Abstract
Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS) to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF) algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV) ensembles for Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), and Bagging (BAG) classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.
Highlights
Monitoring and mapping of benthic habitats using remote sensing systems and machine learning approaches can expand our understanding of living conditions in such environments, and ensure, with appropriate supervision, the survival of occupying species over time
We introduced an approach for the semi-automatic detection and mapping of the Shiraho heterogeneous coastal area, including corals, algae, seagrass, and sediment
The Weighted Majority Voting (WMV) algorithm was applied to collate the outputs from three machine learning algorithms
Summary
Monitoring and mapping of benthic habitats using remote sensing systems and machine learning approaches can expand our understanding of living conditions in such environments, and ensure, with appropriate supervision, the survival of occupying species over time. Developments in high-quality video cameras have meant that data from videos from towed cameras can be accurately recorded and geo-located. High-quality towed video cameras can record clear images of seafloor benthic habitats, and cover large regions quickly without affecting the environment [1], providing a potential system for monitoring benthic habitats within coastal ecosystems. The analysis of recorded towed videos in marine applications is usually performed manually [3], and automatic feature extraction is not often applied [4,5]. The implementation of appropriate algorithms is fairly difficult, and many complexities are still associated with the video data of a towed system, including unstable illumination due to limited energy and variable velocities, angles, and elevations of the camera above the seafloor. The algorithms have to analyze a wide spectrum of overlapping features spread over the seafloor
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