## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.3 DL for Coastal Wetland Mapping and Analysis  
Coastal wetlands, such as mangroves and salt marshes, are vital ecosystems located at the boundary of land and ocean [240]. They play a key role in supporting marine biodiversity, sustaining fisheries, protecting coastlines from erosion, and mitigating climate change by sequestering greenhouse gases. However, these ecosystems are increasingly threatened by climate change, with rising sea levels, stronger storms, and shifts in salinity and temperature posing significant challenges. Human activities like pollution and land reclamation further exacerbate their degradation. Mapping the spatial distribution of coastal wetlands is essential for observing their changes over time. The integration of advanced ocean remote sensing techniques with DL models has greatly improved the accuracy and efficiency of coastal wetland mapping [241; 242]. For example, [PERSON] et al. [220] used RGB imagery from UnmannedAerial Vehicles (UAVs) to classify coastal wetlands. They compared traditional pixel-based and object-based methods with three DL models, i.e., U-Net, PSPNet, and DeepLabV3+. Among these, DeepLabV3+ achieved the highest accuracy at 94.6%, producing the most precise vegetation distribution map. Their study highlighted the potential of integrating UAV remote sensing, cloud computing, and DL for effective coastal wetland monitoring. Convolution-based models, combined with Transformer architectures, have been increasingly applied to coastal wetland classification. [PERSON] et al. [221] introduced a multimodel DL framework that utilized Sentinel-1 SAR data, Sentinel-2 optical multispectral data, and LiDAR point clouds as inputs. By integrating a modified 3D CNN with a Swin Transformer, the framework achieved superior classification accuracy compared to traditional methods such as RF and SVM. [PERSON] et al. [219] developed a method that combines convolutional operations with self-attention mechanisms to capture both local and global features from hyperspectral images for coastal wetland mapping. The proposed method achieved state-of-the-art performance, with overall accuracies (OA) of 98.29% at Mong Cai and 96.82% at Cam Pha. Mangroves are one of the most extensively studied coastal wetlands [243, 244], offering essential habitats for marine life and playing a key role in carbon sequestration. Their detection and mapping mainly rely on optical remote sensing and SAR technologies. Optical remote sensing data provides detailed information about vegetation cover, facilitating the identification and classification of mangroves [245, 246, 247]. [PERSON] et al. [223] introduced a selective kernel mechanism into a CNN framework for mapping mangrove ecosystems using multimtemporal Sentinel-2 imagery. Unlike traditional CNNs with fixed convolutional kernels, the selective kernel mechanism dynamically adjusts kernel sizes during the learning process through three stages, i.e., split, fuse, and select, enhancing feature extraction across different scales. The model achieved a 94% accuracy, outperforming other algorithms such as XGBoost (87%) and 3D-DenseNet (90%), demonstrating its effectiveness in mangrove ecosystem mapping. SAR data is particularly effective for mangrove mapping in challenging environments, as it can penetrate clouds and fog, operate day and night, and provide valuable information about surface roughness and dielectric properties. Using Sentinel-1 time series data collected from 2017 to 2020 along the Brazilian coast, [PERSON] et al. [222] assessed different polarization methods and evaluated the performance of U-Net with three distinct backbones: ResNet-101, VGG16, and EfficientNet-B7. Their results revealed that the combination of VV&VH polarizations and the U-Net utilizing EfficientNet-B7 as the backbone yielded the best result, achieving an OA of 97.35%. Moreover, combining these two data types often leverages their complementary strengths, enhancing the accuracy of mangrove mapping [248, 249]. [PERSON] et al.