不同大气校正方法对中小湖泊蓝藻遥感动态监测的影响  

Comparing Effects of Different Atmospheric Correction Algorithms in Remote Sensing Dynamic Monitoring of CyanoBacteria Bloom in Inland Lakes

作　　者：王嘉楠[1] 叶勤[1,2] 林怡[1,2] 

机构地区：[1]同济大学测绘与地理信息学院,上海200092 [2]同济大学遥感与空间信息技术研究中心,上海200092

出　　处：《遥感技术与应用》2013年第1期157-165,共9页Remote Sensing Technology and Application

基　　金：科技部中加国际合作项目(2009DFA92310);上海市科委重大科技攻关项目(10DZ1200903)

摘　　要：环境小卫星可实现中小湖泊蓝藻动态监测,但不同大气校正方法对于相同影像的处理结果有很大差异。研究利用多种大气校正方法对环境小卫星影像进行辐射校正处理,利用多个感兴趣区的全局和局部特征以及多个统计量对处理结果进行分析,比较其在蓝藻动态监测中的作用。基于多光谱植被指数计算蓝藻生物量的思路,分析了影像经不同大气校正算法处理后,其归一化植被指数的差异性来源及其对蓝藻生物量计算结果的影响。结果表明:蓝藻动态监测的定量描述会因大气校正算法不同而不一致,进而对几种大气校正算法在蓝藻生物量监测和定量分析中所产生的不确定性进行了比较分析,并对各算法的有效利用提出了建议。Cyanobacteria bloom in relatively smaller and medium sized inland lakes can be detected by H J-1 satellite more efficiently. However, various types of results will be retrieved from the same remote image due to different atmospheric correction algorithms. Three typical atmospheric correction algorithms are ex- perimented in this study to implement the radiometric correction of multi-temporal remote images, and then acquired global and local features in several regions of interest as well as statistics, in order to compare their effects in the dynamic monitoring of cyanobacteria bloom. Because biomass can be calculated by vege- tation indices obtained through multi-spectral arithmetic, the study utilized that method and analyzed both the source of diversities of NDVIs and the impacts on the estimate of cyanobacteria biomass. The results demonstrate quantitative descriptions of cyanobacteria bloom are incongruous because of different correc- tion algorithms. Then the study specifies and characterizes the uncertainties generated by the experimented correction algorithms, and presents recommendations on how to utilize these algorithms more effectively.

关 键 词：大气校正 湖泊 蓝藻水华 遥感 动态监测 归一化植被指数 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]