融合Sentinel-2和GF-1时序影像的入侵植物互花米草清除动态监测

闵钰魁; 柯樱海; 韩月; 尹小岚; 周德民

doi:10.11834/jrs.20232279

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融合Sentinel-2和GF-1时序影像的入侵植物互花米草清除动态监测
Dynamic monitoring of invasive Spartina alterniflora clearance via fusion of Sentinel-2 and GF-1 time series images
2023年27卷第6期页码：1467-1479
DOI： 10.11834/jrs.20232279

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闵钰魁, 柯樱海, 韩月, 等. 融合Sentinel-2和GF-1时序影像的入侵植物互花米草清除动态监测[J]. 遥感学报, 2023,27(6):1467-1479.

MIN Yukui, KE Yinghai, HAN Yue, et al. Dynamic monitoring of invasive Spartina alterniflora clearance via fusion of Sentinel-2 and GF-1 time series images[J]. National Remote Sensing Bulletin, 2023,27(6):1467-1479.
闵钰魁, 柯樱海, 韩月, 等. 融合Sentinel-2和GF-1时序影像的入侵植物互花米草清除动态监测[J]. 遥感学报, 2023,27(6):1467-1479. DOI： 10.11834/jrs.20232279.

MIN Yukui, KE Yinghai, HAN Yue, et al. Dynamic monitoring of invasive Spartina alterniflora clearance via fusion of Sentinel-2 and GF-1 time series images[J]. National Remote Sensing Bulletin, 2023,27(6):1467-1479. DOI： 10.11834/jrs.20232279.

摘要

互花米草入侵对中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，中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10 m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—12月，研究区互花米草清除面积为4816.35 ha，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。

Abstract

Invasion of ,Spartina alterniflora, poses a serious threat to the biodiversity and ecosystem health of coastal wetlands in China. Many coastal provinces in China have initiated projects for clearance and treatment of ,S. alterniflora, in recent years. The timely and accurate understanding of ,S. alterniflora, clearance dynamics is crucial in coastal wetland management and decision making. The objective of this study was to propose a new method for monitoring ,S. alterniflora, clearance dynamics on the basis of dense time series remote sensing images.,The Yellow River Estuary wetland was taken as the study area in this work. First, Sentinel-2 MSI, GF-1 PMS, and GF-1 WFV images were fused to construct time-series Normalized Difference Vegetation Index (NDVI) dataset. Second, temporal variations of NDVI were analyzed, and the potential clearance periods were detected. Finally, tidal inundation was examined 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 a spatial resolution of 10 m was obtained for the Yellow River Estuary.,The overall accuracy of clearance dates was 88.24%, and the Kappa coefficient was 0.87. Results showed that the fusion of Sentinel-2 and GF-1 data can effectively improve the identification accuracy of clearance dates compared with the single Sentinel-2 date source. The cleared area of ,S. alterniflora, from September to December 2021 was 4816.35 ha, which accounts for 92.81% of the total ,S. alterniflora, region in the study area. 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 because of 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 stage was concluded between mid-October and mid-December, with the majority of ,S. alterniflora, being cleared in early December.,The rapid and accurate observation of the dynamics of ,S. alterniflora, clearance through the proposed method is crucial in the monitoring and evaluation of ,S. alterniflora, treatment and wetland restoration projects in coastal wetlands across the country. This method is expected to be applied to the dynamic monitoring and comprehensive evaluation of the effectiveness of large-scale treatment projects.

关键词

Sentinel-2GF-1入侵物种互花米草治理滨海湿地时间序列

Keywords

Sentinel-2GF-1invasive speciesspartina alterniflora managementcoastal wetlandtime series

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