Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

被引:5
作者
Beaulne, Danielle [1 ]
Fotopoulos, Georgia [1 ]
机构
[1] Queens Univ, Dept Geol Sci & Geol Engn, Kingston, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Metadata; Algal blooms; Remote sensing; Geovisualization; Web application; Citizen science; MAPPING CYANOBACTERIAL BLOOMS; CHLOROPHYLL-A CONCENTRATIONS; CLOUD SHADOW DETECTION; WATER-QUALITY; GREAT-LAKES; PHYTOPLANKTON BLOOMS; TAIHU LAKE; WEST LAKE; LANDSAT; ALGORITHM;
D O I
10.1016/j.ecoinf.2023.102447
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.
引用
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页数:13
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