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

TL;DR

This study demonstrates the application of deep learning techniques to classify land cover types such as seagrass beds, sandy areas, and oyster farming rafts in a Japanese lagoon, revealing changes caused by the 2011 earthquake and tsunami.

Contribution

It introduces a novel approach using deep learning for land cover classification in coastal areas, enabling effective detection of spatial changes after natural disasters.

Findings

Deep learning achieved over 69% accuracy in vegetation classification.

The method detected an increase in sand area and decrease in vegetation post-earthquake.

Segmentation model identified a decrease in oyster farming areas.

Abstract

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