Distinguishing Cyanobacterial Bloom From Floating Leaf Vegetation in Lake Taihu Based on Medium-Resolution Imaging Spectrometer (MERIS) Data
Distinguishing Cyanobacterial Bloom From Floating Leaf Vegetation in Lake Taihu Based on Medium-Resolution Imaging Spectrometer (MERIS) Data
复制标题
基于中分辨率成像光谱仪（MERIS）数据区分太湖蓝藻水华和浮叶植被
DOI：
10.1109/jstars.2017.2757006
复制
发表时间：
2018
影响因子：
5.5
通讯作者：
Shen Qian
中科院分区：
文献类型：
--
作者：
Zhu Qing;Li Junsheng;Zhang Fangfang;Shen Qian
基于太湖水体反射光谱的实测数据，结合叶绿素光谱指数和藻蓝蛋白基线，建立了太湖蓝藻水华和浮叶植被的识别模型。现场Rrs测量验证结果表明，该模型能较好地区分太湖蓝藻水华和浮叶植被。我们将此模型应用于2003年至2011年的中分辨率成像光谱仪（MERIS）的52幅遥感图像。使用两种不同的精度评估方法，我们发现一个平均识别精度超过80%的蓝藻水华和浮叶植被时，使用最佳的指标阈值。采用平均指数阈值提取蓝藻水华和浮叶植被，相对准确率分别为78.8%和74.6%。如果需要更高的效率，可以使用这些平均阈值，这是方便的批处理和自动提取蓝藻水华和浮叶植被的遥感数据。通过对2003 - 2011年太湖蓝藻水华和浮叶植被分布图的叠加分析，确定了太湖蓝藻水华和浮叶植被的总体分布特征，分析结果与以往发表的结果一致。此外，我们的分析表明，该模型不受薄云和气溶胶的干扰。
Based on field measurements of water surface reflectance spectra in Lake Taihu, we construct a model for distinguishing between cyanobacterial bloom and floating leaf vegetation by combining a chlorophyll spectral index with a baseline of phycocyanin. In situ Rrs measurements validation results show that this model performs well in distinguishing cyanobacterial bloom from floating leaf vegetation in Lake Taihu. We apply this model to 52 remote sensing images from the Medium-Resolution Imaging Spectrometer (MERIS) from 2003 to 2011. Using two different accuracy evaluation methods, we find an average recognition accuracy of more than 80% for cyanobacterial bloom and floating leaf vegetation when using optimal index thresholds. Using an average index threshold to extract cyanobacterial bloom and floating leaf vegetation from the images, the relative accuracies are 78.8% and 74.6%, respectively. If more efficiency is desired, these average thresholds can be used, which is convenient for batch processing and automated extraction of cyanobacterial bloom and floating leaf vegetation from remote sensing data. The overall distribution of cyanobacterial bloom and floating leaf vegetation in Lake Taihu from 2003 to 2011 is determined by overlapping the distribution maps from individual images, and the results of our analysis are consistent with previously published results. In addition, our analysis shows that this model is immune to perturbations from thin clouds and aerosols.
登录
查看更多内容
DOI：
10.1016/j.ecoinf.2008.01.004
发表时间：
2008-07
期刊：
Ecol. Informatics
影响因子：
--
作者：
Lin Yuan;Li-Quan Zhang
通讯作者：
Lin Yuan;Li-Quan Zhang
影响因子：
13.5
作者：
Kudela, Raphael M.;Palacios, Sherry L.;Torres-Perez, Juan
通讯作者：
Torres-Perez, Juan
青藏高原MODIS地表温度的有效插值方法
DOI：
10.1109/jstars.2015.2464094
发表时间：
2015
影响因子：
5.5
作者：
Yu Wenjun;Nan Zhuotong;Wang Zhiwei;Chen Hao;Wu Tonghua;Zhao Lin
通讯作者：
Zhao Lin
DOI：
10.1109/jstars.2013.2280815
发表时间：
2014-05
影响因子：
5.5
作者：
Lin Qi;R. Ma;Weiping Hu;S. Loiselle
通讯作者：
Lin Qi;R. Ma;Weiping Hu;S. Loiselle
中国太湖蓝藻水华的中分辨率成像光谱仪 (MODIS) 观测
影响因子：
3.6
作者：
Hu, Chuanmin;Lee, Zhongping;Shang, Shaoling
通讯作者：
Shang, Shaoling