Coastal living reefs provide considerable services from tropical to temperate systems. Threatened by global ocean-climate and local anthropogenic changes, reefs require spatially explicit management at the submeter scale, where socioecological processes occur. Drone surveys have adequately addressed these requirements with red-green-blue (RGB) orthomosaics and digital surface models (DSMs). The use of ancillary spectral bands has the potential to increase the mapping of all reefscapes that emerge during low tide. This research investigates the contribution of the drone-based red edge (RE), near-infrared (NIR), and DSM into the classification accuracy of five main habitats of the largest intertidal biogenic reefs in Europe, built by the honeycomb worm Sabellaria alveolata. Based on photoquadrats and the maximum likelihood algorithm, overall, producer’s and user’s accuracies were distinctly augmented. When isolated, the DSM provided the highest gain percentage (3.42%), followed by the NIR (2.58%), and RE (2.02%). When joined, the combination of the DSM with both RE and NIR was the best contributor (4.98%), followed by the DSM with RE (4.80%), DSM with NIR (3.74%), and RE with NIR (3.22%). At the class scale, all datasets increasingly advantaged sand, gravel, reef, mud and water. The rather low effect of the DSM with NIR (3.74%) was assumed to be linked with a statistical noise originated from redundant information in the intertidal area.

Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while human-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes to Bora Bora over time, this study highlights the impacts of human-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia.

Coastal areas are among the most endangered places in the world, due to their exposure to both marine and terrestrial hazards. Coastal areas host more than two-thirds of the world’s population, and will become increasingly affected by global changes, in particular, rising sea levels. Monitoring and protecting the coastlines have impelled scientists to develop adequate tools and methods to spatially monitor morpho-sedimentary coastal areas. This paper presents the capabilities of the aerial drone, as an “all-in-one” technology, to drive accurate morpho-sedimentary investigations in 1D, 2D and 2.5D at very high resolution. Our results show that drone-related fine-resolution, high accuracies and point density outperform the state-of-the-science manned airborne passive and active methods for shoreline position tracking, digital elevation model as well as point cloud creation. We further discuss the reduced costs per acquisition campaign, the increased spatial and temporal resolution, and demonstrate the potentialities to carry out diachronic and volumetric analyses, bringing new perspectives for coastal scientists and managers.

In the current context of decadal global changes and predicted sea level rise, annual erosion is one of the most obvious indicators of threats to coastal systems. Shoreline monitoring during high energy events is therefore a crucial action to prevent and alleviate future coastal risks. However, most studies look at this on a regional issue basis with limited resolution, and with limited support from field observations. This study addresses this lack by focusing on high resolution (HR) shoreline surveys, combined with wave measurements, in the megatidal Bay of Mont-Saint-Michel. The salt marsh vegetation line and the inner margin of shelly ridges were selected as markers of the stabilized shoreline, to follow its evolution during two high energy winter events, from February 18 to 24, 2015 and from March 19 to 24, 2015, in two different study sites. A transdisciplinary methodology was adopted which included: (1) in situ wave measurements with pressure sensors, (2) topographical data acquisition using a differential GPS, and (3) in silico observations of the shoreline movements through HR aerial and satellite imageries. Our findings highlighted the positive linkage between significant wave height and erosion rate (ranging from 0 to 60.9 m), as well as the variability of coastline responses depending on the geomorphic features.

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.

James, D.; Collin, A.; Houet, T.; Mury, A.; Gloria, H., and Le Poulain, N., 2020. Towards better mapping of seagrass meadows using UAV multispectral and topographic data. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1117-1121. Coconut Creek (Florida), ISSN 0749-0208Seagrasses are exposed to global changing such as ocean warming and acidification, sedimentation, eutrophication and direct degradation. Seagrass meadows are traditionally monitored with visible imagery in red-green-blue (RGB). To date, the acquisition methodology dedicated to coastal habitats ranges from satellite to SoNAR through LiDAR sensors. This research proposes to evaluate the contribution of unmanned airborne vehicle (UAV) multispectral bands (red edge - RE -, and near-infrared - NIR) and digital surface model (DSM) to the habitat classification based on the traditional red-green-blue (RGB) dataset. Surveyed by a fixed-wing UAV (eBee+ provided with the Sequoia and S.O.D.A. sensors), five classes were studied: water, Zostera marina L. eelgrass, boat, sediment, and algae. Supported by the maximum likelihood algorithm, the classifications showed a very satisfactory performance for the RGB benchmark (93.64%), and gains when the spectral bands and DSM were added separately (RE+NIR contributions: +4.46%, DSM contribution: +4.78%) and jointly (RE+NIR+DSM contributions: +5.84%). The contributions of the spectral and topographic data were further discussed at class level, with an emphasis on the boat-driven seagrass fragmentation (15% of the meadow).