Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Lymphovascular and perineural invasion as selection criteria for adjuvant therapy in intrahepatic cholangiocarcinoma: a multi-institution analysis

AbstractObjectivesCriteria for the selection of patients for adjuvant chemotherapy in intrahepatic cholangiocarcinoma (IHCC) are lacking. Some authors advocate treating patients with lymph node (LN) involvement; however, nodal assessment is often inadequate or not performed. This study aimed to identify surrogate criteria based on characteristics of the primary tumour.MethodsA total of 58 patients who underwent resection for IHCC between January 2000 and January 2010 at any of three institutions were identified. Primary outcome was overall survival (OS).ResultsMedian OS was 23.0months. Median tumour size was 6.5cm and the median number of lesions was one. Overall, 16% of patients had positive margins, 38% had perineural invasion (PNI), 40% had lymphovascular invasion (LVI) and 22% had LN involvement. A median of two LNs were removed and a median of zero were positive. Lymph nodes were not sampled in 34% of patients. Lymphovascular and perineural invasion were associated with reduced OS [9.6months vs. 32.7months (P= 0.020) and 10.7months vs. 32.7months (P= 0.008), respectively]. Lymph node involvement indicated a trend towards reduced OS (10.7months vs. 30.0months; P= 0.063). The presence of either LVI or PNI in node-negative patients was associated with a reduction in OS similar to that in node-positive patients (12.1months vs. 10.7months; P= 0.541). After accounting for adverse tumour factors, only LVI and PNI remained associated with decreased OS on multivariate analysis (hazard ratio4.07, 95% confidence interval 1.60–10.40; P= 0.003).ConclusionsLymphovascular and perineural invasion are separately associated with a reduction in OS similar to that in patients with LN-positive disease. As nodal dissection is often not performed and the number of nodes retrieved is frequently inadequate, these tumour-specific factors should be considered as criteria for selection for adjuvant chemotherapy

Object-based random forest classification for mapping floodplain vegetation structure from nation-wide CIR and LiDAR datasets

Very high resolution aerial images and LiDAR (AHN2) datasets with a national coverage provide opportunities to produce vegetation maps automatically. As such the entire area of the river floodplains in the Netherlands may be mapped with high accuracy and regular updates, capturing the dynamic state of the vegetation. In this study, these fused datasets are used to map the vegetation of 936 ha of the floodplain on the north-side of the river Nederrijn near Wageningen into ten vegetation structure classes. The method follows object-based image analysis principles. Objects are defined in segmentation and subsequently labeled using the ensemble-tree classifier random forest. The mapping scale is controlled by selecting segmentation parameters from quantified discrepancies between reference polygons and segmented objects. Effects on the mapping scale of different reference polygons and different segmentation data is investigated. The results show that it is important to be able to select the right segmentation parameters to control the mapping scale. A discrepancy measure with reference polygons is a suitable method to do this objectively. The use of random forest classification on the objects resulted in an estimated classification accuracy of 86% on the basis of the built-in cross-validation estimate of random forest. Variable importance measures of random forest showed that the AHN2 lidar dataset is a valuable addition to the spectral information contained in the aerial images in the classification.</p

A new European Landscape Classification (LANMAP): A transparent, flexible and user-oriented methodology to distinguish landscapes

