Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

Abstract

Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.

Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets

“…Some studies have shown promising results in binary classification in which mangrove are distinguished from non-mangrove classes such as water, human settlement, and agricultural field [28,29]. However, this study identified multiple classes of intricate mangrove regions that contributed detailed LULC information for the management of mangroves.…”

“…Among state-of-the-art models, the U-Net segmentation model and artificial neural networks (ANNs) have consistently demonstrated superior performance in remote sensing image analysis [25][26][27]. However, limited studies have applied the U-Net and ANN models using different spatial resolution of satellite images for mangrove ecosystem [28][29][30]. Moreover, their research focused only on mangrove and non-mangrove classifications to highlight the distribution and dynamics of mangroves.…”