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About BIO_SOS

The emphasis of the BIO_SOS project is on NATURA 2000 sites in the Mediterranean part of Europe, but also sites in the Netherlands, Wales and even the tropical rainforest of Brazil are included. The latter is a contribution to global issues as are carried out in GEO.

   

The BIOSOS project focuses on developing a robust system for habitat mapping and monitoring, named EO Data for Habitat Monitoring (EODHaM). The input data sources are multi-seasonal EO measurements and on-site data, including ancillary information and in-field measurements

It is  based on prior generation of land cover/use (LCLU) 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.

System peculiarities

a.  The system adopts deductive learning schemes, i.e. it based on expert knowledge elicitation from botanist, ecologists, remote sensing experts and management authorities.to fill the gap between LCLU and habitat domains. Ontologies are used to formally represent the expert knowledge:

  • For LCLU and habitat classes description. Prior knowledge of class spectral signatures, class  phenology (i.e temporal relations), spatial relations (e.g. adjacency, distance) as well as class attributes (e.g. shape)

  • To translate LCLU into habitat classes as both GHC and Annex 1 maps

  • To evaluate the impacts that anthropic pressure may have on biodiversity.

b.  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. LCCS is more suitable than CORINE as the land cover/use categories can be more readily translated to habitat categories (Tomaselli at. al, 2013)

c.  EODHaM has been implemented in python (open source  software)

 

The EODHaM proposed system is a 3-stage processing system  (Figure che è nella prima slide che vi ho mandato l’altro giorn, vedi qui sotto).

  • EODHaM 1st- stage provides robust classification of  bi-temporal radiometrically calibrated EO images into LCCS Levels 1 to 2, with this based primarily on spectral data, followed by

  • a 2nd stage that additionally utilises contextual information to discriminate and map classes in LCCS level 3 and beyond. Based on expert knowledge of botanists, ecologists and local site managers, land cover/use and habitat classes are described by the experts in terms of their temporal characteristics and/or spatial relationships which are used in both 2nd and  3rd third  EODHaM stages.  The outputs of the 2nd  stage are LCLU maps.

  • The outputs of the 3rd  stage include General Habitat Categories (GHCs), Annex 1 habitats as well as biodiversity indices and their trends. Thematic change maps are also provided. Once LC/LU classes and habitats are described through a semantic language, any site can theoretically be mapped and subsequently monitored over time.  

 

The expert knowledge classification approach adopted in  BIO_SOS strongly involves end users. Consequently, the products proposed 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.

WP2. For each of the sites, pre-existing information and datasets were obtained and site management authorities have been contacted and involved in the development of the EODHaM system.  The structure and composition of the landscape at each site and also the human pressures and threats have been identified by stakeholders and experts (D2.2).  A workshop was held to define stakeholder needs and to select appropriate biodiversity indicators (D2.1).

WP3. The EODHaM system architecture was defined (D3.1) and the workflow execution environment prototyped (D3.2, D3.3)

WP4.  Metadata on pre-existing datasets were collected and an evaluation of their quality and relevance to the project was undertaken (D4.1).  A metadata geoportal was also developed and a methodology for evaluating the quality of new spatial datasets was implemented (D4.5). Two new modules have been developed within the WebGIS platform for metadata management and sharing, to enhance communication.

Criteria were defined for the selection of EO data considered most appropriate for mapping and monitoring habitats (D4.4). Connections to previous and on-going projects were also established (D4.2).

Protocols for in-field collection of data were defined (D4.3).  Training sessions on in-field protocols relevant to the identification of GHCs were held in Italy, Wales, Greece and Portugal involving also local users. In-field campaigns were performed in several sites.

WP5. The main achievement has been the implementation of a standardised procedure for classifying Land Cover/Use (LC/LU) classes according to the Food and Agriculture Organisation (FAO) Land Cover Classification System (FAO-LCCS) from very high resolution (VHR) satellite and airborne (i.e., LiDAR)  and translating these to general habitat categories (GHCs), from which Annex I classes can be extracted.  EODHAM system was prototyped using commercial object-orientated software but then transferred for use within open source software (i.e. Python). The system can now be applied more rapidly and consistently across sites although future refinement of the programs is needed to ensure robustness and transferability between sites.  Based on the use of spectral indices, the EODHaM 1st stage can produce classification of vegetation/non-vegetation or aquatic/terrestrial surfaces (i.e.,  FAO-LCCS level 1 to level 2 classes (D5.3). Based on context-sensitive features, the EODHaM 2nd stage modules which had been applied to several sites can classify FAO-LCCS Levels 3 and beyond. The approach is being developed with reference to ontologies for  LC/LU class description.  Tests have also been carried out to generate translation ontologies of LCCS into GHC classes within Protégé. The Dempster-Shafer theory of evidence has been collated to handle uncertainty in the data and rules (D5.1, D5.2, D5.5).

