The fourth edition of the International Code of Phytosociological Nomenclature (ICPN) was prepared by the Steering Committee of the IAVS Working Group for Phytosociological Nomenclature (GPN). The edition consists of 14 Definitions, 7 Principles, 53 Articles, and 7 Appendices. When compared with the previous edition, the main amendments are: (a) the acceptance of electronic publications (Art. 1); (b) the introduction of binding decisions (Definition XIV, Principle II, Articles 1, 2b, 3c, 29b, 40, 42, 44, Appendices 6 and 7); (c) the mandatory use of the English or Latin terminology for syntaxonomic novelties (Definition II, Principle II, Articles 3d and 3i); (d) the introduction of autonyms for the main ranks when the corresponding secondary ranks are created (Articles 13b and 24); (e) the automatic correction of the taxon names (name‐giving taxa) used in the names of syntaxa in accordance with the International Code of Nomenclature for algae, fungi, and plants (ICN) (Article 44); (f) the possibility to mutate the name of a syntaxon in using other correct, alternative names for the name‐giving taxa (Article 45); (g) the introduction of inadequate names, a new category of rejected names (Definition V, Articles 43 through 45); and (h) the introduction of a conserved type (Definition XIII, Article 53). The fourth edition of ICPN was approved by the GPN on 25 May 2019 and becomes effectively binding on 1 January 2021.

Italy has a rich natural heritage, which is dangerously under pressure. In recent years, there is an increased awareness of the crucial role of plants in ecosystem functioning and in providing ecosystem services. Consequently, an updated Red List of the Italian vascular flora was compiled in this work, at the request of the Ministry for Environment, Land and Sea Protection, with the scientific support of the Italian Botanical Society. The IUCN Red List criteria were applied to 2,430 Italian native vascular plant taxa to assess their current extinction risk and to highlight the major threats affecting the Italian flora. Our results revealed that 54 taxa (2.2% of the assessed taxa) are extinct or possibly extinct at regional level, while 590 taxa (24.3%) were assigned to a risk category. Moreover, 404 taxa (16.6%) were categorized as Data Deficient. The Italian vascular flora is primarily threatened by habitat modifications due to anthropic disturbance and, especially, to agriculture, tourism and residential development. Coastal areas and lowlands, where anthropogenic impacts and ecosystem destruction are more pronounced, host the greatest number of extinct or declining taxa. Our results represent an important baseline to establish conservation priorities, legislative choices and intervention strategies on a national scale.

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.