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Over the past decades, remote sensing has been repeatedly identified as a promising and powerful tool to aid nature conservation. Many methods and applications of remote sensing to monitor biodiversity have indeed been published, and continue to be at an increasing rate. As such, remote sensing is seemingly living up to its expectations; yet, its actual use in nature conservation (or rather the lack thereof) contradicts this. We argue that, at least for the practical implementation of regular vegetation monitoring, including within protected areas (e.g., European Natura 2000 sites), a lack of transferability of remote sensing methods is an overlooked factor that hinders its effective operational use for nature conservation. Among the causes of poor method transferability is the large variation in objects of interest, user requirements, ground reference data, and image properties, but also the lack of consideration of transferability during method development. To stimulate the adoption of remote sensing based techniques in vegetation monitoring and conservation, we recommend that a number of actions are taken. We call upon remote sensing scientists and nature monitoring experts to specifically consider and demonstrate method transferability by using widely available image data, limiting ground reference data dependence, and making their preferably open-source programming code publicly available. Furthermore, we recommend that nature conservation specialists are open and realistic about potential outcomes by not expecting the replacement of current in-place methodologies, and actively contributing to the thought process of generating transferable and repeatable methods.We believe a new focus on method repeatability instead of novelty, would herald a mature era for remote sensing in nature conservation, in which remote sensing, through its operational use, would truly live up to its potential for nature conservation.

Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 20062007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa 0.820.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Monitoring global biodiversity is essential for understanding and countering its current loss. However, monitoring of many species is hindered by their difficult detection due to crepuscular activity, hidden phases of the life cycle, short activity period and low population density. Few statistical power analyses of declining trends have been published for terrestrial invertebrates. Consequently, no knowledge exists of the success rate of monitoring elusive invertebrates. Here data from monitoring transects of the European stag beetle, Lucanus cervus, is used to investigate whether the population trend of this elusive species can be adequately monitored. Data from studies in UK, Switzerland and Germany were compiled to parameterize a simulation model explaining the stag beetle abundance as a function of temperature and seasonality. A Monte-Carlo simulation was used to evaluate the effort needed to detect a population abundance decline of 1%/year over a period of 12 years. To reveal such a decline, at least 240 1-hour transect walks on 40 to 100 transects need to be implemented in weekly intervals during warm evenings. It is concluded that monitoring of stag beetles is feasible and the effort is not greater than that which has been found for other invertebrates. Based on this example, it is assumed that many other elusive species with similar life history traits can be monitored with moderate efforts. As saproxylic invertebrates account for a large share of the forest biodiversity, although many are elusive, it is proposed that at least some flagship species are included in monitoring programmes.

Efficient management and conservation of natural habitats requires a thorough knowledge and sustained monitoring of their ecological quality. In recent years, several methods have been developed to assess the local conservation status in the field. These typically combine estimates of coarse-scale indicators, such as tree and grass encroachments, with very fine-scale indicators that require precise fieldwork, such as the number of key species present. We first tested whether coarse-scale field characteristics can provide information on fine-scale indicators. Then, this idea was extended to remote sensing techniques to derive estimates of fine-scale properties that cannot be derived directly by the sensors. The method was elaborated for two Natura 2000 heathland areas, combining field conservation status assessments of over 650 locations with remote sensing information derived from an airborne hyperspectral scanner image. Boosted regression trees using field estimates of coarse-scale parameters as predictors were able to explain up to 43% of the variation in the fine-scale indicators. When using remote sensing data, models performed only slightly less. Up to 35% of the variation was explained using remote sensing estimates of coarse-scale parameters as predictors, and up to 39% was explained when additional remotely sensed land cover data were included in the models. Although these rates are not high in absolute terms, model predictions for certain parameters were more precise than field estimates, especially for criteria with a high between-observer variability. These results clearly illustrate the potential of remote sensing imagery to derive information on the conservation status of habitats, even for fine-scale elements that are too small to be derived directly.

