Very high-resolution mapping of emerging biogenic reefs using airborne optical imagery and neural network: the honeycomb worm ( Sabellaria alveolata ) case study

Type Article
Date 2018
Language English
Author(s) Collin Antoine1, Dubois StanislasORCID2, Ramambason Camille1, Etienne Samuel1
Affiliation(s) 1 : PSL Res Univ, EPHE, Dinard, Brittany, France.
2 : IFREMER, Lab Ecol Benth Cotiere LEBCO, Plouzane, France.
Source International Journal Of Remote Sensing (0143-1161) (Taylor & Francis Ltd), 2018 , Vol. 39 , N. 17 , P. 5660-5675
DOI 10.1080/01431161.2018.1484964
WOS© Times Cited 12
Note Issue : Fine Resolution Remote Sensing of Species in Terrestrial and Coastal Ecosystems
Abstract

Biogenic reefs provide a wide spectrum of ecosystem functions and services, such as biodiversity hotspot, coastal protection, and fishing practices. Honeycomb worm (Sabellaria alveolata) reefs, in the Bay of Mont-Saint-Michel (France), constitute the largest intertidal bioconstruction in Europe but undergo anthropogenic pressures (aquaculture-stemmed food/space competition and siltation, fishing-driven trampling). Very high-resolution (VHR) airborne optical data enable cost-efficient biophysical measurements of reef colonies, strongly expected for conservation approaches. A synergy of remotely sensed airborne optical imagery, calibration/validation photoquadrat ground-truth (202/101, respectively), and artificial neural network (ANN) modelling is first used to map S. alveolata relative abundance, over the largest bioconstruction in Europe. The best prediction of S. alveolata abundance was reached with the infrared–red–green (IRRG) spectral combination and ANN model structured with six neurons (R2 = 0.72, RMSE = 0.08, and r = 0.85). The six hyperbolic tangent formulas were applied to the three input spectral bands (IRRG) in order to build six hidden neuronal images, resulting in VHR digital S. alveolata abundance model (6547 × 6566 pixels with 0.5 m pixel size). The innovative model revealed undescribed spatial patterns, namely a reef polarization (perpendicular to the shoreline) of S. alveolata abundance: high abundance on forereef and low abundance on backreef.

Full Text
File Pages Size Access
17 2 MB Access on demand
Author's final draft 19 2 MB Open access
Top of the page

How to cite 

Collin Antoine, Dubois Stanislas, Ramambason Camille, Etienne Samuel (2018). Very high-resolution mapping of emerging biogenic reefs using airborne optical imagery and neural network: the honeycomb worm ( Sabellaria alveolata ) case study. International Journal Of Remote Sensing, 39(17), 5660-5675. Publisher's official version : https://doi.org/10.1080/01431161.2018.1484964 , Open Access version : https://archimer.ifremer.fr/doc/00445/55644/