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According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.<\/jats:p>","DOI":"10.3390\/rs16040654","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T11:42:05Z","timestamp":1707478925000},"page":"654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Mengna","family":"Chen","sequence":"first","affiliation":[{"name":"School of Land Engineering, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Rong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4548-899X","authenticated-orcid":false,"given":"Mingming","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Lina","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"College of Earth Sciences, Jilin University, Changchun 130061, China"}]},{"given":"Chuanpeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Huiying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-8235","authenticated-orcid":false,"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","first-page":"799","article-title":"Lake Status, Major Problems and Protection Strategy in China","volume":"22","author":"Yang","year":"2010","journal-title":"J. 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