环境保护科学2025，Vol.51Issue(3)：112-119,8.DOI:10.16803/j.cnki.issn.1004-6216.202408039
基于Planet卫星的城市建成区黑臭水体解译研究
Interpretation of urban black-odorous water in urban built-up areas based on Planet satellite
摘要
Abstract
To grasp the distribution of black-odorous water bodies in urban built-up areas in a timely and accurate manner is of great significance for improving the ecological environment and enhancing the quality of residents'lives.Taking the water bodies in the urban built-up area of Bazhou,a central city in Hebei Province,as the research object,based on the band characteristics of Planet satellites,five remote sensing identification models for black-odorous water bodies were selected.Combined with sample data,the threshold values of the algorithm models were revised,and the accuracy was verified.The results showed that the maximum difference between black-odorous water bodies and normal water bodies was near 865 nm,and the maximum difference in the visible light band was near 565 nm.Regional differences and different types of satellite sensors led to the instability of the threshold values of the black-odorous water body identification models.Through model threshold revision,the five models could effectively identify black-odorous water bodies in Bazhou.The black-odorous water body index model(HCI1)had the highest correct identification rate for black-odorous water body points.Through modeling analysis,it was determined that water bodies with HCI1 threshold values between 1,324 and 6,049 were black-odorous water bodies,and the correct identification rate was as high as 92.2%.The research results could provide a methodological reference for the interpretation of black-odorous water bodies based on Planet satellites in the future.关键词
黑臭水体/Planet卫星/遥感识别/模型修正Key words
black-odorous water/Planet satellite/remote sensing identification/model revision分类
环境科学引用本文复制引用
王春博,孙庆松,高鹏远,范文浩,贺磊,刘伟,左伟昆,赵萌..基于Planet卫星的城市建成区黑臭水体解译研究[J].环境保护科学,2025,51(3):112-119,8.基金项目
河北省一般公共预算财政资金项目(13000023P00B044100399) （13000023P00B044100399）
"三三三人才工程"人才培养资助(C20221056) （C20221056）