We have developed a new hierarchical European Landscape Classification that can be used as a framework for, e.g., indicator reporting and environmental sampling. Landscapes are ecological meaningful units where many processes and components interact. And as such, landscapes themselves have resulted from long-term interactions of natural abiotic, biotic and anthropogenic processes. A good understanding of landscapes is essential for its assessment, protection, management and planning. An internationally consistent approach is therefore obligatory and the production of landscape classifications and associated maps is an important tool in this context. Although intuitive maps are available there are no consistent quantitative maps of European landscapes. In this paper, landscapes are regarded as forming recognizable parts of the earth's surface and as showing a characteristic ordering of elements. The complex nature of the underlying scientific concepts, which sometimes overlap and conflict, requires an objective and consistent methodology, as described in the present paper. As there are many regional differences in landscape properties, it is crucial to strike the right balance between reducing the inherent complexity and maintaining an adequate level of detail. Against this background, a European Landscape Map (LANMAP) has been produced, making use of available segmentation and classification techniques on high-resolution spatial data sets. LANMAP is a landscape classification of Pan-Europe with four hierarchical levels; using digital data on climate, altitude, parent material and land use as determinant factors; and has 350 landscape types at the most detailed level. At this level there are 14,000 mapping units with a minimum mapping unit of 11 km2. Thus far, LANMAP is limited to a biophysical approach, since there is a lack of consistent and European-wide data on cultural–historical factors. This paper describes the conceptual background of LANMAP, its methodology and results, and shows its potentials and limitation

Compilation and Assessment of Pan-European Land Cover Changes

Land cover is changing in many parts of Europe at an increasing rate. The knowledge on these land cover changes is important for spatial planning, resource evaluation, ecological modelling etc. Modification of ecosystems is most visible through changing land cover. Furthermore, spatio-temporal models that describe and predict land cover change due to social and economic processes need reliable information on land cover changes in order to calibrate and validate these models (Kaufman and Seto, 2001). Against this background three new Pan-European land cover databases were created for the years 1960, 1990 and 2000. The main objective of this paper is the compilation of land cover databases for Pan-Europe and the assessment of land cover changes. The main data source of the land cover databases for the years 1990 and 2000 are the CORINE Land Cover databases (CLC1990 and CLC2000) (Heyman et al., 1994, Nunes de lima, 2005). The CLC databases were extended to Pan-Europe with national land cover databases (Switzerland and Norway), GLC2000 and PELCOM data. The thematic detail of these databases is CORINE level 3 nomenclature and the spatial resolution is 100m. The Pan-European land cover database for the year 1960 (HISLU60) is based on digital processing of data from the World Atlas of Agriculture (WAA, 1969). This database helps to better understand the major land cover changes in Europe over a longer time-span. Several thematic and spatial aggregations facilitate the comparison of the databases at various scales. The aim of the paper is a) to present the compilation of Pan-European land cover databases for 1960, 1990 and 2000, b) to provide an assessment on land cover changes between the years 1960-1990 and 1990-2000, and c) to give an idea on the accuracy and reliability of those change

Synergy of airborne LiDAR and Worldview-2 satellite imagery for landcover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

A major challenge is to develop a biodiversity observation system that is cost effective and applicable inany geographic region. Measuring and reliable reporting of trends and changes in biodiversity requiresamongst others detailed and accurate land cover and habitat maps in a standard and comparable way.The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification resultsfor a Dutch case study. The EODHaM system was developed within the BIO SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each landcover and habitat class based on spectral and height information. One of the main findings is that canopyheight models, as derived from LiDAR, in combination with very high resolution satellite imagery providesa powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping forany location across the globe. The assessment of the EODHaM classification results based on field datashowed an overall accuracy of 74% for the land cover classes as described according to the Food andAgricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while theoverall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC)system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping uniton the basis of the composition of the individual life forms and height measurements. The classificationshowed very good results for forest phanerophytes (FPH) when individual life forms were analyzed interms of their percentage coverage estimates per mapping unit from the LCCS classification and validatedwith field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might alsobe due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification resultsencouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, inpreparation for new habitat classifications

Procesindicatoren PAS : rapportage 2016

In het kader van het Programma Aanpak Stikstof (PAS) is in 2016 doorgewerkt aan een aantal onderwerpen rondom de ecologische onderbouwing van het PAS, waaronder de procesindicatoren. Binnen de PAS-monitoring is afgesproken dat het proces van natuurherstel ook op korte termijn gevolgd wordt om zo snel mogelijk de effectiviteit van de herstelmaatregelen in kaart te brengen. Hiervoor zijn de PAS-procesindicatoren ontwikkeld. Deze procesindicatoren zijn vooral bedoeld om een indicatie van het herstelproces te geven. Deze procesindicatoren kunnen verschillen per habitattype en per maatregel, maar ook per gebied. Om die redenen is een flexibel systeem ontworpen met diverse parameters: luchtfoto’s, abiotische metingen, vegetatie en soorten. De huidige rapportage betreft de verslaglegging van de ontwikkelde systematiek van PAS-procesindicatoren

