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 123Landscape Ecol (2013) 28:905-930 DOI 10.1007 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.

cited By 19Monitoring 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.Peer reviewe

In the Mediterranean Region, habitat loss and fragmentation severely affect coastal wetlands, due to the rapid expansion of anthropogenic activities that has occurred in the last decades. Landscape metrics are commonly used to define landscape patterns and to evaluate fragmentation processes. This investigation focuses on the performance of a set of landscape pattern indices within landscapes characterized by coastal environments and extent below 1,000 ha. The aim is to assess the degree of habitat fragmentation for the monitoring of protected areas and to learn whether values of landscape metrics can characterize fine-resolution landscape patterns. The study areas are three coastal wetlands belonging to the Natura 2000 network and sited on the Adriatic side of Apulia (Southern Italy). The Habitat Maps were derived from the Vegetation Maps generated integrating phytosociological relevés and Earth Observation data. In the three sites, a total of 16 habitat types were detected. A selected set of landscape metrics was applied in order to investigate their performance in assessing fragmentation and spatial patterns of habitats. The final results showed that the most significant landscape patterns are related to highly specialized habitat types closely linked to coastal environments. In interpreting the landscape patterns of these highly specialized habitats, some specific ecological factors were taken into account. The shape indices were the most useful in assessing the degree of fragmentation of habitat types that usually have elongated morphology along the shoreline or the coastal lagoons. In all the cases, to be meaningful, data obtained from the application of the selected indices were jointly assessed, especially at the class level.

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

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In this paper the results of a study on the composition and the distribution of the plant communities in three coastal areas of southern Apulia are presented. A total of about 180 vegetation relevés were performed following the BraunBlanquet phytosociological method. Vegetation data were analysed using both classification (UPGMA, similarity ratio) and ordination methods (including Non-metric Multidimensional Scaling (NMS) and Detrended Correspondence Analysis (DCA). The relevés are distributed in the following classes: Molinio-Arrhenateretea, Phragmito-Magnocaricetea, Juncetea maritimi, Sarcocornietea fruticosae, Saginetea maritimae, Thero-Salicornietea, Helianthemetea guttati. Detailed information about structure and zoning of the detected plant communities are here provided. Two new associations, belonging to the Alkanno-Maresion nanae alliance (microphytic ephemeral plant communities growing on sandy soils, Helianthemetea guttati class) have been described here, both in the "Torre Guaceto" site. The area of "Le Cesine" showed the highest total number of plant communities, while the "Saline di Punta della Contessa" site revealed the largest number of Sarcocornietea fruticosae plant communities.

The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty.

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1.