Search

Farmland habitats host important populations of several threatened bird species. So far, how to reconcile farmland management with the maintenance of viable populations of these taxa is a major challenge for conservation biology. Southeastern Italy hosts ca 7000 pairs of breeding lesser kestrels Falco naumanni, representing one of the European strongholds of this small colonial raptor of conservation concern. We firstly assessed the relative importance of managed crops versus seminatural grasslands in determining the local abundance of lesser kestrels at the landscape scale, and we successively studied the foraging habitat preferences at a smaller spatial scale. Surveys of foraging birds were associated with land-use collection at 191 homogeneous habitat sampling parcels from 14 plots of 16 km2 each, uniformly distributed over a 2400 km2 area. Each plot was visited six times during the 2017 breeding season (May-July). Land-use markedly changed along the season, unripe cereals being dominant in May, while harvested cereal crops prevailed in July. Land-use did not affect lesser kestrel distribution early in the season while foraging birds were more abundant in plots with a greater proportion of harvested cereal crops and a lower one of semi-natural grassland in the late breeding season. In accordance, the analysis of foraging habitat preferences within plots showed that in May unripe cereal crops and semi-natural grasslands were used proportionally to their availability. In June and July, harvested cereal crops were used more than expected from their availability, while semi-natural grasslands were significantly avoided. Our landscape-scale analysis thus indicates that semi-natural grasslands are much less used in comparison to harvested crops during the mid and late parts of the breeding season, suggesting that lesser kestrel may be able to take advantage of crop management practices more than other farmland birds of conservation priority. [ABSTRACT FROM AUTHOR]

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) images for natural grassland ecosystem mapping. The classification was applied to 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. [ABSTRACT FROM AUTHOR]