中低分辨率卫星蓝藻水华监测面积校正研究:以GOCI-2为例  

Research on Area Correction for Monitoring Cyanobacterial Bloom with Medium and Low Resolution Satellites:Taking GOCI-2 as an Example

作　　者：王雅萍[1] 徐喜飞 李家国 陈兴峰 张宁 陈华杰 赵利民 刘军 WANG Yaping;XU Xifei;LI Jiaguo;CHEN Xingfeng;ZHANG Ning;CHEN Huajie;ZHAO Limin;LIU Jun(School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China;Aerospace Information Research Institute,Chinese Academy of Science,Beijing 100094,China;China Academy of Urban Planning and Design,Beijing 100044,China;Satellite Application Center for Ecology and Environment,Beijing 100094,China)

机构地区：[1]河南理工大学测绘与国土信息工程学院,焦作454003 [2]中国科学院空天信息创新研究院,北京100094 [3]中国城市规划设计研究院,北京100044 [4]生态环境部卫星环境应用中心,北京100094

出　　处：《航天返回与遥感》2024年第5期123-133,共11页Spacecraft Recovery & Remote Sensing

基　　金：国家自然科学基金项目(41971391);河南省科技攻关项目(No.232102210043);高分辨率对地观测系统重大专项(30-Y60B01-9003-22/23);河南理工大学青年骨干教师资助项目(2023XQG-12)。

摘　　要：面对中低分辨率影像因监测尺度较低而导致其估算湖泊蓝藻水华面积精度较差这一问题,文章以GK 2B卫星上搭载的多光谱成像设备GOCI-2为例,选取太湖研究区,分析了GOCI-2与Sentinel-2卫星遥感影像水华面积提取结果的差异,研究了GOCI-2混合像元中NDVI值与水华面积占比的关系,并进行回归分析;以此为基础构建了面向GOCI-2的水华面积校正模型,并对该模型进行精度验证和比对分析。试验结果表明:混合像元中水华面积占比与NDVI值非线性正相关,直接用NDVI阈值法提取的蓝藻水华面积比实际值偏大;经模型校正后GOCI-2水华面积监测平均精度由67.8%提升到90.0%,且校正模型对NDVI阈值设定不敏感;与EVI、AFAI水华提取算法相比,NDVI能更好的反映出水华在混合像元中的占比。文章的研究成果可为GOCI-2影像在水华监测领域的应用提供一定参考。In the face of the problem that the accuracy of cyanobacteria bloom area in lakes estimated with low and medium resolution images is poor due to the low monitoring scale,this paper took GOCI-2 as an example,selected the Taihu Lake as research area,compared the difference in cyanobacteria bloom area extracted from GOCI-2 and Sentinel-2,researched the relationship between the NDVI of GOCI-2 and the proportion of cyanobacteria bloom area in mixed pixels and conducted regression analysis.Based on this,a corrected model of the cyanobacteria bloom area for GOCI-2 was established.The accuracy of the model was compared and analyzed.The results showed that:a non-linear positive correlation was found between the proportion of cyanobacterial blooms area and NDVI value in mixed pixels,and the area of cyanobacterial blooms extracted directly using the NDVI threshold method was larger than the actual value.After correction by this model,the average accuracy of GOCI-2 cyanobacterial blooms area monitoring was improved from 67.8%to 90.0%.And the model in this article was not sensitive to the changes in NDVI threshold settings.NDVI was found to better reflect the proportion of cyanobacterial blooms in mixed pixels,compared with the algorithm of EVI and AFAI which were used to extract cyanobacterial bloom.The research results of this paper can provide valuable reference for the application of GOCI-2 image in the field of cyanobacteria bloom monitoring.

关 键 词：蓝藻水华面积 混合像元 GOCI-2影像 Sentinel-2 MSI影像 校正模型 

分 类 号：V19[航空宇航科学与技术—人机与环境工程] P237[天文地球—摄影测量与遥感]