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Nature conservation policies require up-to-date and accurate biodiversity monitoring. Innovative synoptic information products such as Remote Sensing-enabled Essential Biodiversity Variables (RS-enabled EBVs) could complement field observations in biodiversity monitoring. It is not clear however, how these scientific remote sensing products can be utilized for policy reporting. Agreement on the monitored geographic extent (area size and scale), as well as biodiversity attributes (composition, structure, and function), may provide a common'point of departure' for policymakers and the scientific community to develop and further improve monitoring. In this study, biodiversity indicators of 10 nature conservation policies and 50 RS-enabled EBVs were compared using non-parametric tests (chi-square and Mann-Whitney U). Our main finding is that policy indicators and RS-enabled EBVs are very similar in the spatial extent they address (mapping scale). However, most policy indicators are related to ecosystem structure while most of the RS-enabled EBVs are related to ecosystem function and ecosystem structure. RS-enabled EBVs have added value in monitoring of biodiversity, especially when looking at ecosystem functioning. Information on ecosystem functioning and structure provides evidence needed as input for policy development and management of biodiversity. However, to make this happen, a stronger focus on ecosystem functioning and structure with appropriate variables is needed, in policy requirements and targets.

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowl- edge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.

In the version of this Perspective initially published, there was an error in units reported in the main text. Specifically, in the first sentence of the sixth paragraph under the heading “A critical review of EBVs retrieved by remote sensing,” in the text now reading “Finally, when harmonizing the terminology used by ecological and remote sensing communities, it is important to emphasize that utilizing broadband optical wavelengths (for example, for PlanetScope, approximately 400–700 nm) at very high spatial resolution,” 400–700 nm originally appeared as “60–90 nm.” The error has been corrected in the online version of the article.

Regular mapping of vegetation structure is important for biodiversity monitoring, and increasingly for tracking compliance with nature policy mandates. As such, the Netherlands uses vegetation structure mapping to monitor the Natura 2000 site on the Dutch Wadden Sea island of Ameland. Three decades of natural gas extraction here has caused soil subsidence, impacting vegetation and habitats on the island. In the Netherlands, vegetation structure mapping is typically done using conventional techniques, primarily field surveys combined with visual interpretation of aerial photographs. This procedure is time-consuming and often too inconsistent and inefficient for large areas. In the current study we exploited commonly shared and low-cost aerial photographs and LiDAR data for detailed vegetation structure mapping. Aerial photographs are not always easy to use in automatic classification procedures, as they often lack calibrated spectral reflectance values. Furthermore, pre-processing of aerial photographs to render them more attractive may skew the image so that it no longer accurately depicts the original scene anymore. Our aim was to determine if automatic or semi-automatic classification techniques could be applied to these readily available Dutch data to support mapping and monitoring of the vegetation structure of larger areas. We compared the effectiveness of two well-known classification methods, namely rule-based (RB) and random forest (RF). The RF algorithm was applied with its default settings, as supplied by eCognition software. Both classification methods performed well, with overall accuracies of 84.1% (RB) and 86.4% (RF). Each method, however, has its advantages and disadvantages, which are discussed. Overall, RF classification was preferred over RB classification, as it was better able to handle the complexity of the rules needed for distinguishing more classes. Provision of in situ training data, such as vegetation relevés, was not really problem in the Dutch context. Nevertheless, exploitation of new digital aerial photographs produced each year in a semi-automatic process remains a challenge. Commercial high resolution satellite imagery (~0.5 m resolution) is therefore still preferred by us. This latter, unfortunately, is more costly than aerial photographs which, while not always ideal, are readily available at no additional costs for involved organisations

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods. Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest. We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective. Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.

In the version of this Perspective originally published, the names of authors Pedro J. Leitão and Ilse Geijzendorffer were mistakenly written as Pedro J. Leitãu and Ilse Geizendorffer, respectively. These have now been corrected.

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst 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 results for 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 land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall 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 unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

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.

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.

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/ semi-automatic translation framework of LC/ LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed.

Ecologists and space agencies must forge a global monitoring strategy, say Andrew K. Skidmore, Nathalie Pettorelli and colleagues.