Historic land cover changes at Natura 2000 sites and their associated landscapes across Europe

The Habitat Directive is a an important policy instrument for nature conservation in order to avoid the further loss of important European habitats and to reduce the amount of pressures on these habitats. The set-up of a effective network of Natura 2000 sites is the most important goal of the the Habitat and Bird Directive. In order to test the potential efficiency of this network in protecting landscapes from land use changes that have a significant impact on biodiversity, a historical analysis of land use changes over the last fifty years was undertaken for 71 Natura 2000 sites and their direct surroundings across the EU. The analysis of historical land use changes in and round Natura 2000 sites shows that conservation sites have a positive effect on reducing not only the speed of land cover changes but also on the types of land cover changes. Unfortunately, the speed of land cover changes outside protected sites does hardly slow down and causes increased isolation of protected sites and fragmentation of remaining habitats within the landscapes. There-fore, the landscapes in which the remaining habitats are embedded should receive more attention for conservation strategies. This paper has a primary focus on historical land cover changes not only directly inside and outside Natura 2000 sites across Europe but also at the scale of land cover changes within the associated landscapes and what does it implies for the future

Linking pan-European land cover change to pressures on biodiversity - Biopress final report 1st January 2003 - 31st December 2005, sections 1 to 4

BIOPRESS – Linking pan-European land cover change to pressures on biodiversity – is a 3 year EC-FPV project funded in the framework of the GMES ‘Global Monitoring for Environment and Security’ initiative (http://gmes.fdc.fr/what_is/home.html). It was the only GMES project under the priority theme "Land cover change in Europe”. BIOPRESS’s main goal was to provide the EU-user community with quantitative information on how changes in land cover and land use has affected the environment and biodiversity in Europe. The project aimed at producing consistent and coherent sets of historical (1950 – 1990 – 2000) land cover change information in and around circa 75 Natura2000 sites located from the boreal to the Mediterranean, and from the Atlantic to the continental regions of Europe. These land cover change statistics would be converted into quantitative measures of pressures on biodiversity through the integration of socio-economic indicators. The impact of the land cover changes on biodiversity would also be assessed. The change statistics were produced by means of two parallel activities, the backdating of CORINE land cover 1990 of circa 75 windows (30km x30km) with aerial photography of the 1950’ies and, the interpretation of aerial photography from 1950, 1990 and 2000 for circa 50 transects (2km x 15km). The windows were interpreted to identify the CORINE level 3 land cover and use classes to a minimum mapping unit of 25 ha. The transects, at the other hand, were interpreted to a minimum mapping unit of 0.5 ha. Scientific achievements: Data access The BIOPRESS team established an operational online access point for metadata and data of relevant European datasets. The European data policy appears to be the major obstacle for easy access to European datasets even in case of projects that are financed by the European Commission. The INSPIRE initiative as well as the GMES framework could benefit from the experiences made in the BIOPRESS project in order to streamline access to European wide data relevant for environmental monitoring. Land Cover change The methodological development for production of land cover change matrices was completed successfully ensuring the BIOPRESS team had the appropriate tools (list of 30km x 30km window sites, list of 2km x 15km transect sites, interpretation manual, quality assurance protocol and meta database designed to follow progress) and material (aerial photography) to successfully carry out the photo to photo (1950 – 1990 – 2000) interpretation of transects and CORINE Land Cover 1990 backdating (1950-1990). Several of the tools, in particular the interpretation manuals, have the potential of being adopted by GMES services and future EU projects (The GEOLAND and GSELAND projects were given copies of the manuals on request). A total of 57 transects and 73 windows were interpreted. The results were stored in a database. The database will be made available to the wider research community in 2007. The total extent of land cover changes that have occurred within all BIOPRESS windows account only to 9,62 % of the total measured area. In other words, 90,38% of the measured area within the BIOPRESS windows have shown no change of land cover at all. Overall the most important land cover conversions based on CORINE level 2 nomenclature can be summarised as one of the following: • FROM shrub and/or herbaceous vegetation association TO forests, and its inverse conversion, FROM forest TO shrub and/or herbaceous vegetation association • FROM heterogeneous agricultural areas TO urban fabric, as well as TO forest • FROM arable land TO industrial, commercial, and transport units. Because the focus was on biodiversity and historical land cover changes, it was clear from the start that Europe had to be sampled. Bias was introduced in the