Classification of Seagrass Beds by Coupling Airborne LiDAR Bathymetry Data and Digital Aerial Photographs
Evaluation of the spatial distribution pattern of patchy and fragmental seagrass beds, as hotspots of faunal biodiversity and of high primary productivity, is key to the robust understanding of the ecological state and of the effects of environmental changes on biota in coastal areas. Supervised classification of aerial photographs and satellite imagery is used for assessing the state of shallow-water bottom features (i.e., substrata), such as rock and seagrass patches. For accurate classification, it is important to measure the topography of the seabed extensively and at high resolution, because the color of aerial photographs must be corrected for depth. This is difficult, however, because the shallowness of the water restricts the movements of survey vessels. We generated a digital surface model (DSM) of shallow-water bottom features via airborne LiDAR bathymetry and then used the DSM and digital aerial photographs to classify the bottom features. We conducted simultaneous bathymetry and aerial photography of a bay on the east coast of Tohoku, Japan, using a Fugro LADS Mk 3 system for bathymetry (at 5-m resolution) and a RedLake image sensor for aerial photography (at 0.4-m resolution). After using the topographic data to correct for absorption, we classified the imagery to reveal the distribution of seagrass beds. The estimated distribution corresponded with empirical observations.
KeywordsAirborne LiDAR Supervised classification Shallow-water bottom features Absorption correction Seagrass
We gratefully thank Y. Mochizuki, M. Nakaoka, M. Tamaoki, N. Nakajima, N. Takamura, M. Kurosawa, A. Shirai, and the staff at the International Coastal Research Center of the Ocean Research Institute (University of Tokyo) for the help with field data collection. This research was supported in part by the Center Project of the National Institute for Environmental Studies and Center for Environmental Biology and Ecosystem Studies (no. 1112AF001) and conducted by the National Institute of Advanced Industrial Science and Technology (no. 1112ZZ002).
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