1,721,004 research outputs found
Habitat mapping and change detection in Natura2000 coastal sites in Southern Apulia
No full textMonitoring biodiversity at habitat and landscape level is becoming widespread in Europe and elsewhere as countries establish national and international habitat conservation policies and monitoring systems. Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies. Earth observation (EO) data and techniques are a valuable resource for long-term habitat mapping, through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. The Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) has been identified as the most effective for translating EO-derived LC/LU classes to habitat types, since it allows a better description of natural habitats in comparison to other classification systems; moreover, LCCS has proven to be a effective tool in change detection, both at the level of conversion and modification (Tomaselli et al., 2013; Adamo et al 2014). As regards the present contribution, vegetation, LC and habitat mapping has been performed on three coastal sites belonging to the Natura 2000 and located in Southern Apulia (Italy), in years 2007 and 2015. Vegetation maps represented the baseline position for natural and semi-natural types, defined as phytosociological units in accordance with the Zurich-Montpellier method. Vegetation units were then reclassified in habitat types (according to the Annex I to the 92/43 EEC Directive and EUNIS) and in LC classes (according to Corine Land Cover and LCCS). The adopted landscape classification procedure refers to a hierarchical model with three different information levels: the vegetation unit, the habitat type, and the LC type. The mapping products were then compared, in the different acquisitions, in order to point out the ability of different taxonomies in detecting changes in vegetation and habitat types. LCCS turned out to be the most effective, highlighting changes such as height, structure and density, which were not evidenced with other classification systems
How does the selection of landscape classification schemes affect the spatial pattern of natural landscapes? An assessment on a coastal wetland site in southern Italy
No full textIt is widely known that thematic resolution affects spatial pattern and landscape metrics performances. In literature, data dealing with this issue usually refer to a specific class scheme with its thematic levels. In this paper, the effects of different land cover (LC) and habitat classification schemes on the spatial pattern of a coastal landscape were compared. One of the largest components of the Mediterranean wetland system was considered as the study site, and different schemes widely used in the EU were selected and harmonized with a common thematic resolution, suitable for habitat discrimination and monitoring. For each scheme, a thematic map was produced and, for each map, 28 landscape metrics were calculated. The landscape composition, already in terms of number of classes, class area, and number of patches, changes significantly among different classification schemes. Landscape complexity varies according to the class scheme considered and its underlying semantics, depending on how the different types aggregate or split when changing class scheme. Results confirm that the selection of a specific class scheme affects the spatial pattern of the derived landscapes and consequently the landscape metrics, especially at class level. Moreover, among the classification schemes considered, EUNIS seems to be the best choice for a comprehensive representation of both natural and anthropogenic classes
Assessing the spatial complexity in a coastal wetland site (Southern Italy) according to different habitat and land cover classification schemes
No full textAim: Object of this work was to compare different habitat and land cover classification schemes, applied to a specific coastal wetland landscape, with a defined thematic resolution level. Location and Methods: Study area is the SCI IT9110005, one of the most extensive wetlands of the Italian peninsula and one of the largest components of the Mediterranean wetland system, located in the Northeastern part of the Puglia Region (Southern Italy). The natural vegetation is represented mostly by halophytic scrub, reed thickets and by annual pioneer salt marsh communities. Natural and semi-natural landscape elements were described as phytosociological units and represented on a vegetation map at a 1:5,000 scale. Vegetation units were then reclassified in habitat types according to Annex I of the EEC 92/43 Directive and EUNIS habitat classification schemes and in land cover types according to different land cover schemes. For each scheme a thematic map was produced and, for each map, various landscape metrics were calculated. Results and Conclusions: The selection of a specific class scheme affects the spatial pattern of the derived landscapes and consequently the landscape metrics, especially at class level. The presence of various vegetation types and mosaics increases the complexity of the spatial pattern, which varies greatly according to the classification system considered, based on how the different types are aggregated. Our results confirm that the choice of specific classification schemes produces important effects on the spatial composition of the derived patch-mosaic landscape, and therefore can significantly affect the derived landscape metrics values
Mapping and monitoring in protected natural areas: the use of the FAO LCCS as an effective tool for habitat mapping and change detection
