FN Archimer Export Format
PT J
TI Very high-resolution mapping of emerging biogenic reefs using airborne optical imagery and neural network: the honeycomb worm (     Sabellaria alveolata     ) case study
BT 
AF COLLIN, Antoine
   DUBOIS, Stanislas
   RAMAMBASON, Camille
   ETIENNE, Samuel
AS 1:1;2:2;3:1;4:1;
FF 1:;2:PDG-ODE-DYNECO-LEBCO;3:;4:;
C1 PSL Res Univ, EPHE, Dinard, Brittany, France.
   IFREMER, Lab Ecol Benth Cotiere LEBCO, Plouzane, France.
C2 EPHE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-DYNECO-LEBCO
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 2.493
TC 17
UR https://archimer.ifremer.fr/doc/00445/55644/57300.pdf
LA English
DT Article
AB 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. 
PY 2018
SO International Journal Of Remote Sensing
SN 0143-1161
PU Taylor & Francis Ltd
VL 39
IS 17
UT 000449510000005
BP 5660
EP 5675
DI 10.1080/01431161.2018.1484964
ID 55644
ER

EF