Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Abstract

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).

Coastal wetlands mitigate storm flooding and associated costs in estuaries

“…Within estuaries, this interplay between the role of long-distance surge mitigation, and limited local wave and surge reduction in our study adds to previous work emphasising direct local wave-and surgeattenuation along exposed coastlines e.g. 53,64,65 , to reveal the under-appreciated role of marshes within typically wavesheltered small estuaries. Furthermore, within the size ranges of estuaries (3-108km²) in our study, the role of marsh vegetation in attenuating upstream surge was stronger in the smallest estuaries, consistent with previous evidence of scale-dependence of surge mitigation along creeks 54 , as well as theoretical work on the morphology and con guration of estuary channels [66][67][68] , and is supported by smaller surge reductions observed in the much larger Scheldt estuary 69 (~370km²).…”

Predicting the Infrared Uav Imagery Over the Coast

“…However, most of the commercial UAVs are only provided with a RGB sensor (e.g., Schiefer et al, 2020). In line with MAV (Collin et al, 2018a) and satellite (Collin et al, 2018b) imagery, the integration of the NIR can yet significantly improve the UAV-based estimation (Mury et al, 2020) and classification of continuous and discrete environmental variables (Collin et al, 2019b.…”

Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel

“…By investigating the medium-scale Dove Planetscope PS2 information, the mutating zone was further analyzed at the local scale using the ultra high resolution UAV-borne senseFly "sensor optimized for drone application" (SODA) sensor. This 1'' camera was mounted on a fixed-wing eBee UAV [21], whose the flight mission was planned at 120 m height (here, equivalent to the altitude), what corresponded to a 0.03-m pixel size. The survey occurred on 8 July 2020, three days before the Dove acquisition.…”

“…Usually restricted to active very fine-scale remote sensing, such as airborne topographic and/or bathymetric lidar [20], the salt marsh vegetation height and composition could have been reached using neural network (NN) predictive modelling [19]. By augmenting the spatial resolution, the three-band sensor conveyed by unmanned aerial vehicle (UAV) can leverage natural-colored imagery (red-green-blue, RGB) at ultra high resolution, ranging from the 0.01-m to 0.1-m pixel size [21]. This flexible platform, along the time axis, is strongly cost-efficient owing to the consumer-grade purchase and maintenance costs, but very frequently lacks the NIR band, meaningful for discriminating the salt marsh vegetation, soil, sediment and water.…”