基于时序Sentinel-2数据水体动态变化监测——以河南省为例  

Monitoring of dynamic changes in water bodies of HenanProvince based on time-series Sentinel-2 data

作　　者：魏鑫 任雨 陈曦东 胡青峰 刘辉 周婧 宋冬伟 张培佩 黄志全 WEI Xin;REN Yu;CHEN Xidong;HU Qingfeng;LIU Hui;ZHOU Jing;SONG Dongwei;ZHANG Peipei;HUANG Zhiquan(College of Surveying,Mapping and Geographic Information,North China University of Water Resources and Electric Power,Zhengzhou 450045,China;College of Geography and Environmental Sciences,Northwest Normal University,Lanzhou 730070,China;Seventh Geological Brigade,Henan Nonferrous Metals Geological and Mineral Bureau,Zhengzhou 450045,China;Henan Surveying and Mapping Institute,Zhengzhou 450045,China;Luoyang Institute of Science and Technology,Luoyang 471023,China)

机构地区：[1]华北水利水电大学测绘与地理信息学院,郑州450045 [2]西北师范大学地理与环境科学学院,兰州730070 [3]河南有色金属地质矿产局第七地质大队,郑州450045 [4]河南省测绘院,郑州450045 [5]洛阳理工学院,洛阳471023

出　　处：《自然资源遥感》2024年第2期268-278,共11页Remote Sensing for Natural Resources

基　　金：河南省高等学校青年骨干教师培养计划项目“融合多源异质数据的南水北调渠道边坡InSAR三维形变测量”(编号:2021GGJS073);中原科技创新领军人才计划资助项目“膨胀土灌渠边坡亲水响应机理研究”(编号:214200510030)共同资助。

摘　　要：内陆水体作为生态系统中不可替代的资源,在气候变化、区域水循环等诸多方面起着至关重要的作用。科学准确地监测水体分布与动态变化,对维持生态系统平衡、人类可持续发展、水旱灾害预警等方面具有重要意义。然而,目前针对内陆水体的研究多以静态监测为主,对于高分辨率水体动态变化监测研究仍较为匮乏。因此,研究依托Google Earth Engine(GEE)云计算平台,以2020年的Sentinel-2地表反射率数据为数据源,进行10 m空间分辨率水体动态变化监测研究。首先,研究基于典型地表覆盖类型在Sentinel-2不同波段及水体指数中的表现特征,选取最优的水体监测特征;其次,研究结合先验水体产品提出一种水体训练数据集的自动提取方法,来获取高置信度水体训练样本;再次,依据D-S(Dempster-Shafer)证据理论模型将光谱角距离与欧式距离相融合,并结合所提取的最优监测特征提出一种SA-ED(spectral angle-Euclidean distance)水体动态监测模型;最后,以河南省为例对算法的稳定性进行测试。结果表明,本研究所提SA-ED模型能够有效对水体的动态变化进行监测,基于SA-ED算法对河南省内水体的整体监测精度达到97.03%,对于稳定性水体用户精度和生产者精度分别为95.85%和95.17%,季节性水体的用户精度和生产者精度分别为96.21%和93.82%。研究成果可为精细分辨率的水体动态变化监测提供新的借鉴与思路。Inland water bodies,as irreplaceable resources in ecosystems,play a vital role in climate change and regional water circulation.Scientifically and accurately monitoring the distribution and dynamic changes of water bodies is critical for ecosystem balance maintenance,sustainable human development,and early warning of floods and droughts.However,current research primarily focuses on the static monitoring of inland water bodies,lacking high-resolution monitoring of dynamic changes in water bodies.Hence,relying on the Google Earth Engine(GEE)cloud computing platform,this study monitored the dynamic changes of water bodies at a spatial resolution of 10 m,with the Sentinel-2 surface reflectance data in 2020 as the data source.First,the optimal water body monitoring features were selected by examining the features of typical land cover types in Sentinel-2 spectral bands and water indices.Then,an automatic extraction method for water body training datasets was proposed in conjunction with priori water body products,obtaining high-confidence water body training samples.Furthermore,the spectral angle(SA)and Euclidean distance(ED)methods were integrated based on the Dempster-Shafer(D-S)evidence theory model,and a SA-ED dynamic monitoring model for water bodies was developed combined with the extracted optimal water body monitoring features.Finally,the stability of the SA-ED model was tested with Henan Province as a study area,demonstrating that the SA-ED model can effectively monitor the dynamic changes in water bodies.The SA-ED model yielded an overall monitoring accuracy of 97.03%for water bodies in Henan Province,with user accuracy of 95.85%and producer accuracy of 95.17%for permanent water bodies,user and producer accuracies of 96.21%and 93.82%for seasonal water bodies,respectively.The results of this study provide a novel approach for the fine-resolution monitoring of dynamic changes in water bodies.

关 键 词：内陆水体 水体分布 动态监测 Google Earth Engine Sentinel-2 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]