基于海岸带高光谱成像仪影像的太湖蓝藻水华和水草识别

doi:10.11873/j.issn.1004-0323.2016.5.0879

遥感技术与应用

2016, Vol. 31

Issue (5): 879-885 DOI: 10.11873/j.issn.1004-0323.2016.5.0879

模型与反演

基于海岸带高光谱成像仪影像的太湖蓝藻水华和水草识别

朱庆^1,2,李俊生²,张方方²,申茜²,林卉¹,王李娟¹,朱琳³

(1.江苏师范大学测绘学院,江苏徐州 221116;
2.中国科学院遥感与数字地球研究所数字地球重点实验室,北京 100094;
3.首都师范大学资源环境与旅游学院,北京 100048)

Distinguishing Cyanobacteria Bloom and Aquatic Plants in Lake Taihu based on Hyperspectral Imager for the Coastal Ocean Images

Zhu Qing^1,2,Li Junsheng²,Zhang Fangfang²,Shen Qian²,Lin Hui¹,Wang Lijuan¹,Zhu Lin³

(1.College of Geodesy and Geomatics,Jiangsu Normal University,Xuzhou 221116,China;
2.Key Laboratory of Digital Earth,Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences,Beijing 100094,China;
3.Resource Environment and Tourism College,Capital Normal University,Beijing 100048,China)

全文: PDF(3720 KB)

摘要：

水华与水草的同步监测对于研究湖泊水环境、生态特性以及水循环具有重要意义,相对于传统监测方法如实地调查,利用遥感手段具有大范围、长时间周期、高效率以及低成本等优势。基于海岸带高光谱成像仪HICO(Hyperspectral Imager for the Coastal Ocean)影像,利用叶绿素a光谱指数和藻蓝蛋白基线的水华和水草识别模型,提取太湖水华和水草分布图,经过检验,水华和水草的平均提取精度为93%和95%;通过水华和水草分布图的叠加分析了2010~2014年太湖水华和水草的分布规律,与相关文献的分析结果一致,进一步证实了识别方法的可靠性;将平均阈值与最佳阈值进行对比分析,提取水华与水草面积的精度分别为75.7%和84%,在对精度要求不高但对效率要求较高的情况下,可以利用平均阈值提取水华和水草,便于实现水华和水草的自动化提取及批处理。

关键词： 太湖; 水华; 水草; 叶绿素a光谱指数; 藻蓝蛋白基线

Abstract:

The synchronous monitoring for the cyanobacteria bloom and aquatic plants is of great significance for the study of lake water environment、ecology and the water cycle.Compared with traditional monitoring methods,for instance,field investigation,using remote sensing technology with the advantages of large scope,long cycle,high efficiency and low cost.A model based on “Chlorophyll a Spectral Index” and “Baseline of Phycocyanin” was built to distinguish cyanobacteria bloom and aquatic plants in Lake Taihu by using Hyperspectral Imager for the Coastal Ocean (HICO) images.The average accuracy of cyanobacteria bloom and aquatic plants are 93% and 95% respectively.By overlapping the distribution maps of cyanobacteria bloom and aquatic plants,the distribution rules of cyanobacteria bloom and aquatic plants in Lake Taihu from 2010 to 2014 were analyzed,which are consistent with the former results in the literatures.The average thresholds were used to extract cyanobacteria bloom and aquatic plants,and the accuracy are 75.7% and 84.0% respectively.If the efficiency is more desired than accuracy,then average thresholds can be used to extract cyanobacteria bloom and aquatic plants.Which is convenient for realizing batch processing and the automation extraction of cyanobacteria bloom and aquatic plants.

Key words: Lake Taihu Cyanobacteria bloom Aquatic plants Chlorophyll a spectral index Baseline of phycocyanin

收稿日期: 2015-07-06 出版日期: 2016-11-25

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基金资助:

国家自然科学基金项目“基于软分类的太湖水体叶绿素a浓度反演与时空变化分析”（41471308）。

通讯作者: 李俊生（1979-），男，吉林吉林人，博士，副研究员，主要从事水环境遥感研究。Email:lijs@radi.ac.cn。

作者简介: 朱庆(1990-)，男，江苏南京人，硕士研究生，主要从事水华水草识别研究。Email:15190660592@163.com。

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引用本文:

朱庆,李俊生,张方方,申茜,林卉,王李娟,朱琳. 基于海岸带高光谱成像仪影像的太湖蓝藻水华和水草识别[J]. 遥感技术与应用, 2016, 31(5): 879-885.

Zhu Qing,Li Junsheng,Zhang Fangfang,Shen Qian,Lin Hui,Wang ijuan,Zhu Lin. Distinguishing Cyanobacteria Bloom and Aquatic Plants in Lake Taihu based on Hyperspectral Imager for the Coastal Ocean Images. Remote Sensing Technology and Application, 2016, 31(5): 879-885.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.5.0879 或 http://www.rsta.ac.cn/CN/Y2016/V31/I5/879

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