Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution(VHR)imagesfornaturalgrasslandecosystemmapping. Theclassificationwasappliedto a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies

Phytosociological research on temporary ponds in Apulia (southern Italy)

The ephemeral hygrophilous vegetation occurring in the temporary ponds of Apulia (southern Italy) weres studied following the phytosociological approach. On the base of 153 phytosociological relevés carried out during the period 2015-2018, 19 associations were identified, of which 16 described for the first time. All the associations belong to the Isoeto-Nanojuncetea class. The surveyed associations can be arranged in two orders, such as Isoetetalia, including those with a winter-spring cycle and Nanocyperetalia, regarding those with a summertime cycle. The identified association has been examined in detail, and for each one, we provided a phytosociological table. The communities belonging to the Isoetetalia are 17 and arranged in four alliances (Isoetion, Preslion cervinae, Cicendio filiformis-Solenopsion laurentiae, Agrostion salmanticae); instead, those ones of Nanocyperetalia are two both included in the Verbenion supinae. In order to highlight the relationships among the associations, all the relevés used for this investigation are processed. Overall, this analysis confirms the autonomy of the associations, grouping them according to the syntaxonomic arrangement proposed by the authors. Taxonomic investigations on the flora occurring in these habitats allowed the detection of two new subspecies of Solenopsis laurentia, both with a different autoecology

Validation of associations for the temporary ponds of the class Isoeto-Nanojuncetea in Puglia (southern Italy)

This paper presents the validation of 16 new associations, described in a previous contribution, for the temporary ponds of the class Isoeto-Nanojuncetea in Apulia (southern Italy)

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

Notulae to the Italian native vascular flora: 4

In this contribution new data concerning the distribution of native vascular flora in Italy are presented. It includes new records, exclusion, extinction and confirmations to the Italian administrative regions for taxa in the genera Androsace, Artemisia, Fragaria, Melampyrum, Myosotis, Petrorhagia, Phillyrea, Rosa, Rumex, Spiranthes, Trifolium, and Vicia. Furthermore, a new combination in the genus Omalotheca is proposed

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

Monitoring and Recording Changes in Natural Landscapes: A Case Study from Two Coastal Wetlands in SE Italy

This study analyzed and evaluated the changes that occurred in two coastal wetlands, characterized by complex and fragmented landscape patterns, in Southern Italy, which were monitored over a period of seven years from 2007 to 2014. Furthermore, the performances of two Land Cover (LC) and habitat taxonomies, compared for their suitability in mapping the identified changes, were assessed. A post-mapping method was adopted to detect the habitat/LC changes that occurred in the study period. Various changes were identified, both inter-class changes (class conversions) and intra-class changes (class modifications), and quantified by means of transition matrices. Conversions were easily mapped, while the modification mapping depended on the taxonomy adopted: the Land Cover Classification System (LCCS) allowed the detection of morpho-structural changes in woody vegetation, but the European Nature Information System (EUNIS) showed a higher thematic resolution for the salt marsh types. The detected changes were related to specific impacts, pressures and underlying factors. Landscape indices highlighted different trends in landscape richness and complexity in the two sites. Changes are occurring very quickly in the observed coastal sites and the ongoing dynamics are strictly related to their small size and complexity. For effective monitoring and detection of change in these environments, the coupling of EUNIS and LCCS is suggested

Land Cover map for Le Cesine in FAO-LCCS taxonomy

Land Cover map for Le Cesine in FAO-LCCS taxonomy. Spatial resolution: 2 meters. The README file contains the look-up