Monitoring of habitat types protected under the Annex I of the EU Habitats Directive requires every 6 years information to be reported on their conservation status (area, range, structure and function) in the member states. Hyperspectral imagery can be an important source of information to assist in the evaluation of the habitats’ conservation status, as it can provide continuous maps of habitat quality indicators (e.g., life forms, management activities, grass, shrub and tree encroachment) at the pixel level. Such local level information is highly needed for management purposes, e.g., the location of habitat patches and their sizes and quality within a protected site. This paper focuses on the use of continuous fraction images as derived from spectral mixture analysis of hyperspectral imagery (AHS-160), in combination with segmentation techniques, to facilitate habitat quality assessment in a heathland site in the Netherlands. This combined application of techniques on hyperspectral imagery demonstrates the usefulness of information from continuous fraction maps of grass abundance (Molinia caerulea) in heathlands – at and within the patch level – compared to traditional mapping techniques that assess grass encroachment in a limited number of abundance classes at the patch level. It therefore provides a better basis to monitor large areas for processes such as grass encroachment that largely determine the conservation status of Natura 2000 heathland areas. Timely, accurate and up-to-date spatial information on the encroachment of mosses, grasses, shrubs or trees (dominant species) can help conservation managers to take better decisions and to better evaluate the effect of taken measures. While discrepancies exist between the results of field-based vegetation surveys and the proposed remote sensing approach, we provide a discussion on the uncertainty of determining which of both methods is most accurate in relation to dominant species, which is in our case Molinia caerulea, and set forth several reasons why the remote sensing based approach might form a better basis for the monitoring of abundant species and patch evolution through time.

Understanding which environmental conditions are critical for species survival is a critical, ongoing question in ecology. These conditions can range from climate, at the broadest scale, through to elevation and other local landscape conditions, to fine scale landscape patterns of land cover and use. Remote sensing is an ideal technology to monitor and assess changes in these environmental conditions at a variety of spatial and temporal scales, with many studies focusing on the physiological state of vegetation derived from time series of satellite measurements. As vegetation occurs within specific climatic zones, over certain soil, terrain, and land cover types, it can be difficult to decipher the influence of the underlying role of climate, topography, soil, and land cover on the observed vegetation signal. In this article, we specifically addressed this problem by asking the question: what is the relative impact and importance of these different scales of environmental drivers on the temporal and spatial patterns observed on a habitat index derived from remotely sensed data? To find the solution, we utilized a SPOT VEGETATION-normalized difference vegetation index time series of Europe to create a remote-sensing-derived habitat index, which incorporates aspects of productivity, seasonality, and cover. We then compared the observed temporal and spatial variations in the index to a pan-Europe terrestrial classification system, which explicitly incorporates variations in climate, terrain, soil parent material, land cover, and use. Results indicated that the most accurate level of discrimination from the habitat index was at the broadest level of the hierarchy, climate, while the poorest degree of discrimination was associated with elevation. In terms of similarity on the index across time and space, we found that arable and forest cover classes were more similar across elevation and parent materials than across other land cover types within them. Analyzing the remote-sensing index, at multiple scales, provides significant insights into the drivers of satellite-derived greenness indices, as well as highlights the benefit and cautions associated with linking satellite-derived indirect indicators to species distribution modeling and biodiversity.

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth Observation (EO) data offers a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of Land Cover (LC) and/or Land Use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies - CORINE Land Cover (CLC), the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) and the International Geosphere-Biosphere Programme (IGBP) to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in-situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring - a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result Key words: Mapping; land cover; land use; habitat; earth observation; taxonomies; Natura 2000; classification schemes

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.

In this paper we describe how historical land use information has been derived for the whole of Europe, using the World Atlas of Agriculture, scale 1: 2,500,000. This paper describes the process of converting the analog land-use maps to a digital European historical land-use database, the Historical Land Use Database 1960 (HISLU60). The processing techniques that are applied in this process are commonly used for the processing of satellite imagery. The paper maps were scanned and geo-referenced. Image filtering techniques were used to eliminate the cartographic elements like text and boundaries. Land-use classes were created from the colours in the maps using image classification techniques. As a last step, GIS filtering functions were used to eliminate remaining cartographic elements and small classification errors. The HISLU60 database contains six land-use classes; arable land, grassland, forest, non-agricultural land, inland water and urban. It is a raster dataset with a cell size of 250 meters. The HISLU60 database was validated by performing a statistical comparison with three reference datasets. The overall classification accuracy is around 50 percent. Despite its limitations, the HISLU60 database gives an overview of the pan-European land use around 1960.

