Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries
The occurrence of Ulva prolifera (U. prolifera) can cause significant environmental damage in coastal areas, making its monitoring crucial. Remote sensing technology provides an effective tool for large-scale monitoring of U. prolifera. Most studies rely on optical images to monitor U. prolifera, wh...
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000482 |
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| author | Yanxia Wang Xiaoyu Ni Xiaoshuang Ma |
| author_facet | Yanxia Wang Xiaoyu Ni Xiaoshuang Ma |
| author_sort | Yanxia Wang |
| collection | DOAJ |
| description | The occurrence of Ulva prolifera (U. prolifera) can cause significant environmental damage in coastal areas, making its monitoring crucial. Remote sensing technology provides an effective tool for large-scale monitoring of U. prolifera. Most studies rely on optical images to monitor U. prolifera, which are highly dependent on weather conditions. Synthetic Aperture Radar (SAR) can penetrate clouds, rain, and fog, providing clear observations of ocean surfaces in a large scale regardless of time of day. However, current research on SAR data for U. prolifera detection primarily focuses on SAR intensity or amplitude information, while its rich polarimetric data remains underutilized. This paper presents U. prolifera Detection Network (UDNet), an intelligent detection framework based on the DeepLabV3+ deep learning model, leveraging amplitude and polarimetric information from Sentinel-1 dual-polarimetric imageries. To construct the proposed model, 2283 samples were annotated using SAR images of the Yellow Sea, of which 1737 samples were used for training and 546 samples were used for validation and testing. The well-trained model was used to detect U. prolifera in a typical coastal area from 2018 to 2021. The experimental results demonstrate that the proposed UDNet achieves superior performance with an overall accuracy of 0.9859, a mean intersection over union of 0.9198, and an F1 score of 0.9239. Spatio-temporal distribution analyses indicate that the most severe outbreak of U. prolifera in the study area occurred in 2019, with intensive occurrences in June of each year. The outbreak was more severe in the southwest region of the study area than in the northeast. Besides, it was observed that the outbreak area of U. prolifera was larger at night than that during the day, mainly driven by changes in summer temperature. In addition, a larger diurnal temperature difference generally promoted the growth of U. prolifera. These findings are instrumental in formulating management policies and taking actions to control the outbreak of U. prolifera. |
| format | Article |
| id | doaj-art-9f8a776064bf40d4819f99400bc8fe17 |
| institution | OA Journals |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | Ecological Informatics |
| spelling | doaj-art-9f8a776064bf40d4819f99400bc8fe172025-08-20T02:04:00ZengElsevierEcological Informatics1574-95412025-05-018610303910.1016/j.ecoinf.2025.103039Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageriesYanxia Wang0Xiaoyu Ni1Xiaoshuang Ma2College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China; School of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, China; Corresponding author.The occurrence of Ulva prolifera (U. prolifera) can cause significant environmental damage in coastal areas, making its monitoring crucial. Remote sensing technology provides an effective tool for large-scale monitoring of U. prolifera. Most studies rely on optical images to monitor U. prolifera, which are highly dependent on weather conditions. Synthetic Aperture Radar (SAR) can penetrate clouds, rain, and fog, providing clear observations of ocean surfaces in a large scale regardless of time of day. However, current research on SAR data for U. prolifera detection primarily focuses on SAR intensity or amplitude information, while its rich polarimetric data remains underutilized. This paper presents U. prolifera Detection Network (UDNet), an intelligent detection framework based on the DeepLabV3+ deep learning model, leveraging amplitude and polarimetric information from Sentinel-1 dual-polarimetric imageries. To construct the proposed model, 2283 samples were annotated using SAR images of the Yellow Sea, of which 1737 samples were used for training and 546 samples were used for validation and testing. The well-trained model was used to detect U. prolifera in a typical coastal area from 2018 to 2021. The experimental results demonstrate that the proposed UDNet achieves superior performance with an overall accuracy of 0.9859, a mean intersection over union of 0.9198, and an F1 score of 0.9239. Spatio-temporal distribution analyses indicate that the most severe outbreak of U. prolifera in the study area occurred in 2019, with intensive occurrences in June of each year. The outbreak was more severe in the southwest region of the study area than in the northeast. Besides, it was observed that the outbreak area of U. prolifera was larger at night than that during the day, mainly driven by changes in summer temperature. In addition, a larger diurnal temperature difference generally promoted the growth of U. prolifera. These findings are instrumental in formulating management policies and taking actions to control the outbreak of U. prolifera.http://www.sciencedirect.com/science/article/pii/S1574954125000482Ulva proliferaCoastal areasSynthetic aperture radarDeep learning |
| spellingShingle | Yanxia Wang Xiaoyu Ni Xiaoshuang Ma Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries Ecological Informatics Ulva prolifera Coastal areas Synthetic aperture radar Deep learning |
| title | Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries |
| title_full | Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries |
| title_fullStr | Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries |
| title_full_unstemmed | Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries |
| title_short | Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries |
| title_sort | identification and spatio temporal analysis of ulva prolifera in a typical coastal area using sar imageries |
| topic | Ulva prolifera Coastal areas Synthetic aperture radar Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000482 |
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