基于时序Sentinel-2影像的鄱阳湖湿地典型植被群落提取  

Extraction of Typical Vegetation Communities in Poyang Lake Wetland based on Time Series Sentinel-2 Images

作　　者：李斌[1] 张爱竹[1] 孙根云[1] 潘兆杰 付航 Bin LI;Ai Zhu ZHANG;Gen Yun SUN;Zhao Jie PAN;Yang FU(College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)海洋与空间信息学院,山东青岛266580

出　　处：《遥感技术与应用》2024年第5期1271-1283,共13页Remote Sensing Technology and Application

基　　金：国家自然科学基金项目“协同多源数据的城市三维绿地多尺度热环境效应研究”(42271347);国家自然科学基金项目“复杂环境大尺度不透水面多源遥感协同机理与提取方法研究”(42371350);国家自然科学基金项目“复杂城市地表不透水面多源高分遥感成像机理与分层优化提取方法”(41971292);国家重点研发计划“亚大热点区域生态系统遥感综合监测”(2019YFE0126700)。

摘　　要：鄱阳湖湿地是国际上重要的湖泊湿地,对于整个长江流域的健康绿色发展具有重大意义。然而,近年来人类活动和全球气候变化的影响使得鄱阳湖湿地植被的群落结构和空间分布正在快速变化,生态功能面临退化的风险。因此,及时准确地掌握湿地植被群落的空间分布对于保护和修复鄱阳湖湿地具有重要意义。针对鄱阳湖湿地季节性变化的特点,以2019年时间序列Sentinel-2影像为数据源,基于GEE平台提出了一种融合统计—时间特征的随机森林分类方法,对鄱阳湖国家保护区湿地典型植被群落进行提取。研究结果表明:(1)鄱阳湖湿地植被受到周期性洪水淹没及生长物候差异的影响,植被群落在秋冬季节的差异性更为明显。(2)基于时间序列的Sentinel-2影像可以较好地实现对植被群落、滩涂和水体进行识别,其中时间特征对植被群落的识别贡献度较大。(3)基于统计—时间特征结合随机森林分类算法的湿地植被群落总体识别精度达到86.21%,Kappa系数为0.83。该方法可以及时准确地提取湿地植被群落的空间分布,在湿地研究中具有很好的应用前景,可以为鄱阳湖湿地国家自然护区湿地资源的科学获取和管理、生态环境的评价和修复提供重要的数据支撑。Poyang Lake Wetland is an internationally important lake wetland and is of great significance to the healthy and green development of the entire Yangtze River Basin.However,in recent years,the impact of human activities and global climate change has caused rapid changes in the community structure and spatial distribution of Poyang Lake wetland vegetation,and the ecological functions are at risk of degradation.Therefore,timely and accurate knowledge of the spatial distribution of wetland vegetation communities is of great significance for the protection and restoration of Poyang Lake wetland.In view of the characteristics of seasonal changes in the Poyang Lake Wetland,this study uses the 2019 time series Sentinel-2 images as the data source and proposes a random forest classification method that combines statistical and temporal features based on the GEE platform to classify the Poyang Lake National Reserve Wetland.Typical vegetation communities were extracted.The research results show that:(1)Poyang Lake wetland vegetation is affected by periodic flooding and differences in growth phenology,and the differences in vegetation communities are more obvious in autumn and winter.(2)Sentinel-2 images based on time series can better identify vegetation communities,tidal flats and water bodies,among which time features contribute greatly to the identification of vegetation communities.(3)The overall identification accuracy of wetland vegetation communities based on statistical-temporal features combined with random forest classification algorithm reaches 86.21%,and the Kappa coefficient is 0.83.The method proposed in this article can timely and accurately extract the spatial distribution of wetland vegetation communities,and has good application prospects in wetland research.It can provide scientific acquisition and management of wetland resources in Poyang Lake Wetland National Nature Reserve,evaluation of ecological environment,and Repair provides important data support.

关 键 词：Sentinel-2 植被群落 时间序列 随机森林 鄱阳湖湿地 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]