BIOPRESS samples by (1) relying on an expert to select a superset of samples including Natura 2000 sites and (2) the availability of aerial photography. The project’s resources limited the total number of samples acquired. As a result some bio-geographical regions were under represented in the sample (Boreal and Mediterranean) whilst other regions were over represented (the Alpine, Atlantic, and Continental). So the development of an appropriate extrapolation approach was seen as a challenge from the beginning of the project. The key was to produce information which is useable in the data integration and which is meaningful, and reliable enough for use by our key stakeholder, the EEA. An extensive sensitivity analysis and the development of minimum land cover accordance maps have provided an excellent insight in the acquired land cover change data with respect to samples’ representativeness of biogeographical areas and land cover. Quality assurance and error propagation The following problems were identified as the main sources of possible mistakes and lack of correspondence in windows: • Ambiguity of CLC classes delineation. • Quality of B&W AP. • Availability of ancillary data. • Separation of CLC classes in B&W AP (E.g. burnt areas). • Diversity within class definition. • Occurrence of polygon less than 0.5 ha. • Amalgamation of objects less than 0.5 ha. • Real changes omitted. • Identification of questionable changes. • Identification of point and linear features, questionable, ambiguity and unknown relevance The quality of the input data was comparable for all transects, indicating that the comparability of results between partners and transects was unlikely to have been influenced by the quality of the input data. The date of the aerial photos (1950, 1990 or 2000) proved to have no influence on the thematic consistency of the interpretations whereas the level of thematic detail did have a high impact. The geometric accuracy was more difficult to evaluated, still we found that the controllers identified more spatial structures than the local interpreters. The quality of the interpretation depends on the land cover characteristics of the individual transects or windows. An error model was developed describing each step of the land cover change production chain. We found that most error sources which reduced the interpretation quality such as image quality, unclear class definition and confusion caused by lax use of land cover and land use attributes in the definitions, can be almost completely reduced by using modern data sources and adjusting the interpretation methodology. However, knowledge and experience of the interpreter play an important role in manual visual interpretation of remotely sensed data. Land cover change and pressures A land cover change - pressure association matrix was developed. This matrix enabled the grouping of types of land cover changes related to one of the six pressures under consideration in BIOPRESS: Urbanisation, intensification, afforestation, deforestation, abandonment and drainage. This cross-tabulation matrix is a fundamental starting point in the analysis of land cover change, because it provides a national-scale assessment of not only the losses or gains in the area of specific land categories but what these changes represent in terms of types of pressures However, additional research is needed to analyse this matrix according to its various components in order to gain more insight into the potential processes that determine a pattern of land cover change. It was impossible to derive a simple and practical list of indicators that would consistently explain the pressures on biodiversity. It was clear that there were multiple potential indicators, and the best indicators have not simply appeared out from the extensive data and information that already existed. In essence, the search for a coherent set of pressure indicators was a frustrating and time-consuming activity. It is clear from our effort that a single set of indicators would never explain the whole dynamics of anthropogenic pressures on biodiversity, and would go only some way towards meeting the needs for understanding the observed land cover changes. The main research challenge faced was to define a pattern-process model of land cover dynamics in space and time in order to combine the local level measurement of the land cover changes (e.g. BIOPRESS windows) and the socio-economic indicators of a larger region (e.g. the countries). The proposed multi-representation model is based on the degree of variability in the behaviour of generalised statistics and their dependency of the spatial generalisation of the variable values at different spatial scales. A systematic analysis of spatial coincidence between land cover types in CLC1990 and Annex1 habitat types recorded in Natura 2000 sites was carried out, translated into the EUNIS habitat classification and summarised per Biogeographical region. The work showed that a significant improvement could be made by adopting a regional approach providing neater and more specific links between CLC classes and habitats than what has been available so far. It also identifies what the limitations are in attributing habitat types to CLC classes. We found that the BIOPRESS land cover change product was suitable for quantifying some pressures on biodiversity but quite insufficient for the interpretation of land cover change related to other pressures: • BIOPRESS contributed very