ICESat-2 and Multispectral Images Based Coral Reefs Geomorphic Zone Mapping Using a Deep Learning Approach

Abstract

The coral reef geomorphic zone classification (CRGZC) map can provide a wealth of information for coastal management and protection. Remote sensing plays an important role in CRGZC by virtue of its speed, wide range, and low cost. Although many excellent results have been achieved in this field, there are still some shortcomings. With the development of machine learning, such methods are gradually introduced to CRGZC, yet the research and application of deep learning methods are still relatively few. In this article, based on ICESat-2 data and multispectral images, a deep learning model coupled with convolutional neural network (CNN) and random forest (RF) was proposed for coral reef geomorphic zone classification (CR_CRGZC). First, the priori bathymetry points were extracted from ICESat-2. Then, a near-shore bathymetry map was generated using a log-ratio model. Finally, topographic data and multispectral images were combined to achieve CRGZC through CR_CRGZC. The northeastern part of Coffin Island (CI) and the southern part of Punta Vaquero (PV) in Puerto Rico Island were selected as study areas. By comparing the classification results with those of CNN, RF, and maximum likelihood classification, CR_CRGZC outperformed the other classification methods. By quantitative analysis, the OA and Kappa coefficients of CR_CRGZC were 91.91% and 0.9013 in the CI region; and 89.91% and 0.8735 in the PV region, respectively. Under the same environmental requirements, this approach can map high-precision submeter CRGZC maps, providing a database for dynamic coral reef habitat mapping, which contributes to marine coastal ecosystem protection and coastal underwater topography monitoring.

Keywords

• Coral reef geomorphic zone classification (CRGZC)
• deep learning
• ICESat-2
• multispectral image
• nearshore bathymetry