Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery

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).

Acknowledgments

Authors gratefully thank Service Hydrographique et Océanographique de la Marine for the LiDAR acquisition control and the IDEA Consortium for sparking this collaborative research. This work was partly supported by French Polynesia government for LiDAR acquisition, the ETH Zurich for purchasing satellite imagery, and the National Science Foundation through the Moorea Coral Reef LTER (OCE-1236905 and 1637396) and Physical Oceanography (OCE-143133) programs. Two valuable referees and the editor are deeply acknowledged for the manuscript improvement.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation [OCE-1236905,OCE-143133,OCE-1637396].