Hyperspectral data have been analysed in Italy and Wales. Whilst hyperspectral data may be beneficial for classification of particular species types/groups, the amount of information available from time-series of VHR data allows equivalent or better classifications to be generated (D5.4).

WP6. A comparison of Habitat classification systems and LC/LU taxonomies (CORINE and IGBP, FAO-LCCS),  without precedent for Mediterranean, was carried out.   FAO-LCCS was considered the most appropriate for subsequent LC/LU into habitat maps translation (D6.1).Disambiguation rules were implemented for LC/LU  to GHC habitats  translations for the implementation of the  EODHaM 3rd stage (D6.10). 

Quantitative landscape pattern analysis framework was developed to produce composite, site/scale specific indicator set for monitoring and buffer area identification (D6.3). Protocols were defined for comparative habitat and landscape modelling across sites to relate biodiversity to habitat quality, linking in-situ and EO data (D6.2, D6.4). Based on Ecological Niche modelling (ENM),  GHCs as environmental variables appeared  better than  LC/LU to explain the distribution of the target species (D6.6, D6.7).

Threat analysis framework was established to use EO data to extract pressure trends and impacts(D6.8).

A new runoff connectivity model was proposed with R-script to calculate runoff connectivity. A new spatially implicit model for studying the effects of migration corridor size reduction on cyclic population and provide predictive biological information at a reduced computational cost with respect to explicit models was designed.

WP7. Archive and new EO data were pre-processed for several sites and fed as input to WP5.

WP8. The web page of the project has been set and continuously updated. Quality Assessment Plan (QAP) is established and implemented. An exploitation team was appointed. The first policy brief was produced. Several papers to conferences and journals have been submitted, some already published.

Description of the expected final results and their potential impacts and use

The BIOSOS project 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. Key criteria in the design of the system included a) ease of use by end users, b) use with a defined range of satellite and, in some cases, airborne (e.g., LiDAR) data and c) low reliance on existing datasets (e.g., LC/LU maps, cadastral (if updated) and infrastructure layers).  The key benefits of the EODHAM System are:

  • The use of the LCCS provides a standardized framework for LC/LU classification that is globally recognised.

  • The LCCS and subsequent GHC classes differ when change (e.g., in height, cover, frequency of inundation) occurs, and hence can be used for monitoring.

  • For end users, the building blocks of the system can be generated as background and the final LCCS categories then easily defined with reference to the LCCS components. 

  • The approach is applicable to any area across a range of scales and, in the absence of some components from EO data, other information (e.g., thematic layers) can be included.

  • The EODHaM classification and the range of indices used in its generation can also be used as input to species distribution models, thereby supporting the long-term monitoring of indicator species, which can potentially be mapped from EO data.

 

Impacts. The products and systems generated by the BIO_SOS project will be made available for policy decision making and planning (e.g., scenario building) and, more specifically, evaluating the consequences of changes within Natura 2000 sites and their surroundings.  EODHaM is compliant with on-going GEOSS, GMES and INSPIRE initiatives and its outputs will be used for:

  • Following up impact of existing and new policy.

  • Defining a buffer area for each Natura 2000 site and preventing habitat fragmentation in its surrounding area. When regional authorities elaborate and approve a plan for a protected area, a buffer zone needs to be defined around the protected area where rules different from the ones to be adopted within the site have to be defined. When they enter in negotiation with local authorities and local people, they need to support their decision with scientific evidence of the impacts that such rules may have on the areas and the importance of the buffer zone;

  • The use of BIO_SOS results is not limited to the management of Natura 2000 sites but to the integration of the sustainable management of the regional Natura 2000 network within the ordinary planning system.

  • Innovative planning activity at a local level must try to modifying a static municipal planning system into a dynamic planning system. That means that indicators are needed to establish whether the goals associated with the approved municipal plan were met during the implementation phase. Such indicators should provide a dynamic monitoring of the different planning processes and an evaluation of the effectiveness of the policies implemented.

  • Outside Europe as well, in mega-diverse countries such as Brazil and India, the operational flexibility that is required given the diversity of environments, habitats, species and threats is supported by the BIO-SOS project in that the approach is multi-resolution and multi-scale for mapping out the land cover. EODHaM system would enable managers to understand the pressure issues in multiple ecological and human environments.

 

 

 

 
 
GO TO
WP1 Project Management
WP2 User Requirements
 Completion
 WP3 EODHaM System
and service
WP4 On-site data collection
WP5 EO data processing
modules implementation
WP6 EODHaM modelling
modules development
WP7 EODHaM test on different
sampling sites and validation
WP8 Dissemination
and exploitation
 

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