The spatial configuration of agricultural systems is continuously changing in response to changes in demand for agricultural goods, changes in the level of competition between different land use activities, and progress in agricultural technology. This may lead to a change in the location of agricultural systems and consequently to a change in their average environmental characteristics. This paper explores the change in environmental characteristics of cropland (horticulture and field crops) over the years 1950, 1990 and 2000, for Western and Eastern Europe, using basic descriptive statistics. Underlying mechanisms are explored with logistic (interaction) regression analysis. We find that in both Eastern and Western Europe, crop cultivation shifted away from cities. In Western Europe cropland became situated on shallower soils, steeper slopes, and drier and less accessible areas. Probable reasons are that technical progress reduced the importance of traditional constraints such as drought, poor soils, and distance from markets, so that crop farmers were allowed to move to warm and sunny areas where potential productivity is highest. In addition, cropland probably lost some of its competitive power to grassland and nature. In Eastern Europe cropland concentrated on deeper soils and flatter terrain from 1990 onward. Here, the abandonment of the central planning system and a more flexible land market must have allowed a shift of cropland towards more suitable locations.

International audience; BIOPRESS (Linking Pan-European land cover change to pressures on Biodiversity'), a European Commission funded Global Monitoring for Environment and Security' project, produced land cover change information (19502000) for Europe from aerial photographs and tested if this information is suitable for monitoring habitats and biodiversity. The methods and results related to the land cover change work are summarised. Changes in land cover were established through 73 window and 59 transect samples distributed across Europe. Although the sample size was too small and biased to represent the spatial variability observed in Europe, the work highlighted the importance of method consistency, the choice of nomenclature and spatial scale. The results suggest different processes are taking place in different parts of Europe: the Boreal and Alpine regions are dominated by forest management; abandonment and intensification are mainly encountered in the Mediterranean; urbanisation and drainage are more characteristic of the Continental and Atlantic regions.

Vegetation height is a crucial factor in environmental studies, landscape analysis, and mapping applications. Its estimation may prove cost and resource demanding, e.g., employing light detection and ranging (LiDAR) data. This study presents a cost-effective framework for height estimation, built around texture analysis of a single very high-resolution passive satellite sensor image. A number of texture features are proposed, based on local variance, entropy, and binary patterns. Their potential in discriminating among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m) is tested in an area with heath, tree, and shrub vegetation. A number of missing data handling, outlier removal, and data normalization methods are evaluated to enhance the proposed framework. Its performance is tested with different classifiers, including single and ensemble tree ones and support vector machines. Furthermore, dimensionality reduction (DR) is applied to the full feature set (192 features), through both data transformation and filter feature selection methods. The proposed approach was tested in two WorldView-2 images, representing the peak and the decline of the vegetative period. Vegetation height categories were accurately distinguished, reaching accuracies of over 90% for six height classes, using the images either individually or in synergy. DR achieved similarly high, or higher, accuracies with even a 3% feature subset, increasing the processing efficiency of the framework, and favoring its use in height estimation applications not requiring particularly high spatial resolution data, as a cost-effective surrogate of more expensive and resource demanding approaches.

Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.

Stratifications are made to divide environmental gradients into convenient units and then to use these as areas in which objects and variables might have relatively consistent characteristics. Statistical classification is a useful approach for obtaining this insight into complex environmental patterns and help to simplify heterogeneity. Traditional classifications of the environment are mostly subjective and based on expert knowledge. They are largely intended for descriptive purposes. Present day techniques now allow for continent wide statistically based environmental stratifications that can be applied consistently throughout Europe. Such environmental stratifications can provide the basis for assessment and monitoring biodiversity, land cover and land use and be a starting point for reporting on the European environment. The stratification presented here allows upscaling and downscaling, if needed to reach specified objectives. It can be applied in environmental reporting. Its application as a framework for land cover estimation is elaborated using Portuguese Land cover data.