positively to the quantification of urbanization across Europe between 1950 and 1990/2000. • BIOPRESS land cover product made a useful contribution to the quantification of afforestation and deforestation across Europe between 1950 and 1990 but that these pressures could be better understood if (i) we had more points in time, closer together and (ii) more information on the condition of forest was derived from remote sensing and/or ancillary data was used to evaluate the ecological value of forested land. • BIOPRESS will have underestimated the extent to which the pressure land abandonment is threatening biodiversity in Europe, in comparison to other existing assessments (e.g. MIRABEL but also national scale statements). However, it would be possible to increase the accuracy and the generic value of the BIOPRESS estimates by (i) broadening the definition of land abandonment i.e. modifying the pressure matrix, so that it matches what is meant in other assessments and (ii) by increasing the number of points in time. • BIOPRESS was probably the first project to provide quantitative estimates about the shift from small scale to more large scale agriculture for such a large sample area across Europe and in this respect, this is a very important contribution to understanding changes in European biodiversity. However it is important to keep in mind that what has been quantified within BIOPRESS was only a small part of what is usually understood by farming intensification in biodiversity assessments. This means that, as was the case for land abandonment, BIOPRESS results will greatly underestimate the pressure farming intensification, compared to other assessments. The main conclusion is that remote sensing products such as the BIOPRESS land cover change product can provide very helpful information in the field of biodiversity assessment. There is potential for improving this information, e.g. by adding time steps in the monitoring or using external data to help in the interpretation of land cover change. However, our work also shows that there are clear limitations in this contribution and that remote sensing will only provide part of the information. One important recommendation that would lead to improve facilities for large scale biodiversity monitoring would be the integration of remote sensing products with in situ information. This recommendation forms the basis of a position paper produced jointly by BIOHAB (FP5 funded Concerted Action) and BIOPRESS. Socio-economic relevance and policy implications: The project supported the needs of DG-Environment and EEA in helping to implement and assess European policy on nature and biodiversity and contribute to the objective of enhancing the quality of the environment by helping to understand pressures on biodiversity arising from land cover change in the member states and accession countries. The state of the environment is perceived as an important indicator of a high quality of life by a majority of European citizens. The European public increasingly expresses the wish to be informed by policy on the perceived threats to biodiversity. BIOPRESS supported the development of a European capacity for monitoring the state of the environment (GMES) to meet these information needs. Conclusions: BIOPRESS was one of the first wave of thematic projects which were funded through the GMES initiative. As a result its main objective was to produce information at European level which in the case of BIOPRESS was information on historical land cover change for the purpose of assessing past pressures on habitats and their associated biodiversity. A large part of the project’s resources were used to deliver the land cover change database successfully and the outcome has not only been the delivery of data but also a set of tools for future European wide land cover monitoring. The real challenge was when trying to establish a link between land cover change and pressures on biodiversity. The development of the land cover change - pressure association matrix as a first step enabled the grouping of types of land cover changes related to one of the six pressures under consideration in BIOPRESS. This matrix has the potential to enhance the similar ‘Land Cover Flow’ matrix developed by the EEA as part of the EEA Land Accounting System. In theory the idea of integrating socio-economic data with land cover change data made sense but in practice the team struggled with the wide variety of data types, spatial and temporal resolutions. To assess the consequences of the observed Land cover changes on habitats and their biodiversity, BIOPRESS impact tables were developed using the same conceptual approach as that establish for the DPSIR assessment MIRABEL. The overall agreement between MIRABEL and the BIOPRESS tables, which unlike MIRABLE provided quantitative estimates for a selected sample of land in each region, was an important result. This part of the work concluded that a land cover change product such as that produced by BIOPRESS was suitable for quantifying some pressures on biodiversity but quite insufficient for the interpretation of land cover change related to other pressures. The error propagation, quality assessment and data search exercises highlighted the importance of the availability of good quality, affordable data (e.g. aerial photography, digital elevation data, social and economic indicators) which for long term monitoring should be continuously collected in a consistent manner

