Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), we tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes.

Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao’s Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2—MSAVI2, Normalized Difference Water Index 2—NDWI2 and Brightness Index 2—BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao’s Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.

Many invasive plants are threatening the already highly vulnerable habitats of coastal dunes in Europe. Setting priority target species to control is mandatory for an effective planning of invasion management strategies at European level. This can be possible after identifying the species that currently have greater invasion success, in consideration of their ecological traits and origin. We quantified the three main components of invasion success for the extra-European alien plants found on European coastal dunes: local abundance, regional distribution and niche breadth, and related them to their life forms and origins. We found that life form was a better predictor of invasion success. In particular, geophytes and therophytes were the species with the greatest invasion success. Quite surprisingly, alien plants from Africa appeared as the group with slightly higher mean invasion success although this result was no statistically significant. We also highlighted the species deserving special attention. Among these,Xanthium orientale,Erigeron canadensisandOenotheragr.biennisshowed the widest levels of niche breadth and regional distribution, and had overall the greatest invasion success, but other species also had high levels in one of the three components of invasion success.