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全文摘要次数: 81 全文下载次数: 99
引用本文:

DOI:

10.11834/jrs.20232279

收稿日期:

2022-05-29

修改日期:

2022-10-24

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基于Sentinel-2和GF-1时序影像的入侵植物互花米草清除动态监测方法
闵钰魁, 柯樱海, 韩月, 尹小岚, 周德民
首都师范大学
摘要:

互花米草入侵对我国滨海湿地生物多样性和生态系统健康造成严重威胁。近两年,我国沿海多省陆续启动互花米草清除治理工程。及时准确地了解互花米草清除动态对于滨海湿地管理决策具有重要意义。本文以黄河口湿地为研究区,针对2021年互花米草大规模治理工程,提出一种基于密集时间序列遥感影像的互花米草清除动态监测方法。首先融合Sentinel-2 MSI、GF-1 PMS和GF-1 WFV影像,构建高时空分辨率的归一化植被差异指数(NDVI)数据集。充分考虑NDVI的时序变化和潮间带潮汐淹没动态,通过潜在清除时段提取和潮汐淹没监测,识别互花米草清除日期,获取了10m分辨率下黄河口互花米草清除时间分布图,清除日期总体精度达到88.24%,Kappa系数为0.87。实验表明,相较仅使用Sentinel-2单一数据源,融合Sentinel-2和GF-1数据可以有效提升清除日期识别精度。2021年9月到12月,研究区互花米草清除面积为4816.35 公顷,占总面积的92.81%。本研究提出的入侵植物清除监测方法对于全国滨海湿地互花米草治理和湿地修复工程监测评估具有重要的参考意义。

Monitoring of invasive Spartina alterniflora clearance dynamics based on Sentinel-2 and GF-1 time series images
Abstract:

1.Objective: Invasion of Spartina alterniflora poses a serious threat to the biodiversity and ecosystem health of coastal wetlands in China. In recent years, many coastal provinces in China have initiated projects for clearance and treatment of S.alterniflora. Timely and accurate understanding of S.alterniflora clearance dynamics is of great significance for coastal wetland management and decision making. The objective of this study was to propose a new method for monitoring of S.alterniflora clearance dynamics based on dense time series remote sensing images. 2.Method: We took the Yellow River Estuary wetland as our study area. First, Sentinel-2 MSI, GF-1 PMS and GF-1 WFV images were fused to construct time-series normalized vegetation difference index (NDVI) dataset. Then, the temporal variations of NDVI were analyzed, and the potential clearance periods were detected. Afterwards, tidal inundation was analyzed and S.alterniflora clearance date was identified by eliminating the influence of tidal inundation on NDVI time series. The map of S.alterniflora clearance dates with 10 m spatial resolution were obtained for the Yellow River Estuary. 3.Result: The overall accuracy of the clearance dates is 88.24%, and the Kappa coefficient is 0.87. The results show that compared to the single Sentinel-2 date source, the fusion of Sentinel-2 and GF-1 data can effectively improve the identification accuracy of clearance date. From September to December 2021, The cleared area of S.alterniflora was 4816.35 ha, accounting for 92.81% of the total S.alterniflora area in the study area. The uncleared areas are mainly distributed in the coastal areas of the north shore with complex hydrology and interlaced tidal creeks. The project was completed in two stages due to the early October flood peak in the lower reaches of the Yellow River; the first stage was finished between early September and early October, and the second section was finished between mid-October and mid-December, with the majority of S.alterniflora being cleared in early December. 4.Conclusion The method proposed in this study can rapidly and accurately monitor the dynamics of S.alterniflora clearance, which is of great reference significance for the monitoring and evaluation of S.alterniflora treatment and wetland restoration projects in coastal wetlands across the country, and this method is expected to be applied to the dynamic monitoring and comprehensive evaluation of the effectiveness of large-scale treatment projects.

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