收稿：2024-11-11，
网络出版：2025-10-29，
移动端阅览
入侵植物互花米草治理是我国滨海湿地生态保护和修复的重要内容。当前，我国沿海多省陆续实施互花米草治理工程，治理方式包括物理清除和化学清除；前者起效快但易复发，后者成本低但对环境有潜在负面影响。及时监测互花米草清除动态并识别清除方式对于互花米草治理工程实施成效评价以及环境影响评价十分重要。本文以浙沪滨海湿地互花米草分布区为研究区，提出了一种基于时序光学卫星影像的互花米草清除状态和治理方式识别方法（未清除/物理清除/化学清除），并提取物理清除日期。首先融合Sentinel-2 MSI和Landsat 8 OLI影像，构建时序光谱指数数据集，采用滑动窗口和基于规则的判别方法识别治理影响时段，判断清除状态；随后利用随机森林方法对治理方式进行分类，最终得到未清除、物理清除和化学清除三种类别，并进一步识别物理清除的日期。结果表明，本方法对清除状态和清除方式的分类精度较高，总体精度达到98.8%，Kappa系数为0.979；物理清除日期检测的平均绝对误差天数为3.91天，准确度为93.67%。2023年，上海市和浙江省的互花米草清除率分别为4.2%和62.7%。本研究提出的互花米草治理方式识别和动态监测方法对于滨海湿地管理决策具有重要参考意义。
Controlling the invasive plant
Spartina alterniflora
is a crucial aspect of ecological protection and restoration in coastal wetlands in China. Over the past decades
S. alterniflora
has rapidly colonized tidal flats and estuarine areas
and led to a remarkable decline in biodiversity
degradation of wetland ecosystem services
and caused increased challenges for coastal
management. Multiple coastal provinces in China are currently implementing
S. alterniflora
control projects
adopting methods such as physical removal and chemical control. The former is effective but prone to recurrence
while the latter is cost-effective but may have negative environmental impacts. Timely monitoring of
S. alterniflora
control dynamics and identification of the control methods are crucial for evaluating the effectiveness of control projects and assessing their environmental impacts. This study focuses on the
S. alterniflora
area in the coastal wetlands of Zhejiang and Shanghai and aims to introduce a method based on time-series optical satellite imagery to identify the removal status and control methods (unremoved/physical removal/chemical control) of
S. alterniflora
and estimate the dates of physical removal.
A novel framework based on dense time-series optical satellite imagery (Sentinel-2 MSI and Landsat 8 OLI) is proposed. Specifically
a time-series spectral index dataset was constructed through the fusion of Sentinel-2 and Landsat 8 observations
followed by cloud masking and spectral harmonization to ensure consistency. First
periods that are affected by control measures were identified using a sliding window combined with a rule-based decision approach
and unremoved pixels were separated from those experiencing removal. Subsequently
a random forest classifier was trained using field survey data and high-resolution validation imagery to distinguish physical removal from chemical control. The exact removal dates for physically removed areas were further estimated by analyzing rapid decline in vegetation indexes
including NDVI
EVI
and LSWI
as well as abrupt increments in DFI.
The results indicate that this method achieved high classification accuracy for control status and control method classification
revealing an overall accuracy of 98.8% and a Kappa coefficient of 0.979. The Mean Absolute Error of estimated physical removal dates was only 3.91 days
and date recognition accuracy was 93.67%. Spatial analysis revealed substantial differences between regions: in 2023
Shanghai achieved a removal rate of 4.2%
with nearly equal proportions of physical and chemical control
while Zhejiang achieved a substantially higher removal rate of 62.7%
dominated by physical removal operations.
The proposed framework not only provides a reliable means of tracking
S. alterniflora
control projects but also facilitates differentiation of control strategies at regional scales. Such information is crucial for evaluating the effectiveness of invasive species control
supporting ecological restoration planning
and minimizing unintended environmental impacts. Furthermore
the methodology is scalable and can be extended to other coastal provinces or adapted for monitoring different invasive species across various environmental conditions.
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