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).
Highlights
Worldwide, many studies have clearly highlighted the ability of the coastal ecosystems, like salt marshes [1,2,3,4,5,6,7,8,9,10,11], shelly ridges [9,11] or seagrasses [11], to reduce the wave height, Hm0
The simple linear regressions results showed the relevance of each predictor to explain the wave attenuation, via its coefficient of determination, ranked in ascendant order as follows: red edge (RE) (R2 : 0.24), digital surface models (DSMs) (R2 : 0.29), NIR (R2 : 0.32), R (R2 : 0.33), NDVI (R2 : 0.41), B (R2 : 0.50) and G (R2 : 0.51)
Dealing with multiple linear regressions, each predictor was added to the classic RGB combination to quantify how much each one could improve the coefficient of determination of the RGB model (R2 : 0.54)
Summary
Many studies have clearly highlighted the ability of the coastal ecosystems, like salt marshes [1,2,3,4,5,6,7,8,9,10,11], shelly ridges [9,11] or seagrasses [11], to reduce the wave height, Hm0. Drones 2020, 4, 25 reflectance of vegetation in the red edge (RE) and near-infrared (NIR) electromagnetic spectrum, the use of sensors provided with such wavebands and related index is well-suited to the need of an overview of the wave attenuation induced by a meadow. The use of these infrared wavebands makes it possible to discriminate plant functional traits, density [12,13,14], and species identification at very high spatial resolution (VHSR) using remote-sensing methods [8]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call