Linking pan-European land cover change to pressures on biodiversity - Biopress final report 1st January 2003 - 31st December 2005, sections 5 and 6

BIOPRESS – Linking pan-European land cover change to pressures on biodiversity – is a 3 year EC-FPV project funded in the framework of the GMES ‘Global Monitoring for Environment and Security’ initiative (http://gmes.fdc.fr/what_is/home.html). It was the only GMES project under the priority theme "Land cover change in Europe”. BIOPRESS’s main goal was to provide the EU-user community with quantitative information on how changes in land cover and land use has affected the environment and biodiversity in Europe. The project aimed at producing consistent and coherent sets of historical (1950 – 1990 – 2000) land cover change information in and around circa 75 Natura2000 sites located from the boreal to the Mediterranean, and from the Atlantic to the continental regions of Europe. These land cover change statistics would be converted into quantitative measures of pressures on biodiversity through the integration of socio-economic indicators. The impact of the land cover changes on biodiversity would also be assessed. The change statistics were produced by means of two parallel activities, the backdating of CORINE land cover 1990 of circa 75 windows (30km x30km) with aerial photography of the 1950’ies and, the interpretation of aerial photography from 1950, 1990 and 2000 for circa 50 transects (2km x 15km). The windows were interpreted to identify the CORINE level 3 land cover and use classes to a minimum mapping unit of 25 ha. The transects, at the other hand, were interpreted to a minimum mapping unit of 0.5 ha. Scientific achievements: Data access The BIOPRESS team established an operational online access point for metadata and data of relevant European datasets. The European data policy appears to be the major obstacle for easy access to European datasets even in case of projects that are financed by the European Commission. The INSPIRE initiative as well as the GMES framework could benefit from the experiences made in the BIOPRESS project in order to streamline access to European wide data relevant for environmental monitoring. Land Cover change The methodological development for production of land cover change matrices was completed successfully ensuring the BIOPRESS team had the appropriate tools (list of 30km x 30km window sites, list of 2km x 15km transect sites, interpretation manual, quality assurance protocol and meta database designed to follow progress) and material (aerial photography) to successfully carry out the photo to photo (1950 – 1990 – 2000) interpretation of transects and CORINE Land Cover 1990 backdating (1950-1990). Several of the tools, in particular the interpretation manuals, have the potential of being adopted by GMES services and future EU projects (The GEOLAND and GSELAND projects were given copies of the manuals on request). A total of 57 transects and 73 windows were interpreted. The results were stored in a database. The database will be made available to the wider research community in 2007. The total extent of land cover changes that have occurred within all BIOPRESS windows account only to 9,62 % of the total measured area. In other words, 90,38% of the measured area within the BIOPRESS windows have shown no change of land cover at all. Overall the most important land cover conversions based on CORINE level 2 nomenclature can be summarised as one of the following: • FROM shrub and/or herbaceous vegetation association TO forests, and its inverse conversion, FROM forest TO shrub and/or herbaceous vegetation association • FROM heterogeneous agricultural areas TO urban fabric, as well as TO forest • FROM arable land TO industrial, commercial, and transport units. Because the focus was on biodiversity and historical land cover changes, it was clear from the start that Europe had to be sampled. Bias was introduced in the BIOPRESS samples by (1) relying on an expert to select a superset of samples including Natura 2000 sites and (2) the availability of aerial photography. The project’s resources limited the total number of samples acquired. As a result some bio-geographical regions were under represented in the sample (Boreal and Mediterranean) whilst other regions were over represented (the Alpine, Atlantic, and Continental). So the development of an appropriate extrapolation approach was seen as a challenge from the beginning of the project. The key was to produce information which is useable in the data integration and which is meaningful, and reliable enough for use by our key stakeholder, the EEA. An extensive sensitivity analysis and the development of minimum land cover accordance maps have provided an excellent insight in the acquired land cover change data with respect to samples’ representativeness of biogeographical areas and land cover. Quality assurance and error propagation The following problems were identified as the