## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.2 Leveraging DL for Kelp Forest and Other Seaweed Ecosystems Monitoring  
Seaweed, or macroalgae, encompasses numerous marine plant and algae species, classified into red, brown, and green types. Within the brown algae category, kelp includes over 100 species that thrive in cold, nutrient-rich waters, forming underwater habitats resembling forests. Kelp forests are essential to marine ecosystems, providing food and shelter for marine life, sequestering atmospheric carbon, and producing oxygen [227]. However, similar to coral reefs, kelp forests face increasing threats from climate change [228]. As cold-water species, they are particularly susceptible to ocean warming and climate-related extreme events. Beyond climate impacts, kelp forests are also affected by water quality degradation due to nutrient runoff and pollution. According to a 2023 United Nations Environment Programme report [229], up to 60% of kelp forests have experienced degradation over the past 50 years. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \multirow{10}{*}{Seweed} & [PERSON] et al. [216] & Sargassum Mapping & MSI from MODIS-Aqua and VIRS & FANet, a DL-based Feedback Attention Network & Achieved 9\% overall accuracy and 91.72\% precision in cloud masking. \\ \cline{2-5}  & [PERSON] et al. [217] & Sargassum Mapping & Images from MODIS-Terra and Aqua, VIRS (SNPP), and OLCI (Sentinel-3) & Res-U-Net & F1-score = 92.5\%. \\ \cline{2-5}  & [PERSON] et al. [218] & Green Algae Detection & Sentinel-1 SAR image; nitrate concentration and SST (CMES) & GA-Net based on U-Net framework & mIoU = 86.31\%, Accuracy = 98.36\%, Precision = 93.29\%, Recall = 92.03\%, F1-score = 92.65\%. \\ \hline \multirow{10}{*}{Seweed} & [PERSON] et al. [219] & Classification & HSI acquired by OHS-1 sensor, Zhuhai-1 satellite. & HyperBCS, CNN with self-attention module & MongCai Dataset: OA = 98.29\% and Kappa = 9.79\%; CamPha Dataset: OA = 98.682\% and Kappa = 9.58\%. \\ \cline{2-5}  & [PERSON] et al. [220] & Classification & RGB imagery collected by a UAV PSPNet & U-Net, DeepLabv3+, highest performance, G2U-score = 0.8957, mIoU = 0.8188. \\ \cline{2-5}  & [PERSON] et al. [221] & Classification & Sentinel-1 SAR; Sentinel-2 Optical Imagery; LiDAR-derived DEM & Multimodel architecture integrating swim Transformer, VGG-16 CNN, and 3D CNN & OA = 92.30\%, AVA = 92.68\%, Kappa = 90.65\%. \\ \cline{2-5}  & [PERSON] et al. [222] & Mangrove Mapping & Sentinel-1 SAR using EfficientNet-B7, ResNet-101, and VGG16 as backbones. & U-Net with EfficientNet-B7, F1-score = 85.36\%, and IoU = 74.46\%. \\ \cline{2-5}  & [PERSON] et al. [223] & Mangrove Mapping & Sentinel-2 MSI integrating convolution, 3D convolution, and SK attention module & HSI-CNN, a model integrating 2D convolution, and SK convolution, and SK attention module & HSI-CNN, a model & OA = 94\%, Kappa = 0.93. \\ \cline{2-5}  & [PERSON] et al. [224] & Mangrove Mapping & GF-3 SAR; GF-6 MSI with SE attention mechanism & AttU-Net, U-Net, U-Net areas: OA = 94.41\%, F1-score = 90.01\%, Kappa = 84.05\%. \\ \cline{2-5}  & [PERSON] et al. [225] & Salt Marsh Mapping & Sentinel-2 MSI & U-Net & OA = 90\%, Kappa = 0.862. \\ \cline{2-5}  & [PERSON] et al. [226] & Salt Marsh Mapping & Point cloud data from LiDAR mounted on a drone & ANN & AUC = 0.9450. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Mapping the distribution and abundance of kelp forests is crucial for understanding the impacts of climate change on these vital underwater ecosystems [230]. Conventional techniques, such as object-based image analysis, spectral unmixing, and decision trees, have been widely used to monitor kelp dynamics [231; 232; 233; 234]. While effective, these methods often struggle to extract discriminative features and classify large datasets accurately.