No full textEffective and timely biodiversity monitoring within protected sites and their surroundings is critical for detecting landscape changes which might impact sites conservation status, quality and resources and to evaluate the effectiveness of conservation policies in protecting biodiversity and ecosystems from human activities. The most commonly used Land Cover/Land Use (LC/LU) or habitat classification systems are limited in their ability to read all aspects of the landscape. The Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy (Di Gregorio and Jansen, 2005) was identified as the most appropriate for providing a common language for harmonizing different LC/LU legends. One of the basic principles of this system is that a given land-cover class is defined by a dynamic combination of classifiers, thus allowing the more complex semantics of each land-cover class may be described. FAO/LCCS has been also found to be effective for translating EO-derived LC/LU classes to habitat types (Tomaselli et al., 2013; Adamo et al 2014), since it allows a better description of natural habitats in comparison to other classification systems. Furthermore, LCCS has proven to be a valid tool in change detection, both at the level of conversion and modification. In fact, changes become immediately identifiable by a difference in classifier, or through the use of additional classifiers, although maintaining the same class type. In this contribution LC and habitat mapping have been performed on a site belonging to the Natura 2000 and located in Southern Apulia (Italy), characterized by coastal environments, Mediterranean maquis and extensive pine forests. The mapping was performed by means of photo interpretation and on-site survey, in years 2007 and 2015. Different LC and habitat classification systems were used and results compared. The LCCS turned out to be the most effective in detecting changes in forest types, highlighting changes such as height and density which were not evidenced with other classification systems
Habitat monitoring in coastal landscapes: the use of vegetation pattern information for habitat discrimination in satellite images classification
No full textCoastal environments are among the most threatened worldwide, undergoing numerous human-induced and natural pressures resulting in habitat loss, alteration and fragmentation. Conservation of coastal environments is one of the major concerns of the European Union Biodiversity Strategy to 2020, and periodic monitoring of biodiversity changes at different scales constitutes a key issue to adopt adequate conservation policies. Structure, composition and spatial pattern of coastal plant communities may be extensively surveyed by means of in situ methodologies. The integration of in situ data (vegetation) with earth observation (EO) data offer a significant enhancement, through direct or indirect mapping of habitats at different spatial and temporal scales. In the framework of the BIO_SOS project, funded within the European Union FP7-SPACE third call (www.biosos.eu), a pre-operational system for periodic monitoring of changes in land cover and habitats within Natura 2000 sites was developed. The FAO - Land Cover Classification System (LCCS) was considered as the most appropriate Land Cover/Use (LC/LU) taxonomy for habitat mapping, since in situ expert knowledge can be easily embedded in such a framework (Tomaselli et al., 2013). Besides a wide and in depth documentation on omposition, structure and ecology of plant communities, the effectiveness of information related to the vegetation pattern (zonation) has been also explored to enhance the habitat discrimination process. As case study, two coastal Natura 2000 sites located on the Adriatic side of the Puglia region were selected: "Le Cesine" and "Zone umide della Capitanata e Paludi presso il Golfo di Manfredonia". LC/LU maps were produced directly in LCCS taxonomy (scale 1:5000) on the basis of pre-existing information. In order to investigate the application of topological rules based on vegetation pattern for LC/LU to habitats translation, available literature data on ecological gradients and vegetation pattern relationships in Mediterranean coastal environments were examined. Then, in order to validate the feasibility of the rules in the study sites, eight vegetation transects (with regular vegetation plots) were carried out and vegetation composition and structure were surveyed in each plot. Data were analyzed and grouped in plant communities using multivariate analysis. Plant communities were related to syntaxa and then to habitat types according to Annex I (92/43/EEC Directive) and EUNIS taxonomies. The spatial patterns observed are in accordance with the existing literature for Central-Southern Italian peninsula and Puglia region (Biondi,Casavecchia, 2010; Biondi et al., 2006; Sciandrello, Tomaselli, 2014). Adjacency rules based on vegetation pattern turned out to be effective in habitat discrimination, and the products of the habitat mapping process were validated with high rates of overall accuracy. Nevertheless, the whole expected zonation is present only where pressures have none or low relevance. Human activities determining habitat loss, alteration and fragmentation cause deep changes in the vegetation spatial pattern. In cases of intense disturbance, regression effects may also occur (Acosta et al., 2007; Doing, 1985). Therefore, in defining and applying such rules, local expert information is required. Acosta A., Ercole S., Stanisci A., De Patta Pillar V., Blasi C., 2007. Coastal vegetation zonation and dune morphology in some Mediterranean ecosystems. Journal of Coastal Research, 23: 1518-1524. Biondi E., Casavecchia S., 2010. The halophilous retro-dune grasslands of the italian adriatic coastline. Braun-Blanquetia, 46: 11-127. Biondi E., Casavecchia S., Guerra V., 2006. Analysis of vegetation diversity in relation to the geomorphogical characteristics in the Salento coasts (Apulia-Italy). Fitosociologia, 43(1): 25-38. Doing H., 1985. Coastal fore-dune zonation and succession in various parts of the world. Vegetatio, 61: 65-75. Sciandrello S, Tomaselli V., 2014. Coastal salt marshes plant communities of the Salicornietea fruticosae class in Apulia (Italy). Biologia, 69(1): 53-69. Tomaselli V., Dimopoulos P., Marangi C., Kallimanis A.S., Adamo M., Tarantino C., Panitsa M., Terzi M., Veronico G., Lovergine F., Nagendra H., Lucas R., Mairota P., Mücher C.A., Blonda P., 2013. Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: a Mediterranean assessment. Landscape Ecology, 28(5): 905-930
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination
Full text linkThe present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 × 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 × 5 patch sizes are used and then ConvNet performance starts decreasin
Very high resolution change detection of land cover/use target classes in Mediterranean Natura 2000 sites
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