基于GOCI影像分类的太湖水体叶绿素a浓度日变化分析

发布时间：2018-06-24 17:35

本文选题：GOCI + 分类　；参考：《光谱学与光谱分析》2016年08期

【摘要】：叶绿素a浓度(Chlorophyll-a:Chl-a)是内陆水体重要的水质参数之一,遥感数据为其提供了大范围、多时相的监测信息,然而由于内陆湖泊水色要素复杂的光学性质及较大的时空差异,传统的遥感影像及单一的Chl-a反演模型在应用中存在着局限性。因此本研究以太湖为研究区,时间分辨率1小时的静止海洋水色卫星Geostationary Ocean Color Imager(GOCI)为数据源,在基于层次聚类法实现归一化实测光谱反射率分类的基础上,利用光谱角测距匹配实现2012年5月6日(08:16—15:16)8景GOCI太湖影像的水体分类;并针对不同水体类型分别建立基于GOCI影像的Chl-a反演模型,实现不同类型水体的Chl-a浓度反演。结果表明,太湖水体光谱可分为四类,类型1光谱体现出漂浮藻类的特征,可将其作为蓝藻水华的判定依据;类型2—4体现的特征分别为水体含有较高Chl-a浓度、较高悬浮物浓度及相对较低Chl-a较低悬浮物浓度;并且类型2—4与分类前相比,其分类模型估算的Chl-a浓度误差均得到了不同程度的提高,平均相对误差分别降低了7%,12.3%和15.9%;此外,GOCI影像反演结果不仅可以很好地反映Ch卜a浓度的空间分布状况,也能反映出太湖Chl-a浓度的日变化差异及规律,表现出了其在富营养化污染动态监测及预警中的应用潜力。该方法在GOCI影像中的应用,在提高Chl-a浓度反演精度的同时也提高了模型在实际应用中的适用性,为日后太湖水体不同时刻Chl-a浓度的精确估算提供了基础。
[Abstract]:Chlorophyll a concentration (Chlorophyll-a:Chl-a) is one of the important water quality parameters in inland waters. Remote sensing data provides a large range and multi phase monitoring information. However, due to the complex optical properties and space-time differences of the color elements of inland lakes, the traditional remote sensing image and the single Chl-a inversion model exist in its application. Therefore, in this study, Geostationary Ocean Color Imager (GOCI), a stationary marine water color satellite with time resolution of 1 hours, is used as the data source. Based on the hierarchical clustering method to classify the normalized measured spectral reflectance, the spectral angle distance matching is used to realize the 8 scenery of GOCI in May 6, 2012 (08:16 15:16). The water body of the lake image is classified and the Chl-a inversion model based on GOCI images is established for different water types. The Chl-a concentration in different types of water can be retrieved. The results show that the spectrum of Taihu water body can be divided into four categories, and type 1 spectra reflect the characteristics of floating algae, which can be used as the basis for determining the bloom of cyanobacteria; type 2 - 4 The characteristics of the water body contain higher Chl-a concentration, higher suspended substance concentration and relatively low Chl-a concentration. And type 2 - 4, compared with before classification, the Chl-a concentration error estimated by the classification model are all improved in varying degrees, and the average relative error is reduced by 7%, 12.3% and 15.9%, respectively. In addition, GOCI image inversion junction It can not only reflect the spatial distribution of Ch a concentration well, but also reflect the diurnal variation and regularity of the Chl-a concentration in Taihu, which shows its potential application in the dynamic monitoring and early warning of eutrophic pollution. The application of this method in the GOCI image can improve the accuracy of Chl-a concentration inversion while also improving the model. The applicability in practical application provides a basis for accurate estimation of Chl-a concentration at different times in Taihu.
【作者单位】：南京大学国际地球系统科学研究所;江苏省地理信息技术重点实验室;香港中文大学太空与地球信息科学研究所;江苏省地理信息资源开发与利用协同创新中心;
【基金】：“973”前期专项(2010CB434801) 国家“十二五”支撑项目(2012BAH32B03) 江苏省研究生培养创新工程立项项目(CXLX12_0040) 南京大学优秀博士研究生科研能力提升计划B项目(2014001B009)资助
【分类号】：X524;X87

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