Benthic mapping using high resolution multispectral and hyperspectral imagery

Abstract

Coastal ecosystems are essential due to their high biodiversity and primary production, however they are extremely complex and with high spatial and temporal variability. Thus, to properly manage them it is necessary a systematic monitoring. Remote sensing can be very useful due to the spatial and spectral improvement of satellites and the availability of airborne or drone hyperspectral sensors. Unfortunately, the mapping of coastal areas is challenging due to the low SNR received at the sensor, as a consequence of the minimum reflectivity of the seafloor and the atmospheric and water column disturbances. In this context, the goal of this work is to obtain a robust classification methodology to generate accurate benthic habitat maps applying object-oriented and pixel-based classification methods in shallow waters using WorldView-2 and AHS (Airborne Hyperspectral Scanner) images. Maspalomas (Gran Canaria, Spain) was studied due to its complexity and the presence of important seagrass meadows.