National Remote Sensing Bulletin（2023）
城市环境过程与数字模拟国家重点实验室培育基地 | 水资源安全北京实验室 | 首都师范大学
Abstract
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 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—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.
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Key words
Sentinel-2,GF-1,invasive species,spartina alterniflora management,coastal wetland,time series
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