A key success factor of the Copernicus programme is to ensure the acceptance of services by users. This acceptance and further adoption is based on high quality products that meet the specific information requirements of the user. In the realm of biodiversity monitoring and reporting on conservation status of Natura 2000 habitats, such products must meet different needs of local site manager and national environmental agencies, up to European authorities. Hence, the quality of these upcoming Earth observation based technologies must be validated in a pre-operational phase. The case of the MS.MONINA project shows the necessity to streamline the role of different stakeholders in the whole service delivery process. This process is described in depth, taken from the service validation in Sierra Nevada and Andalusia (Spain), showing potentials and limitations of the developed validation protocol and highlighting methodological, technical and scientific achievements obtained during this process.

The implementation of the Habitats Directive (Hab-Dir) in 1992 has been a major step towards a more harmonized approach to nature conservation in the European Union (EU). Member states granted legal protection to habitats and species listed by the HabDir, and designated areas of high nature value as Natura 2000 sites. But legal designation and protection is not enough: sites need to be managed appropriately, and the impacts of environmental pressures working on them require careful monitoring. Furthermore, member states need to monitor the conservation status of habitats and species on their entire territory, and report this to the EU on a six-yearly basis. These reports then serve as input for an assessment at the European scale, aiding the EU to follow up on achievements of the HabDir, and to adapt policy where needed. As a result, this shift towards a more systematic and knowledge-driven approach to biodiversity policy in the EU led to extensive and diverse data needs, at three different scale levels (EU, member state, and protected site) and involving many stakeholders. This can no longer be met by field work alone. Remote sensing image analysis has been demonstrated to be a powerful tool to assist in fulfilling the growing data needs, yet its use in operational Natura 2000 monitoring is still limited. We argue that a successful remote sensing based service for habitat monitoring in the Natura 2000 context should be: (1) multi-scale, (2) versatile, (3) user-friendly, and (4) cost-efficient. The recently started FP7-SPACE project MS. MONINA intends to set the basis for such a service, complying with pan-European efforts for data harmonization and exchange (GMES, INSPIRE, SEIS), and relying on a strong user involvement.

Monitoring and reporting on the state of nature gained increasing importance in the European Union with the implementation of the Habitats Directive and the Natura 2000 network. Reporting habitat conservation status requires detailed knowledge on many aspects of habitats at different spatial levels. Remote sensing is recognised as a powerful tool to acquire synoptic data on habitats, but to date, its use for Natura 2000 monitoring and reporting is still very limited. One reason for this appears to be the knowledge gap between the nature conservation agencies and the remote sensing community. We conducted a review of legal monitoring and reporting requirements on Natura 2000 habitats, looked into the current use of remote sensing in habitat reporting, and consulted monitoring experts in nature conservation administrations to find out about their attitude and expectations towards remote sensing. In this paper, we disclose and summarise the real data needs behind the legal requirements for Natura 2000 habitat monitoring and reporting, analyse opportunities and constraints for remote sensing, and highlight bottlenecks and pathways to resolve them. Monitoring experts are not unwilling to use remote sensing data, but they are unsure of whether remote sensing can suit their needs in a cost-effective way. They look upon remote sensing as a one-way process of data deliverance and fail to see the importance of their active cooperation. Based on our findings, we argue that the integration of remote sensing into Natura 2000 habitat monitoring could benefit from (1) harmonising and standardising approaches, (2) focusing on data at hand to develop readily useful products, (3) a proper validation of both traditional and remote sensing methods, and (4) an enhanced sharing and exchange of ideas and results between the different research communities involved.

