and mainly VHREO data processing and understanding techniques for: • LCLU and LCLU change maps production as an improvement of GMES core services. • The development of an ecological modelling frameworkat both habitat and landscape level to combine EO and in-situ data for: • Habitat and Habitat change mapping (GHC and Annex I) • Biodiversity indicator extraction. • Scenario analysis • as an extension of GMES downstream–services

4. BIO_SOS peculiarities • The system adopts deductive learning schemes, i.e. it based on expert knowledge elicitation to fill the gap between domains • Ontologies and semantic networks are used for • LCLU and Habitat class description; • LCLU to Habitats translation (domain ontologies) • The Food and Agriculture Organization Land Cover Classification System (FAO_LCCS) is adopted for LCLU classification since it provides the best description of natural vegetated classes compared to other taxonomies (e.g. CORINE) (Tomaselli et al. 2013). • V. Tomaselli, P. Dimopoulos • C. Marangi et. al., (2013). Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: a Mediterranean assessment. Landscape Ecology DOI 10.1007/s10980-013-9863-3. Published on March 2013.

5. BIO_SOS peculiarities • The proposed architecture is multi-modular. The modules have been developed independently to reduce the risk of a major project breakdown • Based on expert knowledge elicitation, validated LC/LU maps can be integrated with in-situ datato provide: • General Habitat Category (GHCs) and Annex I maps • LCLU and habitat change maps • Pre-existing LCLU maps produced by photo-interpretation and in-field campaigns (e.g. LCLU and in-situ data) can be exploited mainly in Mediterranean regions were habitat maps are often not available

6. The EODHaM System: Overview

7. www.biosos.wur.nl

8. A12/A2.A5.E7+O3.M233.N3.N12-AR Habitat: 1210 Sandy shore June

9. Combining LCCS Codes www.biosos.wur.nl

11. Habitat discrimination within EODHam

13. LCCS, Annex I and GHC habitat map: Le Cesine site (IT) GHC MAP: Entropy texture for height information GHC with LIDAR www.biosos.wur.nl

14. Le Cesine

15. Classification of Annex 1 HabitatsNorth Pembrokeshire Commons, Wales

16. LCCS Classification: Veluwe

17. Translation to GHC: Veluwe

18. WP 6: EODHaM modelling modules development Aim: To investigate the relationship between the GHCs, landscape features, pressures, and the abundance and composition of some taxa, both animals and plants Task 6.4 Ecological Niche Modelling (ENM) D6.6 “Selected bioindicators” Aim: Verify if GHCs can provide an useful link between EO data and the species distributions (species of conservation interest or representing biodiversity threats) D6.7 “ENM”

19. WP 6: EODHaM modelling modules development • Ecosystem state assessment (Task6.3) • Software generation (in R) to simulate spatially distributed water connectivity: • to relate slope gradient to upslope basin area • to deal with roads as connectivity modificators • Impact of ecosystem fragmentation – metapopulation • Methodology to identify locally recognizable (Task 6.8) pressures and quantify their impact on habitats extract pressure growth www.biosos.wur.nl

20. BIO_SOS achievements • EODHaM System Designed and Implemented • 1st stage: spectral rules • 2nd stage: context-sensitive features and spectral rules • 3rd stage: translation of LCLU to GHCs and Annex 1 • Potential to include expert knowledge, ancillary data • Open source software with translation out of commercial software (Python) • Scalable: UAS, WV2, Landsat, MODIS • LCCS/GHC maps generated for primary BIO_SOS sites • Capacity for change detection: • Change in class • Change in indices (pixel/object-level). • Newaccuracy assessment tools, approaches and data

21. BIO_SOS contribution to GEO Task BI-01 • Long-term baseline data (e.g., thematic maps at 1:5000 scale or finer) of land covers and habitats as well as new automatic, standardized, rapid and cost-effective monitoring techniques. • A modelling framework for evaluating the combined impact that different drivers affecting soils and/or vegetation may have on biodiversity. • BIO_SOS products will be more familiar to the End Users since they are built on their expertise and can be improved as they further engage with the process

22. BIO_SOS contribution to GEO Task BI-01 • Continuity with EBONE and links with Ms.Monina for biodiversity monitoring at regional level: VHR GHC and Annex I. • FAO-LCCS taxonomy is used as brokering tools to combine different domains (e.g. LCLU and habitats) • The output products will be made available for policy decision making; to follow up impact of existing policies and meet the commitments related to European Directive obligations.

23. Conclusions and lessons learnt • Multi-spectral/temporal EO data are more effective and less costly than available single-date hyper-spectral data. • WorldView2 data and LIDAR seems very useful for habitat monitoring. • More research is still needed for biodiversity indicator extraction from VHR imagery • Protocols for in-situ data collection should be: • harmonized based on ecological modeling (e.g. niche modeling) • agreed with Management authorities who institutionally collect data