Climate Change Data Portal
| DOI | 10.3390/jmse12050709 |
| Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data | |
| Lemenkova, Polina | |
| 发表日期 | 2024 |
| EISSN | 2077-1312 |
| 起始页码 | 12 |
| 结束页码 | 5 |
| 卷号 | 12期号:5 |
| 英文摘要 | This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. |
| 英文关键词 | functional algorithm; risk assessment; ensemble learning; hazards; climate change; GRASS GIS; scikit-learn; machine learning; Python; Landsat; image analysis; China; classification |
| 语种 | 英语 |
| WOS研究方向 | Engineering ; Oceanography |
| WOS类目 | Engineering, Marine ; Engineering, Ocean ; Oceanography |
| WOS记录号 | WOS:001232242300001 |
| 来源期刊 | JOURNAL OF MARINE SCIENCE AND ENGINEERING
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/304747 |
| 作者单位 | Salzburg University |
| 推荐引用方式 GB/T 7714 | Lemenkova, Polina. Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data[J],2024,12(5). |
| APA | Lemenkova, Polina.(2024).Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data.JOURNAL OF MARINE SCIENCE AND ENGINEERING,12(5). |
| MLA | Lemenkova, Polina."Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data".JOURNAL OF MARINE SCIENCE AND ENGINEERING 12.5(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Lemenkova, Polina]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Lemenkova, Polina]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Lemenkova, Polina]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