Since 1992, European Member States are legally committed to monitor the biodiversity in designated areas of community interest. This was the success of the European Habitats Directive (92/43/EEC; HabDir) which established a network of these NATURA 2000 sites where trends in area and the quality of the protected habitats have to be observed. To fulfill the Directive\'s reporting requirements, Earth observation (EO) techniques are regarded as crucial to effectively map and monitor habitat status and change dynamics. The derived information supports public authorities in implementing management strategies The shift from focusing on single species to protecting entire habitats is a step towards sustaining the natural environment sensu nature conservation in several aspects: (1) habitats have a large umbrella function providing living space to whole ecosystems and (2) habitat protection is area-effective meaning that habitats have critical spatial parameters of their integrity (such as connectivity etc.), which can be assessed mapped and managed. That results in a significant share of a country\'s territory is under direct concern. The potential of new concepts and methods combing EO data and in-situ measurements is currently investigated in the GMES (Global Monitoring for Environment and Security) project MS.MONINA (www.ms-monina.eu) which aims at offering EO-based services to authorities on European, national and local level. The multiscale approach followed by the project reflects both the specific information requirements on different (political) implementation levels of the Directive and the ecological levels addressed, i.e. the level of single habitats, over habitat arrangements on site and range level, up to biogeographical regions. Local management of NATURA 2000 sites is required to regularly report on the conservation status including information on habitat range, area, quality, impacts and threats. Monitoring the changes also includes judging whether site conditions would improve or deteriorate. The latter triggers explicit management strategies in order to safeguard a favorable conservation status. Dependent on the occurring habitat types and pressures, specific information products are required from high-resolution satellite imagery. Such services shall be provided in a high operational level so to deliver the required information steadily and sound, but also user-focused. Several pilot sites all over Europe have been chosen in MS.MONINA to elaborate information products for different habitat types and user requirements. Heathlands, grasslands, estuaries, floodplains and riverine forests, as well as alpine meadows are among the habitats to be addressed. The anthropogenic impacts include farming, settlement land use, land abandonment and tourism. On the level of federal states or countries tools will be provided to monitor precious ecosystems outside the NATURA 2000 network of protected areas. On either level, services based on dedicated EO-based data helps foster a sustainable maintenance of precious ecosystems and reduce the loss of biodiversity.

Natura 2000 is an ecological network of protected areas in the territory of the European Union (EU). With the introduction of the Habitats Directive in 1992, EU member states are obligated to report every six years the status of the Natura 2000 habitats so that better conservation policy can be formulated. This paper examines the use of angular hyperspectral CHRIS/Proba image for the mapping of heathland at a Belgian Natura 2000 site. We find that the use of angular images increases the overall classification rate as compared to using only the nadir image; with the incorporation of angular images the final mapping is also more homogenous with less salt and pepper effect. While the class accuracy of Calluna- and Erica-dominated heathlands are still low, class accuracy of Molinia-dominated heathland is generally more encouraging. Two tree-based ensemble classifiers, Random Forest (RF) and Adaboost, were compared with Support Vector Machines (SVM). When only the nadir image was used, SVM attained the highest accuracy. When angular images were included, all three classifiers obtained comparable accuracies though in general RF and Adaboost had faster training time. We also adopted an assessment approach which repeats the accuracy assessment in ten independent trials, instead of the common practice of having only one trial. Our results show that accuracy attainment can vary significantly among different trials and hence it is recommendable to have more than one trial in order that a more objective characterization of the classifiers is obtained. 1

Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Ward's clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93% (Kappa=0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodology's potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.

Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types. First results from studies on heathland areas in Belgium and the Netherlands show that hyperspectral imagery can be an important source of information to assist the evaluation of the habitat conservation status. Hyperspectral imagery can provide continuous maps of habitat quality indicators (e.g., life forms or structure types, management activities, grass, shrub and tree encroachment) at the pixel level. At the same time, terrain managers, nature conservation agencies and national authorities responsible for the reporting to the EU are not directly interested in pixels, but rather in information at the level of vegetation patches, groups of patches or the protected site as a whole. Such local level information is needed for management purposes, e.g., exact location of patches of habitat types and the sizes and quality of these patches within a protected site. Site complexity determines not only the classification success of remote sensing imagery, but influences also the results of aggregation of information from the pixel to the site level. For all these reasons, it is important to identify and characterize the vegetation patches. This paper focuses on the use of segmentation techniques to identify relevant vegetation patches in combination with spectral mixture analysis of hyperspectral imagery from the Airborne Hyperspectral Scanner (AHS). Comparison with traditional vegetation maps shows that the habitat or vegetation patches can be identified by segmentation of hyperspectral imagery. This paper shows that spectral mixture analysis in combination with segmentation techniques on hyperspectral imagery can provide useful information on processes such as grass encroachment that determine the conservation status of Natura 2000 heathland areas to a large extent. A limitation is that both advanced remote sensing approaches and traditional field based vegetation surveys seem to cause over and underestimations of grass encroachment for specific categories, but the first provides a better basis for monitoring if specific species are not directly considered.