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A method for monitoring hydrological conditions beneath herbaceous wetlands using multi-temporal ALOS PALSAR coherence data

机译:一种基于多时相ALOS PALSAR相干数据的草本湿地水文条件监测方法

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摘要

Reed marshes, the world's most widespread type of wetland vegetation, are undergoing major changes as a result of climate changes and human activities. The presence or absence of water in reed marshes has a significant impact on the whole ecosystem and remains a key indicator to identify the effective area of a wetland and help estimate the degree of degeneration. Past studies have demonstrated the use of interferometric synthetic aperture radar (InSAR) to map water-level changes for flooded reeds. However, the identification of the different hydrological states of reed marshes is often poorly understood. The analysis given in this paper shows that L-band interferometric coherence is very sensitive to the water surface conditions beneath reed marshes and so it can be used as classifier. A method based on a statistical analysis of the coherence distributions for wet and dry reeds using InSAR pairs was, therefore, investigated in this study. The experimental results were validated by in-situ data and showed very good agreement. This is the first time that information about the water cover under herbaceous wetlands has been derived using interferometric coherence values. This method can also effectively and easily be applied to monitor the hydrological conditions beneath other herbaceous wetlands.
机译:芦苇沼泽是世界上最广泛的湿地植被类型,由于气候变化和人类活动而正在发生重大变化。芦苇沼泽中是否有水对整个生态系统都有重要影响,并且仍然是确定湿地有效面积并帮助估计退化程度的关键指标。过去的研究表明,使用干涉式合成孔径雷达(InSAR)来绘制淹没芦苇的水位变化图。但是,人们对芦苇沼泽的不同水文状态的识别往往知之甚少。本文给出的分析表明,L波段干涉相干对芦苇沼泽下的水面条件非常敏感,因此可以用作分类器。因此,在这项研究中,研究了一种基于统计分析的干芦苇干和干芦苇分布的方法。实验结果通过现场数据验证,显示出很好的一致性。这是首次使用干涉相干值获得有关草本湿地下水覆盖的信息。该方法还可以有效而轻松地应用于监测其他草本湿地下的水文条件。

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  • 来源
    《Remote sensing letters》 |2015年第9期|618-627|共10页
  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

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  • 入库时间 2022-08-17 13:48:21

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