Eelgrass beds and oyster farming at a lagoon before and after the Great East Japan Earthquake 2011: potential to apply deep learning at a coastal area
This work addresses coastal monitoring for environmental management after natural disasters, but it is incremental as it applies existing deep learning methods to a new dataset.
The study tackled automatic land cover classification in a coastal lagoon to detect changes in seagrass beds, sandy areas, and oyster farming rafts before and after the 2011 earthquake, achieving over 69% accuracy for vegetation classification using deep learning. It revealed increases in sand area and decreases in vegetation and oyster farming post-earthquake.
There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The result was used to extract the changes before and after the earthquake and tsunami. The output resolution was best in the image transformation method, which showed more than 69% accuracy for vegetation classification by an assessment using random points on independent test data. The distribution of oyster farming rafts was detected by the segmentation model. Assessment of the change before and after the earthquake by the manual tracing and image transformation result revealed increase of sand area and decrease of the vegetation. By the segmentation model only the decrease of the oyster farming was detected. These results demonstrate the potential to extract the spatial pattern of these elements after an earthquake and tsunami. Index Terms: Great East Japan Earthquake of 2011, Land use land cover (LULC), Zosteracea seagrass, cultured oyster, deep learning, Mangoku Bay