Collin, Antoine, Ramambason, Camille, Pastol, Yves, Casella, Elisa, Rovere, Alessio, Thiault, Lauric, Espiau, Benoît, Siu, Gilles, Lerouvreur, Franck, Nakamura, Nao, Hench, James L., Schmitt, Russell J., Holbrook, Sally J., Troyer, Matthias and Davies, Neil (2018) Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery. International Journal of Remote Sensing, 39 (17). pp. 5676-5688. DOI https://doi.org/10.1080/01431161.2018.1500072.

[img] Text
Collin_etal_2018.pdf - Published Version
Restricted to Registered users only

Download (2MB)

Abstract

Very high resolution (VHR) airborne data enable detection and physical measurements of individual coral reef colonies. The bathymetric LiDAR system, as an active remote sensing technique, accurately computes the coral reef ecosystem’s surface and reflectance using a single green wavelength at the decimetre scale over 1-to-100 km2 areas. A passive multispectral camera mounted on an airborne drone can build a blue-green-red (BGR) orthorectified mosaic at the centimetre scale over 0.01-to-0.1 km2 areas. A combination of these technologies is used for the first time here to map coral reef ecological state at the submeter scale. Airborne drone BGR values (0.03 m pixel size) serve to calibrate airborne bathymetric LiDAR surface and intensity data (0.5 m pixel size). A classification of five ecological states is then mapped through an artificial neural network (ANN). The classification was developed over a small area (0.01 km2) in the lagoon of Moorea Island (French Polynesia) at VHR (0.5 m pixel size) and then extended to the whole lagoon (46.83 km2). The ANN was first calibrated with 275 samples to determine the class of coral state through LiDAR-based predictors; then, the classification was validated through 135 samples, reaching a satisfactory performance (overall accuracy = 0.75).

Document Type: Article
Programme Area: PA2
Research affiliation: Affiliations > Not ZMT
Biogeochemistry and Geology
Biogeochemistry and Geology > Geoecology & Carbonate Sedimentology
Refereed: Yes
Open Access Journal?: No
DOI etc.: https://doi.org/10.1080/01431161.2018.1500072
ISSN: 0143-1161
Date Deposited: 03 Jun 2019 10:47
Last Modified: 24 Nov 2021 12:51
URI: http://cris.leibniz-zmt.de/id/eprint/1957

Actions (login required)

View Item View Item