# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 5 Conclusions  
The main objective of this study is to realize automatic mangrove mapping from high-resolution images acquired by Chinese GF and ZY series satellites using a deep-learning method, i.e., the attention U\({}^{2}\)-Net model. The model can extract detailed features and boundaries of mangroves in coastal areas of Guangdong Province, China, and can also focus on the regions of interest and ignore the irrelevant regions by using an attention gate. The comparison with other machine-learning or deep-learning models (RF, U-Net, and U\({}^{2}\)-Net models) shows that our model generates the highest overall accuracy, precision, and F1 score values (96.5%, 92.0%, and 91.5%, respectively). By using high-resolution satellite images, we can obtain multi-scale information about mangroves, such as their location, area, shape, and density. Based on the mangrove maps generated by the model from high-resolution GF images, we found that the area of mangroves has steadily increased from 2015 to 2018 under the influence of China's mangrove planting policy, which aims to restore and conserve mangroves in coastal areas. The total area of mangroves has increased by 17% in four years, reaching approximately 270 hm\({}^{2}\) in 2018.  
The combination of high-resolution satellite images and deep learning models helps to generate fine-scale mangrove maps, which can effectively support local authorities in mangrove planning and management. The fine-scale mangrove maps can provide useful information for assessing the status and trends of mangroves, evaluating the effectiveness of mangrove restoration and conservation efforts, and designing appropriate strategies and policies for the sustainable management of mangroves. Currently, our study is limited by the amount of collected field survey data, which are essential for training and vali-dating the attention U\({}^{2}\)-Net model. The mangrove data were mainly collected in Guangdong coastal areas, where we established and evaluated our model. Therefore, we cannot assess the applicability and generalization ability of our model in other regions with different environmental conditions. However, with the increase of in situ measurements in the near future, we expect to improve our model and test its performance in other regions with similar or different types of mangroves.  
Conceptualization, X.L. and Q.X.; methodology, X.L., Y.C. and Q.X. and X.Y.; software, Y.C. and Y.W.; validation, Y.W; formal analysis, Y.W.; investigation, J.K. and Y.C.; resources, J.K.; writing--original draft preparation, Y.W.; writing--review and editing, X.L., Q.X., Y.C., X.Y. and J.K., visualization, J.K.; supervision, Q.X., Y.C. and X.Y.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The area of mangroves from 2015 to 2018 in Maoming Shuidong Bay.  
**Funding:** This research was funded by the 1. 2022 Research Program of Sanya Yazhou Bay Science and Technology City: No. SKJC-2022-01-001; 2. National Natural Science Foundation of China: T2261149752; 3. National Natural Science Foundation of China: 41976163.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the China Center for Resources Satellite Data and Application for providing Chinese GF-1, GF-2, GF-6, and ZY-3 satellite data products. The authors thank the U.S. Geological Survey for providing DEM data. Thanks for Dr [PERSON]'s suggestions and assistance on algorithms and data processing during the revision process.  
**Conflicts of Interest:** The authors declare no conflict of interest.