main sources of possible mistakes and lack of correspondence in windows: • Ambiguity of CLC classes delineation. • Quality of B&W AP. • Availability of ancillary data. • Separation of CLC classes in B&W AP (E.g. burnt areas). • Diversity within class definition. • Occurrence of polygon less than 0.5 ha. • Amalgamation of objects less than 0.5 ha. • Real changes omitted. • Identification of questionable changes. • Identification of point and linear features, questionable, ambiguity and unknown relevance The quality of the input data was comparable for all transects, indicating that the comparability of results between partners and transects was unlikely to have been influenced by the quality of the input data. The date of the aerial photos (1950, 1990 or 2000) proved to have no influence on the thematic consistency of the interpretations whereas the level of thematic detail did have a high impact. The geometric accuracy was more difficult to evaluated, still we found that the controllers identified more spatial structures than the local interpreters. The quality of the interpretation depends on the land cover characteristics of the individual transects or windows. An error model was developed describing each step of the land cover change production chain. We found that most error sources which reduced the interpretation quality such as image quality, unclear class definition and confusion caused by lax use of land cover and land use attributes in the definitions, can be almost completely reduced by using modern data sources and adjusting the interpretation methodology. However, knowledge and experience of the interpreter play an important role in manual visual interpretation of remotely sensed data. Land cover change and pressures A land cover change - pressure association matrix was developed. This matrix enabled the grouping of types of land cover changes related to one of the six pressures under consideration in BIOPRESS: Urbanisation, intensification, afforestation, deforestation, abandonment and drainage. This cross-tabulation matrix is a fundamental starting point in the analysis of land cover change, because it provides a national-scale assessment of not only the losses or gains in the area of specific land categories but what these changes represent in terms of types of pressures However, additional research is needed to analyse this matrix according to its various components in order to gain more insight into the potential processes that determine a pattern of land cover change. It was impossible to derive a simple and practical list of indicators that would consistently explain the pressures on biodiversity. It was clear that there were multiple potential indicators, and the best indicators have not simply appeared out from the extensive data and information that already existed. In essence, the search for a coherent set of pressure indicators was a frustrating and time-consuming activity. It is clear from our effort that a single set of indicators would never explain the whole dynamics of anthropogenic pressures on biodiversity, and would go only some way towards meeting the needs for understanding the observed land cover changes. The main research challenge faced was to define a pattern-process model of land cover dynamics in space and time in order to combine the local level measurement of the land cover changes (e.g. BIOPRESS windows) and the socio-economic indicators of a larger region (e.g. the countries). The proposed multi-representation model is based on the degree of variability in the behaviour of generalised statistics and their dependency of the spatial generalisation of the variable values at different spatial scales. A systematic analysis of spatial coincidence between land cover types in CLC1990 and Annex1 habitat types recorded in Natura 2000 sites was carried out, translated into the EUNIS habitat classification and summarised per Biogeographical region. The work showed that a significant improvement could be made by adopting a regional approach providing neater and more specific links between CLC classes and habitats than what has been available so far. It also identifies what the limitations are in attributing habitat types to CLC classes. We found that the BIOPRESS land cover change product was suitable for quantifying some pressures on biodiversity but quite insufficient for the interpretation of land cover change related to other pressures: • BIOPRESS contributed very positively to the quantification of urbanization across Europe between 1950 and 1990/2000. • BIOPRESS land cover product made a useful contribution to the quantification of afforestation and deforestation across Europe between 1950 and 1990 but that these pressures could be better understood if (i) we had more points in time, closer together and (ii) more information on the condition of forest was derived from remote sensing and/or ancillary data was used to evaluate the ecological value of forested