Aim To produce a statistical stratification of the European environment, suitable for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent, and to provide strata for modelling exercises and reporting. Location A ‘Greater European Window’ with the following boundaries: 11 ° W, 32 ° E, 34 ° N, 72 ° N. Methods Tw enty of the most relevant available environmental variables were selected, based on experience from previous studies. Principal components analysis (PCA) was used to explain 88% of the variation into three dimensions, which were subsequently clustered using an ISODATA clustering routine. The mean first principal component values of the classification variables were used to aggregate the strata into Environmental Zones and to provide a basis for consistent nomenclature. Results The Environmental Stratification of Europe (EnS) consists of 84 strata, which have been aggregated into 13 Environmental Zones. The stratification has a 1 km 2 resolution. Aggregations of the strata have been compared to other European classifications using the Kappa statistic, and show ‘good’ comparisons. The individual strata have been described using data from available environmental databases. The EnS is available for noncommercial use by applying to the corresponding author. Main conclusions The Environmental Stratification of Europe has been constructed using tried and tested statistical procedures. It forms an appropriate stratification for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent and for the provision of strata for modelling exercises and reporting at the European scale.

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.

Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere-atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan-European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape-based aggregation scheme. We used indicators of Start-Of-Season, End-Of-Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982-2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18-24 days decade(-1) over 18-30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing-season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI-derived end-of-season contributed more to the GSL trend than changes in spring green-up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change.

At the local spatial scale, land-use variables are often employed as predictors for ecological niche models (ENMs). Remote sensing can provide additional synoptic information describing vegetation structure in detail. However, there is limited knowledge on which environmental variables and how many of them should be used to calibrate ENMs. We used an information-theoretic approach to compare the performance of ENMs using different sets of predictors: (1) a full set of land-cover variables (seven, obtained from the LGN6 Dutch National Land Use Database); (2) a reduced set of land-cover variables (three); (3) remotely sensed laser data optimized to measure vegetation structure and canopy height (LiDAR, light detection and ranging); and (4) combinations of land cover and LiDAR. ENMs were built for a set of bird species in the Veluwe Natura 2000 site (the Netherlands); for each species, 26–214 records were available from standardized monitoring. Models were built using MaxEnt, and the best performing models were identified using the Akaike’s information criterion corrected for small sample size (AICc). For 78% of the bird species analysed, LiDAR data were included in the best AICc model. The model including LiDAR only was the best performing one in most cases, followed by the model including a reduced set of land-use variables. Models including many land-use variables tended to have limited support. The number of variables included in the best model increased for species with more presence records. For all species with 33 records or less, the best model included LiDAR only. Models with many land-use variables were only selected for species with >150 records. Test area under the curve (AUC) scores ranged between 0.72 and 0.92. Remote sensing data can thus provide regional information useful for modelling at the local and landscape scale, particularly when presence records are limited. ENMs can be optimized through the selection of the number and identity of environmental predictors. Few variables can be sufficient if presence records are limited in number. Synoptic remote sensing data provide a good measure of vegetation structure and may allow a better representation of the available habitat, being extremely useful in this case. Conversely, a larger number of predictors, including land-use variables, can be useful if a large number of presence records are available.

Linear landscape elements, such as ditches, hedgerows, lines of trees and field margins, provide important habitats and ecosystem services and function as ecological infrastructure for species within agricultural landscapes. Spatial maps of the distribution of these elements are needed to better represent landscape structure within regional scale environmental assessments. We present wall-to-wall maps for green lines, ditches and grass margins for Europe, using spatial modelling of ground observations on linear features from the 2009 LUCAS (land use/cover area frame statistical survey) database. We compare different spatial interpolation methods, ranging from spatial autocorrelation-based methods to methods that explain the occurrence of elements based on biophysical and socio-economic information. Our results are 1 km2 resolution maps of the occurrence of linear landscape elements for Europe. Independent validation of green lines based on aerial photographs showed the best results for interpolation based on regionally estimated regressions relating the occurrence of landscape elements to environmental and socio-economic location factors. The results confirm the importance of the underlying biophysical and socio-economic factors on the presence and abundance of linear landscape elements. However, the total explanatory strength of the considered factors is moderate and a considerable uncertainty in the exact distribution remains.

