{"title":"以密西斯科伊湾为重点的尚普兰湖蓝藻水华遥感技术","authors":"Timothy T. Wynne","doi":"10.1016/j.jglr.2024.102293","DOIUrl":null,"url":null,"abstract":"<div><p>At 1270 km<sup>2</sup>, Lake Champlain, is the 13th largest lake in the United States. Lake Champlain experiences annual blooms of cyanobacteria, particularly in Missisquoi Bay. Here the Cyanobacteria Index, a previously published algorithm, was applied to satellite imagery from OLCI (available from 2016 through the present) and MODIS (available from 2000-present). The remotely sensed timeseries of the CI was derived and described from each sensor, along with cross-calibration among OLCI and MODIS. The resultant timeseries described when and where cyanobacteria blooms generally occur. Five distinct regions of Lake Champlain were considered for analysis: Malletts Bay, the Northeast Arm, Saint Albans Bay, Missisquoi Bay, and the Main Lake. Saint Albans and Missisquoi Bay were the only basins shown to have consistent cyanobacteria blooms. Saint Albans Bay, due to its small size was not an ideal fit for the methods used here, and the focus of this manuscript was Missisquoi Bay. The objective of this study is to explore the interannual variability of blooms in Missisquoi Bay and compare the variability to cyanobacteria blooms in Lake Erie, Saginaw Bay, and Green Bay. The blooms in Missisquoi Bay showed interannual variability in size, intensity, and start and end date. Observed data from the Burlington International Airport and modeled data from NASA’s Giovanni program were used in an effort to explain this bloom variability. A 2-parameter multiple regression model fit the cyanobacterial data well and showed that the interannual variability of blooms in Missisquoi Bay are a function of atmospheric instability and temperature.</p></div>","PeriodicalId":54818,"journal":{"name":"Journal of Great Lakes Research","volume":"50 2","pages":"Article 102293"},"PeriodicalIF":2.4000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0380133024000157/pdfft?md5=90753915926cb6431180994f14c32c1e&pid=1-s2.0-S0380133024000157-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Remote sensing of cyanobacterial blooms in Lake Champlain with a focus on Missisquoi Bay\",\"authors\":\"Timothy T. Wynne\",\"doi\":\"10.1016/j.jglr.2024.102293\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>At 1270 km<sup>2</sup>, Lake Champlain, is the 13th largest lake in the United States. Lake Champlain experiences annual blooms of cyanobacteria, particularly in Missisquoi Bay. Here the Cyanobacteria Index, a previously published algorithm, was applied to satellite imagery from OLCI (available from 2016 through the present) and MODIS (available from 2000-present). The remotely sensed timeseries of the CI was derived and described from each sensor, along with cross-calibration among OLCI and MODIS. The resultant timeseries described when and where cyanobacteria blooms generally occur. Five distinct regions of Lake Champlain were considered for analysis: Malletts Bay, the Northeast Arm, Saint Albans Bay, Missisquoi Bay, and the Main Lake. Saint Albans and Missisquoi Bay were the only basins shown to have consistent cyanobacteria blooms. Saint Albans Bay, due to its small size was not an ideal fit for the methods used here, and the focus of this manuscript was Missisquoi Bay. The objective of this study is to explore the interannual variability of blooms in Missisquoi Bay and compare the variability to cyanobacteria blooms in Lake Erie, Saginaw Bay, and Green Bay. The blooms in Missisquoi Bay showed interannual variability in size, intensity, and start and end date. Observed data from the Burlington International Airport and modeled data from NASA’s Giovanni program were used in an effort to explain this bloom variability. A 2-parameter multiple regression model fit the cyanobacterial data well and showed that the interannual variability of blooms in Missisquoi Bay are a function of atmospheric instability and temperature.