land. • BIOPRESS will have underestimated the extent to which the pressure land abandonment is threatening biodiversity in Europe, in comparison to other existing assessments (e.g. MIRABEL but also national scale statements). However, it would be possible to increase the accuracy and the generic value of the BIOPRESS estimates by (i) broadening the definition of land abandonment i.e. modifying the pressure matrix, so that it matches what is meant in other assessments and (ii) by increasing the number of points in time. • BIOPRESS was probably the first project to provide quantitative estimates about the shift from small scale to more large scale agriculture for such a large sample area across Europe and in this respect, this is a very important contribution to understanding changes in European biodiversity. However it is important to keep in mind that what has been quantified within BIOPRESS was only a small part of what is usually understood by farming intensification in biodiversity assessments. This means that, as was the case for land abandonment, BIOPRESS results will greatly underestimate the pressure farming intensification, compared to other assessments. The main conclusion is that remote sensing products such as the BIOPRESS land cover change product can provide very helpful information in the field of biodiversity assessment. There is potential for improving this information, e.g. by adding time steps in the monitoring or using external data to help in the interpretation of land cover change. However, our work also shows that there are clear limitations in this contribution and that remote sensing will only provide part of the information. One important recommendation that would lead to improve facilities for large scale biodiversity monitoring would be the integration of remote sensing products with in situ information. This recommendation forms the basis of a position paper produced jointly by BIOHAB (FP5 funded Concerted Action) and BIOPRESS. Socio-economic relevance and policy implications: The project supported the needs of DG-Environment and EEA in helping to implement and assess European policy on nature and biodiversity and contribute to the objective of enhancing the quality of the environment by helping to understand pressures on biodiversity arising from land cover change in the member states and accession countries. The state of the environment is perceived as an important indicator of a high quality of life by a majority of European citizens. The European public increasingly expresses the wish to be informed by policy on the perceived threats to biodiversity. BIOPRESS supported the development of a European capacity for monitoring the state of the environment (GMES) to meet these information needs. Conclusions: BIOPRESS was one of the first wave of thematic projects which were funded through the GMES initiative. As a result its main objective was to produce information at European level which in the case of BIOPRESS was information on historical land cover change for the purpose of assessing past pressures on habitats and their associated biodiversity. A large part of the project’s resources were used to deliver the land cover change database successfully and the outcome has not only been the delivery of data but also a set of tools for future European wide land cover monitoring. The real challenge was when trying to establish a link between land cover change and pressures on biodiversity. The development of the land cover change - pressure association matrix as a first step enabled the grouping of types of land cover changes related to one of the six pressures under consideration in BIOPRESS. This matrix has the potential to enhance the similar ‘Land Cover Flow’ matrix developed by the EEA as part of the EEA Land Accounting System. In theory the idea of integrating socio-economic data with land cover change data made sense but in practice the team struggled with the wide variety of data types, spatial and temporal resolutions. To assess the consequences of the observed Land cover changes on habitats and their biodiversity, BIOPRESS impact tables were developed using the same conceptual approach as that establish for the DPSIR assessment MIRABEL. The overall agreement between MIRABEL and the BIOPRESS tables, which unlike MIRABLE provided quantitative estimates for a selected sample of land in each region, was an important result. This part of the work concluded that a land cover change product such as that produced by BIOPRESS was suitable for quantifying some pressures on biodiversity but quite insufficient for the interpretation of land cover change related to other pressures. The error propagation, quality assessment and data search exercises highlighted the importance of the availability of good quality, affordable data (e.g. aerial photography, digital elevation data, social and economic indicators) which for long term monitoring should be continuously collected in a consistent manner