## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.1 DL Applications for Coral Reef Monitoring  
Coral Reef ecosystems are essential for oceans, providing habitats for marine organisms and supporting marine biodiversity. However, they face threats from climate change, highlighting the necessity for effective monitoring to safeguard their health and stability. Traditional methods often struggle with limitations in scale and efficiency, making DL a promising alternative. By leveraging multisource data and uncovering complex spatial and temporal patterns, DL has demonstrated great potential in mapping coral reefs and detecting coral bleaching [193; 194]. DL techniques, such as CNNs and U-Nets, have been widely used for high-resolution coral reef mapping. [PERSON] et al. [195] developed a method combining CNNs and RF to classify coral reef geomorphic zones. The approach integrated ICESat-2 LiDAR data with multispectral imagery, achieving superior accuracy and robustness while maintaining low computational costs. [PERSON] et al. [196] combined high-resolution Planet Dove satellite  
Figure 5: Flowchart of DL applications for monitoring climate change impacts on various marine ecosystems. imagery with regional data from the Millennium Coral Reef Mapping Project (MCRMP) and employed a Dense U-Net architecture to enhance feature transmission. An RF classifier was subsequently applied to distinguish coral reefs from noncoral reefs, producing a comprehensive global coral reef extent map. [PERSON] et al. (2019) introduced a U-Net architecture with an attention mechanism and geospatial cognition to reduce misclassification caused by spatial changes. Experiments on Gaofen-2 satellite images demonstrated notable improvements in classification accuracy. In addition to these models, advanced architectures like Transformers have been utilized to achieve accurate coral reef segmentation and mapping. For example, [PERSON] et al. (2018) developed a two-step method for coral reef benthic mapping. ICESat-2 data were used to extract water depth, which was combined with Sentinel-2 multispectral data and processed using a Transformer model to generate high-accuracy benthic maps. Alongside ocean remote sensing data acquired from above the water, underwater imaging offers detailed insights into coral health, biodiversity, and benthic structures, providing a complementary perspective for coral reef studies ([PERSON] et al., 2018). Those DL models mentioned earlier also play a crucial role in analyzing underwater coral reef images ([PERSON] et al., 2018). [PERSON] et al. (2018) evaluated the performance of CNNs and Fully Convolutional Networks (FCNs) for coral reef underwater image segmentation. Their results showed that patch-based CNN methods outperformed support vector machine (SVM) classifiers, which relied on texture-based features. Among the CNN architectures tested, ResNet152 delivered the highest accuracy on the annotated coral reef dataset. For semantic segmentation using FCNs, Deeplab v2, with its atrous convolution and spatial pyramid pooling for capturing multiscale contextual information, achieved the best results for underwater coral reef segmentation. [PERSON] et al. (2020) proposed an innovative architecture with a three-branch parallel encoder framework. This framework incorporated an RGB encoder based on ResNet blocks, a Depth encoder utilizing VGG blocks, and a Fusion encoder incorporating ShapeConv blocks, achieving excellent performance in segmentation tasks. [PERSON] et al. (2003) used Structure-from-Motion photogrammetry to generate high-resolution, three-dimensional maps of underwater environments. By integrating these 3D reconstructions with semantic segmentation using U-Nets, their approach provided detailed and precise classifications of coral reef structures, greatly enhancing the study and monitoring of coral reef ecosystems. Coral bleaching, driven by environmental factors such as SST rising and ocean acidification, is an increasingly urgent concern.