基于密集时间序列Sentinel数据的湖滨湿地分布动态监测研究——以鄱阳湖为例
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国家自然科学基金重点项目(42330109)


Dynamic monitoring of lakeshore wetland distribution based on dense time series Sentinel data: taking Poyang Lake as an example
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    摘要:

    湖滨湿地受湖泊水位动态变化影响显著,具有范围易变、变化程度和频率时空差异明显等特点,湖滨湿地的精准监测对于全球变化背景下湖滨生态系统的保护与管理具有重要的意义。以受气候变化和人类活动等影响显著的通江湖泊--鄱阳湖为例,利用GEE平台支持下的密集时间序列Sentinel雷达和光学数据,利用OTSU算法,提出了基于淹水频率的湖滨湿地空间范围遥感监测方法,并构建了2019-2022年鄱阳湖密集时间序列水体和植被数据集,结果表明:(1)构建的基于淹水频率的湖滨湿地提取方法能够有效界定湖滨湿地与湖泊水体之间的范围,为大尺度湖滨湿地监测提供了重要的方法参考。(2)鄱阳湖水体面积季节性波动显著,2019-2022年间由夏季汛期峰值退至枯水警戒水位的退水速率逐年加快,湿地水文周期缩短;湖滨湿地面积分别为3075.83km2、2726.28km2、2953.91km2、3331.03km2,呈波动上升趋势。(3)湖滨湿地植被的生长发育受淹水状态的显著影响,极端干旱气候下,0-20%的淹水频率对植被表现为非抑制作用,高植被覆盖频率沼泽面积显著增加。研究有助于深入了解湖滨湿地水文、植被的时空变化及其动态响应关系,为湖泊水位调控、湖滨湿地的保护与修复提供科学依据。

    Abstract:

    Lakeshore wetlands are an important component of inland wetlands globally, providing ecosystem services such as water purification, maintenance of biodiversity and climate regulation. Delineation of the dynamic borders of wetlands is the basis of wetland research and a key prerequisite for wetland ecosystem conservation, restoration and rehabilitation. Lakeshore wetlands are significantly affected by the dynamic changes of lake water level, characterized by variable range, obvious spatial and temporal differences in the degree and frequency of changes, etc. Moreover, the extent between lakeshore wetlands and lake water bodies is usually difficult to define. Improving the accuracy of lakeshore wetland monitoring is of great significance for the protection and management of lakeshore ecosystems in the context of global change. Massive, Multi-source Remote Sensing Data Provides Reliable Data Source for Accurate Monitoring of Lakeshore Wetlands. Taking Poyang Lake which connect the Yangtze River and is significantly affected by climate change and water conservancy activities as case study area, this study proposes a remote sensing monitoring method for the spatial extent of the lakeshore zone based on the inundation frequency by utilizing dense time-series Sentinel radar and optical data with the support of the GEE platform and OTSU algorithms. We constructed the Poyang Lake dense time series water body and vegetation dataset during 2019-2022. Results show that: the extraction method of lakeshore wetland based on inundation frequency developed in this paper can effectively define the range between lakeshore belt and lake water body, which provides an important methodological reference for monitoring lakeshore zone at a broad scale. The seasonal fluctuation in the water body area of Poyang Lake was remarkable. The receding rate from the peak of summer flood season to the dry water warning level was accelerated year by year from 2019 to 2022, whilst the hydrological cycle of wetland was shortened. The wetland area of lakeshore zone was 3075.83km2,2726.28km2,2953.91km2 and 3331.03km2 suggesting an overall increasing trend. The growth and development of vegetation in the lakeshore zone were strongly affected by the inundation state. Under the extreme arid climate, the water with inundation frequency of 0-20% showed a non-inhibitory effect on the vegetation, while the area of the region with high frequency of vegetation cover increased significantly. This study helps to deeply understand the spatial and temporal changes of hydrology and vegetation in the lakeshore zone and its dynamic response relationship and could provide a scientific basis for the regulation of lake water level and the protection and restoration of wetlands in the lakeshore zone.

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陈亚杰,王宗明,毛德华.基于密集时间序列Sentinel数据的湖滨湿地分布动态监测研究——以鄱阳湖为例.生态学报,2025,45(2):716~729

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