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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.

Desertification seriously hinders economic development and ecological security, which has led to increased research on desertification monitoring and control. Remote sensing technology is widely used in desert research due to its large detection range and ability to obtain target feature information without touching objects. In order to better monitor and control desertification, the research methods on desert mobility and dune morphology in mobile deserts were reviewed. Among them, an important index to distinguish mobile and nonmobile deserts is desert vegetation coverage. The research progress of desert vegetation coverage based on visual interpretation, the nonlinear spectral model, normalized vegetation index (NDVI) fitting and plant community classification was reviewed. The loss of vegetation in the transitional zone of the desert is a contributing factor to desertification. The new technologies and applications of desert area monitoring, the remote sensing ecological index, and desert feature information extraction were introduced and analyzed. To combat desertification more accurately and effectively, the classification methods of moving dunes based on deep learning were also reviewed. It can be concluded that desertification monitoring methods are gradually becoming more accurate and adaptive, but they remain insufficient and less mature. Therefore, exploring how to apply desertification control technology more scientifically and rationally is an extremely valuable area for research.

Aims: Although many phytosociological studies have provided detailed local and regional descriptions of coastal dune vegetation, a unified classification of this vegetation in Europe and the Mediterranean Basin has been missing. Our aim is to produce a formalized classification of this vegetation and to identify the main factors driving its plant species composition at a continental scale. Location: Atlantic and Baltic coasts of Europe, Mediterranean Basin and the Black Sea region. Methods: We compiled a database of 30,759 plots of coastal vegetation, which were resampled to reduce unbalanced sampling effort, obtaining a data set of 11,769 plots. We classified these plots with TWINSPAN, interpreted the resulting clusters and used them for developing formal definitions of phytosociological alliances of coastal dune vegetation, which were included in an expert system for automatic vegetation classification. We related the alliances to climatic factors and described their biogeographic features and their position in the coastal vegetation zonation. We examined and visualized the floristic relationships among these alliances by means of DCA ordination. Results: We defined 18 alliances of coastal dune vegetation, including the newly described Centaureo cuneifoliae-Verbascion pinnatifidi from the Aegean region. The main factors underlying the differentiation of these alliances were biogeographic and macroclimatic contrasts between the Atlantic-Baltic, Mediterranean and Black Sea regions, along with ecological differences between shifting and stable dunes. The main difference in species composition was between the Atlantic–Baltic and Mediterranean–Black Sea regions. Within the former region, the main difference was driven by the different ecological conditions between shifting and stable dunes, whereas within the latter, the main difference was biogeographic between the Mediterranean and the Black Sea. Conclusions: The first formal classification of the European coastal dune vegetation was established, accompanied by an expert system containing the formal definitions of alliances, which can be applied to new data sets. The new classification system critically revised the previous concepts and integrated them into a consistent framework, which reflects the main gradients in species composition driven by biogeographic influences, macroclimate and the position of the sites in the coast–inland zonation of the dune systems. A revision of the class concept used in EuroVegChecklist is also proposed.

Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.

Barrier islands support ecological diversity and offshore ecosystems and provide critical protection to coastal communities. Climate change has intensified the frequency and severity of hurricanes affecting these islands, leading to ongoing erosion. The primary goal of this study was to explore the relationship between human intervention such as development and construction and the vegetative dune systems on Gulf Coast barrier islands in Alabama and Mississippi, USA. This research employed two decades of satellite images of three neighboring barrier islands and employed GIS raster classification to track changes in the vegetative dune system in terms of: (1) dune coverage (surface area of the vegetation), (2) vegetative maturity (vegetation type), and (3) stability (fluctuations in the vegetative coverage over time). Time series and trend analyses were used to compare the results for three neighboring islands. The findings show that Dauphin Island, which features both commercial facilities and vacation homes, exhibited a decrease in total area over time, and had the lowest percentage of vegetative dune coverage and highest level of vegetative fluctuation. In contrast, Petit Bois and Horn Islands, which remain untouched by human activity, displayed significantly higher levels of vegetative maturity and coverage and comparatively less fluctuation. This research provides a foundation for those advocating for dune restoration strategies, development limitations, and conservation regulations as nature-based infrastructure solutions to combat erosion on barrier islands and serves as a point of entry for future inquiries in the field of environmental management.

Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management.

Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands’ shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with man-made infrastructure such as seawalls. High-resolution satellite imagery from Planet was used to assess the impacts of these hurricanes on the beach shorelines of Volusia, Flagler, and St. Johns Counties on the Florida Central East Coast. Shorefront vegetation was classified into two classes. Normalized Difference Vegetation Index (NDVI) values were calculated before the hurricanes, one month after Hurricane Ian, one month after Hurricane Nicole, and one-year post landfall. LiDAR (Light Detection and Ranging) was incorporated to calculate vertical changes in the shorelines before and after the hurricanes. The results suggest that natural sand dunes were more resilient as they experienced less impact to vegetation and elevation and more substantial recovery than armored dunes. Moreover, the close timeframe of the storm events suggests a compound effect on the weakened dune systems. This study highlights the importance of understanding natural dune resilience to facilitate future adaptive management efforts because armored dunes may have long-term detrimental effects on hurricane-prone barrier islands.

Coastal dune distinction is an essential for monitoring, conservation and sustainable management of fragile coastal ecosystem. Most of the coastal scientists classified the coastal sand dunes by conventional methods as frontal dunes (foredune) and back dunes (stabilized dune). Due to the significance in the developing stage of the dunes and the coastal protection, foredunes are very significant in this context. Studies demarcating the boundary of coastal dune field based on its spectral signatures of vegetation species have not been attempted by any authors earlier. In this view, the goal of this study was to determine whether it is possible to demarcate the foredune from the dune field using vegetation species with the aid of Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral data. To achieve this, the study scrutinized the major vegetation species and the end members were identified using Pixel Purity Index algorithm (PPI), N-Dimensional visualizer, Insitu spectra and Linear Spectral Unmixing algorithm. Identified training pixels falling in the dune field were grouped into six training classes to perform classification techniques (Spectral Angle Mapper (SAM), Spectral Informative Divergence (SID) and Support Vector Machine (SVM)). This study also discussed about various methods of selecting training pixels for demarcation of foredunes. Accuracy assessment reveals that SVM shows an overall accuracy of 91.97% compared to SID of 68.25% and SAM of 58.52% with kappa coefficient of 0.89, 0.53 and 0.49 respectively. The study found the indicator species such as Ipomoea Pes-caprae, Spinifex Littoreus and Launaea Sarmentosa blankets the foredunes in this study area, which could be used to demarcate the boundary of the foredunes. The methodology employed in this study can be applied to distinguish the foredune from the dune field in any coastal dune ecosystem, provided that the researcher has access to a database of the flora present in the area. The proposed methodology and this research might bring new insight into the classification and demarcation of foredune using the vegetation species present over the dune field. The research allows more efficient and cost-effective identification of foredunes from vast coastal environments, providing valuable information for the conservation and sustainable management of ecologically sensitive ecosystems.

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.

What are the main findings? * Spectral unmixing with random forest regressor showed good skill in terms of detecting the fractional cover of major dune plant species but performed poorly for small-stemmed or less abundant species. * Model accuracy was enhanced when including the distance to the shoreline in the predictor dataset, with a significantly lower influence when including surface elevation data. Spectral unmixing with random forest regressor showed good skill in terms of detecting the fractional cover of major dune plant species but performed poorly for small-stemmed or less abundant species. Model accuracy was enhanced when including the distance to the shoreline in the predictor dataset, with a significantly lower influence when including surface elevation data. What are the implications of the main findings? * Class abundance and plant characteristics (spectral signature and fractional cover within the pixel) are significant limiting factors for the detectability of a class. * Enhancing broadness and resolution of spectral data used with unmixing algorithms may be the key to improving the segregation of plants or plant groups in highly mixed coastal dunes. Class abundance and plant characteristics (spectral signature and fractional cover within the pixel) are significant limiting factors for the detectability of a class. Enhancing broadness and resolution of spectral data used with unmixing algorithms may be the key to improving the segregation of plants or plant groups in highly mixed coastal dunes. While improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mixed and sparse vegetation requires high resolution satellites and spectral unmixing techniques. To achieve this aim, we employed random forest regressors to predict the fractional cover of dune plant species in two of the sandy barriers of Ria Formosa (S. Portugal) from WorldView-2 imagery (June 2024). The algorithm, tested with spatially upscaled multispectral drone data and satellite imagery, detected the fractional cover of major species (most abundant classes and bushy vegetation) with reasonable to very good accuracy (coefficient of determination, CoD: 0.4 to 0.8) for the former and reasonable to good accuracy (CoD: 0.4 to 0.6) for the latter. Additional tests showed that (a) including the distance to the shoreline can increase model accuracy (CoD by ~0.1); (b) the grouping of species resulted in an insignificant increase in model skill; and (c) testing over independent dune plots showed generalisation beyond the training set and low risk of overfitting or noise. Overall, the approach showed promising results for large-scale observations in highly mixed coastal dunes.