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.

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm₀) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R²): 0.85, root mean square error (RMSE): 0.20%/m).

Mury, A.; Collin, A.; Jeanson, M.; James, D.; Gloria, H.; Pastol, Y., and Etienne S., 2020. Mapping nature-based marine flooding risks using VHR wave, LiDAR, satellite: The case study of the Dol Marsh (Bay of Mont-Saint-Michel, France). In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 743-747. Coconut Creek (Florida), ISSN 0749-0208.A growing interest in nature-based solutions has gained unprecedented attention in the coastal risk management, to complement or replace conventional approaches like hard structures (dykes, seawalls, breakwaters). However, due to the diversity of coastal ecosystems and the heterogeneity of the ecosystem service of wave attenuation they can induce, the integration of their protective role in an accurate way into risk studies remains rare. This paper shows an experimental methodology to integrate this ecosystem service into the risk mapping at very high spatial resolution using in situ sensors, airborne LiDAR and spaceborne satellite imagery. Risk study is achieved using a combination of indices which allows to evaluate the asset's exposure and vulnerability. The nature-based solution is also integrated through the creation of an adaptive capacity index, based on a spatially-explicit model of wave attenuation. The study enables to highlight the assets which are the most concerned by the marine flooding risk, using a synthetic risk index ranging from 0 to 1.