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基于Otsu算法的太湖蓝藻水华与水生植被遥感同步监测方法
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  • 英文篇名:A novel remote sensing simultaneous monitoring method for cyanobacteria blooms and aquatic vegetation in Taihu Lake based on Otsu algorithm
  • 作者:曹鹏 ; 梁其椿 ; 李淑敏
  • 英文作者:Cao Peng;
  • 关键词:蓝藻水华 ; 水生植被 ; 太湖 ; Otsu ; MODIS
  • 中文刊名:江苏农业科学
  • 英文刊名:Jiangsu Agricultural Sciences
  • 机构:北京大学遥感与地理信息系统研究所;中国电子科技集团海洋信息技术研究院;
  • 出版日期:2019-07-29 08:56
  • 出版单位:江苏农业科学
  • 年:2019
  • 期:14
  • 基金:国家自然科学基金(编号:41625003);; 中电科海洋信息技术研究院创新基金(编号:xyxt)
  • 语种:中文;
  • 页:296-302
  • 页数:7
  • CN:32-1214/S
  • ISSN:1002-1302
  • 分类号:X524;X87
摘要
蓝藻水华与水生植被在光学遥感影像上容易混淆,传统方法将太湖划分为藻型湖区和草型湖区进行分区监测,近年来太湖梅梁湖等蓝藻水华易发区域出现了大量的水生植物,分区的方法已无法满足蓝藻水华和水生植被遥感监测要求。基于光谱特征分析,采用蓝藻水华与水生植被指数(cyanobacteria and macrophytes index,简称CMI)判别蓝藻水华与水生植被水域,采用浮游藻类指数(floating algae index,简称FAI)识别蓝藻水华、浮叶/挺水植被与沉水植被,构建同步监测决策树,基于Otsu算法自动获取阈值,将中分辨率成像光增仪(MODIS)卫星影像分成湖水、蓝藻水华、沉水植被和浮叶/挺水植被几种类型。结果表明,分类结果较好,符合太湖不同地物类型实际分布情况;与相关研究HJ卫星影像东部湖区水生植被监测结果进行交叉检验,水生植被的空间分布基本一致,一致性检验结果显示,2种分类结果一致的像元比例为70.11%。实现蓝藻水华及水生植物的同步遥感监测,有助于精确评估蓝藻水华的实际强度和水生植被区范围,为富营养化湖泊的水环境管理和决策提供重要的科技支撑。
        
引文
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