Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

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

The Earth Observation Data for Habitat Monitoring (EODHaM) system

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

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer 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, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme 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

A review of approaches for automated habitat mapping and their potential added value for biodiversity monitoring projects

Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program.</p

A Review of Approaches for Automated Habitat Mapping and their Potential Added Value for Biodiversity Monitoring Projects

Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program

Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982-2011)

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

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

nitoring 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

Environmental science: Agree on biodiversity metrics to track from space

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