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European semi-natural dry grasslands are among the most endangered terrestrial ecosystems, being recognised as habitats of community interest by the EU Habitats Directive. The occurrence and preservation of these habitats depend on a combination of anthropogenic and natural factors, although little is known regarding the role of past land-use changes. Here, we investigated the role of time since cultivation abandonment as a major driver of grassland successional dynamics in the Mediterranean agro-pastoral system of Alta Murgia, southern Italy. By integrating cartographic information on the past agricultural land-use with the main abiotic constraints (patch area, slope and aspect), we used generalised additive mixed models to test for the probability of occurrence of current grassland habitat types along time since cultivation abandonment (10 to 200 years). Our results disclosed the successional sequence of grassland plant communities since crop abandonment in the study area, highlighting that the distribution of semi-natural grassland communities largely depends on land use history besides current environmental patterns. Among the habitat types protected under the EU Habitats Directive, we highlighted that xero-thermic communities may represent an intermediate step of grassland succession after cultivation abandonment, while more mesic perennial communities indicate a late successional stage. These successional dynamics are further modulated by mesoclimatic conditions associated with slope and aspect, especially in case of long-standing pastures that were not historically affected by agricultural transformations. Our findings can contribute to a deeper understanding of dynamics relevant to spontaneous vegetation recovery in open environments, which is a prerequisite for setting up effective grassland conservation and restoration actions. Furthermore, our results underline the value of integrating historical maps and current information for the assessment of habitat conservation status., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The 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 decreasing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]