Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery
Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery
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DOI：
10.3390/rs16040654
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发表时间：
2024-02
期刊：
影响因子：
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通讯作者：
Mengna Chen;Rong Zhang;M. Jia;Lina Cheng;Chuanpeng Zhao;Huiying Li;Zongming Wang
中科院分区：
文献类型：
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作者：
Mengna Chen;Rong Zhang;M. Jia;Lina Cheng;Chuanpeng Zhao;Huiying Li;Zongming Wang
自20世纪50年代初以来，阿穆尔河流域（CARB）中国一侧人类住区的发展和农业的过度开发对周边湖泊的水环境产生了重大影响，导致水生植被减少。根据联合国可持续发展目标，全面了解水生植被的范围和变异性对于保护稳定水生生态系统的结构和功能至关重要。目前，CARB长序列数据集在中国水生植被分布方面存在不足。这一不足妨碍了对实际管理的有效支持。因此，开发一种快速、鲁棒、自动化的水生植被精确提取方法对于大规模应用至关重要。我们的目标是收集信息的空间和时间分布，以及在CARB水生植被的变化。利用一种混合的方法，结合最大光谱指数合成和大津算法，沿着与卷积神经网络（CNN）和随机森林的集成，我们应用这种方法来获得一个年度数据集的水生植被从1985年到2020年使用Landsat系列图像。该方法的准确性通过实地调查和谷歌图片进行了验证。在评估1985年至2020年的混淆矩阵后，水生植被分类的生产者准确度始终超过87%。进一步的定量分析揭示了过去36年来CARB内面积大于20平方公里的湖泊的水域和植被面积均呈明显下降趋势。具体而言，水域总面积从3575平方公里减少到3412平方公里，而植被面积从745平方公里减少到687平方公里。这些变化可能是气候变化和人类活动共同作用的结果。这些定量数据对建立湖泊水生植被的科学恢复途径具有重要的现实意义。它们对于构建水生植被的历史背景和参考指数具有重要的参考价值。
Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. 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.