基于SMDPSO算法的呼伦湖藻华遥感监测  

Remote sensing monitoring of algal blooms in Hulun Lake based on SMDPSO algorithm

作　　者：曹萌萌 青松[1] 杜雨春子 袁瑞强 顺布日 CAO Mengmeng;QING Song;DU Yuchunzi;YUAN Ruiqiang;SHUN Buri(College of Geographical Science,Inner Mongolia Normal University,Hohhot 010022,China)

机构地区：[1]内蒙古师范大学地理科学学院,内蒙古呼和浩特010022

出　　处：《水资源与水工程学报》2021年第2期66-72,80,共8页Journal of Water Resources and Water Engineering

基　　金：国家自然科学基金项目(41961057、61461034);内蒙古自治区高等学校青年科技英才支持计划项目(NJYT-17-B04);内蒙古自然科学基金项目(2019MS04013)。

摘　　要：水体富营养化所引起的藻华爆发现象是我国面临的重大环境问题之一。以内蒙古呼伦湖为研究区,采用基于离散粒子群优化的光谱匹配(SMDPSO)算法提取藻华,以浮游藻类指数(FAI)的分类结果作为验证数据进行精度检验。然后分析2009-2018年藻华的时空变化特征,并将此算法应用于黄海。结果表明:SMDPSO算法可以有效地识别呼伦湖藻华,与FAI分类结果之间的R^(2)为0.97,RMSE为0.22 km^(2);呼伦湖藻华爆发于7-8月,且主要出现在湖泊边缘;SMDPSO算法既可以较好地识别以蓝藻为优势门的呼伦湖藻华,也可以提取黄海的浒苔(绿藻);SMDPSO算法不仅保留了光谱指数法精度高的特点,而且它还具有成本低、参数少、无需人工干预的优势。该研究为藻华遥感监测提供了新的工具,有助于控制湖泊水体富营养化和改善水生态环境。Algal blooms caused by eutrophication is one of the major environmental problems in China.Spectrum matching based on discrete particle swarm optimization(SMDPSO)algorithm was used to identify algal blooms in Hulun Lake,Inner Mongolia,and the classification results of floating algae index(FAI)were used as validation data to evaluate the accuracy of the algorithm.Then,the temporal and spatial characteristics of algal blooms from^(2)009 to 2018 were analyzed,after which the algorithm was applied to the identification of agal blooms in the Yellow Sea.The results show that SMDPSO algorithm can effectively identify algal blooms in Hulun Lake.The R^(2) and RMSE between SMDPSO and FAI are 0.97 and 0.22 km^(2) respectively.The outburst of algal blooms in Hulun Lake last from July to August,and mainly appeared at the edge of the lake.SMDPSO algorithm can not only extract the algal blooms(cyanobacteria is the dominant phylum)from Hulun Lake,but also identify enteromorpha(green algae)in the Yellow Sea.The algorithm shares the characteristics of high precision with spectral index method,and has the advantages of low cost,less parameters involved and no need of manual intervention.This study provides a novel tool for algal bloom remote sensing monitoring,which is helpful for controlling the eutrophication of lake water and improving the water ecological environment.

关 键 词：藻华 SMDPSO算法 浮游藻类指数(FAI) Landsat-8 OLI 遥感监测 呼伦湖 

分 类 号：X832[环境科学与工程—环境工程]