Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin
文献类型：期刊论文
| 作者 | Li, Kai2,3; Wang, Juanle2,4; Cheng, Wenjing5,6; Wang, Yi7; Zhou, Yezhi2,3; Altansukh, Ochir1 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
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| 出版日期 | 2022-08-01 |
| 卷号 | 112页码:12 |
| 关键词 | Water extraction Deep learning Noise correction Quality assessment bands Google Earth Engine Lake Baikal |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2022.102928 |
| 通讯作者 | Wang, Juanle(wangjl@igsnrr.ac.cn) |
| 英文摘要 | Studying the spatial and temporal water distribution in the Lake Baikal Basin, which hosts the freshwater lake with the most water storage in the world, is essential to understand the water resources and environment of the basin and its impact and influence in terms of climate change and disaster prevention and mitigation. The basin spans two countries, Russia and Mongolia, which, along with its vastness, makes it challenging to accurately automate the acquisition of large-scale and long-term series data. The Google Earth Engine (GEE) is capable of processing large amounts of remote sensing imagery but does not support the computation and application of deep learning models. This study uses a combination of local deep learning training and GEE cloud-based big data intelligent computing to empower GEE with deep learning computing power, enabling it to rapidly automate the deployment of deep learning models. Visible light, near infrared (NIR), modified normalized difference water index (MNDWI), short-wave infrared 1 (SWIR1), linear enhancement band (LEB), and digital elevation model (DEM), which are more sensitive to water bodies, were selected as input features, along with the optimized input features of the existing pixel-based convolutional neural network (CNN) model. This method corrects the initial water labels from the Landsat quality assessment bands to reduce the time cost of manually drawing the labels and improving the classification accuracy of the water bodies. On average, only 1-2 h are required to generate the results for each water body product for each period in Lake Baikal Basin. The extraction of water bodies from the Lake Baikal Basin was achieved for nine yearly periods between 2013 and 2021. The validation accuracy was 92.9 %, 92.7 %, and 92.4 % for the three years 2013, 2017 and 2021, respectively. The results showed that the mean area of water bodies in the basin was 37,500 km2 and that the area of water bodies in the basin fluctuated without significant change from 2013 to 2021. This study provides methodological support for the continuous monitoring and assessment of water body dynamics at more catchment scales and other large scenarios. |
| WOS关键词 | INDEX NDWI ; ZONE |
| 资助项目 | National Natural Science Foundation of China[32161143025] ; Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences[XDA20030200] ; Mongolian Foundation for Science and Technology ; National University of Mongolia[P2020- 3779] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000844332200004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences ; Mongolian Foundation for Science and Technology ; National University of Mongolia |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/182323]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Natl Univ Mongolia, Dept Environm & Forest Engn, Ulaanbaatar 210646, Mongolia 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 5.Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China 6.Chinese Acad Meteorol Sci Training Ctr, Beijing 100081, Peoples R China 7.Natl Sci & Technol Infrastruct Ctr, Beijing 100862, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Kai,Wang, Juanle,Cheng, Wenjing,et al. Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,112:12. |
| APA | Li, Kai,Wang, Juanle,Cheng, Wenjing,Wang, Yi,Zhou, Yezhi,&Altansukh, Ochir.(2022).Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,112,12. |
| MLA | Li, Kai,et al."Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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