The first section of the paper discusses the background to the use of habitats as indicators for biodiversity including a discussion of the range of definitions that have been used. Habitats can now be recorded consistently across Europe at different time intervals in order to estimate stock and change as an indicator of biodiversity that is efficient and relatively easy to record. Habitats are considered to be the third level in a hierarchy with biomes and landscapes as higher categories and vegetation, species and genetic diversity as lower levels. An advantage of using habitats is that many other taxa are associated with them and examples are given from the literature. Examples are also given of the association between habitats and species in European Environmental Zones using expert judgement. Statistical analysis using a range of procedures can also be used to assess the association between species and habitats. Reliable data on the extent, status and changes in European habitats is essential for policy makers across the European Union and would also be important for promoting species conservation.

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.

Monitoring the conservation status of natural habitats is an essential aspect of effective conservation management. Not only data on habitat occurrence are needed, but also detailed information on the structural and functional characteristics of the habitat patches is crucial for an adequate conservation status assessment. Classification of hyperspectral remote sensing images performs well in discriminating dominant land cover and vegetation classes, but the accuracy drops significantly for the classification of more subtle differences in conservation status that are related to structural characteristics. This study proposes a method to facilitate ecological conservation status assessment based on decision tree modeling of subpixel fraction estimates steered by ecological expert knowledge. In particular, it contributes to the spatially explicit assessment of an important structural aspect of dry heathland vegetation, namely the heather age structure, using Airborne Hyperspectral line-Scanner radiometer (AHS-160) data of the Kalmthoutse Heide in northern Belgium. We implemented a subpixel unmixing approach to identify the percentage of heather, sand and shadow in each heather pixel, and subsequently applied a decision tree classification to allocate each pixel to a certain age class. As such, our method provides a tool that contributes to the information required for an appropriate management and successful conservation of natural heathlands.

This chapter concludes that, in combination with additional environmental data sets, it is now possible to model quantitatively the spatial extent of widespread habitats and landscapes on the basis of land cover information derived from satellite imagery. Although it is now possible to model the spatial extent of widespread European habitats, these patterns cannot be directly translated into area estimates. The retrieval of accurate land cover information is not only crucial for the spatial modelling of European landscapes and habitats, but also for their monitoring. Operational remote sensing enables land cover characterization at various scales but the classification accuracies are still insufficient at continental and global scales for monitoring purposes. Instead, the use of continuous thematic fraction layers, as derived from linear unmixing, provides a good basis for monitoring land cover changes of Europe’s complex landscapes. However, gradual and small changes in habitats and their quality are not easily detected from space by satellite imagery, and therefore, additional information from field surveys is needed. Protocols for rapid field surveying of habitats have been developed that can provide a European baseline based on a sampling design across European landscapes. The information from the field samples (e.g. square kilometres) can be used for the validation and calibration of the obtained distribution maps of European habitats. The field surveying method is amongst others based on the estimation of the main plant life forms, which are highly correlated with vegetation structure. The latter has been shown to have a good relationship with satellite imagery. Field surveys are always limited to relatively small areas in Europe, and therefore, the spatial modelling of habitats and landscapes with the help of remotely sensed information remains important for providing a synoptic overview.

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.

A range of new spatial datasets classifying the European environment has been constructed over the last few years. These datasets share the common objective of dividing European environmental gradients into convenient units, within which objects and variables of interest have relatively homogeneous characteristics. The stratifications and typologies can be used as a basis for up-scaling, for stratified random sampling of ecological resources, for the representative selection of sites for studies across the continent and for the provision of frameworks for modeling exercises and reporting at the European scale. This paper provides an overview of five recent European stratifications and typologies, constructed for contrasting objectives, and differing in spatial and thematic detail. These datasets are: the Environmental Stratification (EnS), the European Landscape Classification (LANMAP), the Spatial Regional Reference Framework (SRRF), the Agri-Environmental Zonation (SEAMzones), and the Foresight Analysis for Rural Areas Of Europe (FARO-EU) Rural Typology. For each classification the objective, background, and construction of the dataset are described, followed by a discussion of its robustness. Finally, applications of each dataset are summarized. The five stratifications and typologies presented here give an overview of different research objectives for constructing such classifications. In addition they illustrate the most up to date methods for classifying the European environment, including their limitations and challenges. As such, they provide a sound basis for describing the factors affecting the robustness of such datasets. The latter is especially relevant, since there is likely to be further interest in European environmental assessment. In addition, advances in data availability and analysis techniques, will probably lead to the construction of other typologies in the future.

