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1.广东省水利水电科学研究院 / 广东省水动力学应用研究重点实验室,广东广州 510635
2.中山大学地理科学与规划学院,广东 广州 510275
Received:29 October 2024,
Accepted:20 January 2025,
Published Online:02 April 2025,
Published:25 May 2025
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郑泳,冯明薇,蔡季宏等.基于地物时空关系的水浮莲遥感监测方法[J].中山大学学报(自然科学版)(中英文),2025,64(03):74-82.
ZHENG Yong,FENG Mingwei,CAI Jihong,et al.The remote sensing monitoring method of water hyacinth based on geographical spatiotemporal relationship[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(03):74-82.
郑泳,冯明薇,蔡季宏等.基于地物时空关系的水浮莲遥感监测方法[J].中山大学学报(自然科学版)(中英文),2025,64(03):74-82. DOI: 10.13471/j.cnki.acta.snus.ZR20240313.
ZHENG Yong,FENG Mingwei,CAI Jihong,et al.The remote sensing monitoring method of water hyacinth based on geographical spatiotemporal relationship[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(03):74-82. DOI: 10.13471/j.cnki.acta.snus.ZR20240313.
水浮莲泛滥会对区域水安全和水生态造成严重危害,及时、精准地监测水浮莲分布状况是水浮莲治理的基础。为提升水浮莲遥感监测的效率、精度和适用性,本研究提出了一种基于地物时空关系的水浮莲遥感监测方法。该方法在提取植被范围的基础上,通过计算并比较长时序光学遥感影像中植被和水体光谱指数的关系变化排除陆生植被影响,从而实现水浮莲像元的快速提取。本研究以广东省鉴江流域为研究区验证该方法的有效性,并分析了2022年流域片区的水浮莲分布特征。结果表明,该方法对水浮莲识别效果较好,准确率和召回率分别达到92.9%和86.7%。2022年3~5月期间鉴江流域主要河道存在多处水浮莲高密度聚集区,水浮莲面积总体呈先增大后减小的趋势。本方法可为大面积水浮莲的准确监测和精准防治提供技术支撑。
The proliferation of water hyacinth poses serious threats to regional water security and ecological balance. Timely and accurate monitoring of the distribution of water hyacinth is fundamental to its management. To enhance the efficiency,accuracy,and applicability of remote sensing monitoring for water hyacinth,this study proposes a remote sensing monitoring method based on the spatiotemporal relationships of land cover. This method extracts the vegetation range and eliminates the influence of terrestrial vegetation by calculating and comparing the changes in the spectral index relationships of vegetation and water bodies in long-term optical remote sensing images,thereby enabling the rapid extraction of water hyacinth pixels. The effectiveness of this method is validated in the Jianjiang River Basin,and the distribution characteristics of water hyacinth in the basin for the year 2022 are analyzed. The results indicate that this method performs well in identifying water hyacinth,achieving an accuracy of 92.9% and a recall rate of 86.7%. From March to May in 2022,several high-density aggregation areas of water hyacinth were observed in the main rivers of the Jianjiang River Basin,with the overall area of water hyacinth showing a trend of initial increasing and then decreasing. This method provides valuble technical support for the accurate monitoring and precise control of large-scale water hyacinth.
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