</p></div>\",\"PeriodicalId\":54818,\"journal\":{\"name\":\"Journal of Great Lakes Research\",\"volume\":\"50 2\",\"pages\":\"Article 102293\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0380133024000157/pdfft?md5=90753915926cb6431180994f14c32c1e&pid=1-s2.0-S0380133024000157-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Great Lakes Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0380133024000157\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Great Lakes Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0380133024000157","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
尚普兰湖面积为 1270 平方公里,是美国第 13 大湖。香普兰湖每年都会出现蓝藻藻华,尤其是在密西西比湾。在此,蓝藻指数(一种之前发布的算法)被应用于 OLCI(2016 年至今可用)和 MODIS(2000 年至今可用)的卫星图像。在对 OLCI 和 MODIS 进行交叉校准的同时,每个传感器都得出并描述了 CI 的遥感时间序列。由此得出的时间序列描述了蓝藻藻华一般发生的时间和地点。香普兰湖的五个不同区域被纳入分析范围:Malletts 海湾、东北臂、圣奥尔本斯湾、米西斯科伊湾和主湖。圣奥尔本斯湾和米西斯科伊湾是唯一持续出现蓝藻藻华的盆地。由于圣奥尔本斯湾面积较小,不适合采用本研究的方法,因此本手稿的重点是米西 斯夸湾。本研究的目的是探索米西斯科伊湾蓝藻藻华的年际变异性,并将其与伊利湖、萨基诺湾和格林湾 的蓝藻藻华进行比较。米西斯科伊湾的藻华在规模、强度、开始和结束日期等方面都存在年际变化。伯灵顿国际机场的观测数据和美国国家航空航天局 Giovanni 计划的模型数据被用来解释这种藻华变化。一个双参数多元回归模型很好地拟合了蓝藻数据,并表明米西斯科伊湾水华的年际变化是大气不稳定性和温度的函数。
Remote sensing of cyanobacterial blooms in Lake Champlain with a focus on Missisquoi Bay
At 1270 km2, Lake Champlain, is the 13th largest lake in the United States. Lake Champlain experiences annual blooms of cyanobacteria, particularly in Missisquoi Bay. Here the Cyanobacteria Index, a previously published algorithm, was applied to satellite imagery from OLCI (available from 2016 through the present) and MODIS (available from 2000-present). The remotely sensed timeseries of the CI was derived and described from each sensor, along with cross-calibration among OLCI and MODIS. The resultant timeseries described when and where cyanobacteria blooms generally occur. Five distinct regions of Lake Champlain were considered for analysis: Malletts Bay, the Northeast Arm, Saint Albans Bay, Missisquoi Bay, and the Main Lake. Saint Albans and Missisquoi Bay were the only basins shown to have consistent cyanobacteria blooms. Saint Albans Bay, due to its small size was not an ideal fit for the methods used here, and the focus of this manuscript was Missisquoi Bay. The objective of this study is to explore the interannual variability of blooms in Missisquoi Bay and compare the variability to cyanobacteria blooms in Lake Erie, Saginaw Bay, and Green Bay. The blooms in Missisquoi Bay showed interannual variability in size, intensity, and start and end date. Observed data from the Burlington International Airport and modeled data from NASA’s Giovanni program were used in an effort to explain this bloom variability. A 2-parameter multiple regression model fit the cyanobacterial data well and showed that the interannual variability of blooms in Missisquoi Bay are a function of atmospheric instability and temperature.
期刊介绍:
Published six times per year, the Journal of Great Lakes Research is multidisciplinary in its coverage, publishing manuscripts on a wide range of theoretical and applied topics in the natural science fields of biology, chemistry, physics, geology, as well as social sciences of the large lakes of the world and their watersheds. Large lakes generally are considered as those lakes which have a mean surface area of >500 km2 (see Herdendorf, C.E. 1982. Large lakes of the world. J. Great Lakes Res. 8:379-412, for examples), although smaller lakes may be considered, especially if they are very deep. We also welcome contributions on saline lakes and research on estuarine waters where the results have application to large lakes.