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 limitations

Natura 2000, an EU-wide network of nature protection areas, has as main objective the achievement or maintenance of a favorable conservation status of habitats protected by the EU Habitats directives. Within this framework, this study examines a strategy to characterize the status of heathland vegetation from airborne hyperspectral AHS data in the Kalmthoutse Heide, Flanders, Belgium. A hierarchical classification scheme was set-up with the highest detail focusing on vegetation structural elements that determine the conservation status of the habitat. Although conventional classification algorithms performed very well (accuracies > 90%) in discriminating broad land cover classes and habitat types (level 1 to 3), they failed in accurately distinguishing different heather age classes which are an important indicator for the structural quality of the heathland habitat (level 4). Since all heather life stages have their specific structural characteristics, a subpixel unmixing approach succeeded by a decision tree classification was implemented to map variations in heathland morphology and as such enhance the ecological value of information derived from remote sensing data.

Both science and policy require a practical, transmissible, and reproducible procedure for surveillance and monitoring of European habitats, which can produce statistics integrated at the landscape level. Over the last 30 years, landscape ecology has developed rapidly, and many studies now require spatial data on habitats. Without rigorous rules, changes from baseline records cannot be separated reliably from background noise. A procedure is described that satisfies these requirements and can provide consistent data for Europe, to support a range of policy initiatives and scientific projects. The methodology is based on classical plant life forms, used in biogeography since the nineteenth century, and on their statistical correlation with the primary environmental gradient. Further categories can therefore be identified for other continents to assist large scale comparisons and modelling. The model has been validated statistically and the recording procedure tested in the field throughout Europe. A total of 130 General Habitat Categories (GHCs) is defined. These are enhanced by recording environmental, site and management qualifiers to enable flexible database interrogation. The same categories are applied to areal, linear and point features to assist recording and subsequent interpretation at the landscape level. The distribution and change of landscape ecological parameters, such as connectivity and fragmentation, can then be derived and their significance interpreted.

This paper describes the results of a feasibility study to test the usefulness of MERIS for land cover mapping. The Netherlands was used as a test site because of its highly fragmented landscape. Results showed that the geometric and radiometric properties of the studied MERIS images of the Netherlands are suitable for land applications. Calculation of principal components and correlation coefficients revealed that the 15 MERIS bands provided a lot of redundant spectral information. For land applications, information came from the visible part of the spectrum on the one hand and from the near-infrared part on the other hand. In addition, the red-edge slope of the reflectance curve (in particular MERIS band 9 at about 708 nm) provided supplementary information. The Dutch land use database LGN5 was used as a reference for classifications in this study after aggregation from 25 m to 300 m and recoding to 7 relevant land cover classes. For land cover classification best results in terms of classification accuracies were obtained for the image of 14 July 2003. For the seven land cover classes selected the overall classification accuracy was 67.2%. A multitemporal classification did not improve the overall classification accuracy.

Environmental studies need up-to-date and reliable information on land use and land cover. Such databases, which can be characterized by a high spatial accuracy and that can be updated easily, are currently not available for Europe as a whole. We investigated the applicability of satellite data for Pan-European Land Cover Monitoring (PELCOM). The main objective was to develop a method by which to obtain a 1 km spatial resolution pan-European land cover database that can be updated easily using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) satellite data. The database will be used as input for environmental impact studies and climate research. The study takes full advantage of both multi-spectral and multi-temporal 1 km AVHRR data. The proposed methodology for land cover mapping has its limitations in monitoring changes due to the spatial resolution and the limited accuracy of AVHRR-derived land cover data. Therefore, a change detection technique base...

That the relationship between remote sensing and landscape ecology is significant is due in large part to the strong spatial component within landscape ecology. However it is nevertheless necessary to have frequent overview of the interface between remote sensing and landscape ecology, particularly in the light of developments in the types of image data and techniques. The use of remote sensing within European landscape ecology provides a rich range of examples of the interface, including application of some of the latest types of image data. This paper is an overview of the interface that remote sensing has with European landscape ecology, with seven examples of the application of image data in European landscape ecology and examination of associated landscape classification issues. These examples are discussed in terms of the trends and the different roles for image data in landscape ecology that they illustrate, and in particular their classificatory and informational implications. It is suggested that with regard to classification there is a need for re-examination of the roles of image data.