Advances in Remote Sensing of Great Lakes Algal Blooms
Many regions of the Great Lakes now see recurring cyanobacterial harmful algal blooms (cyanoHABs), with documented repercussions for ecosystem services, public health, and ecosystem integrity. Early detection and comprehensive monitoring of cyanoHABs are
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Contents 1 Background 2 Satellite Sensors 2.1 Multi-mission Data and Product Continuity 3 Bloom Detection Algorithms 4 Products and Applications 4.1 Consistency in Bloom Products 5 Challenges and Limitations of Remote Sensing 5.1 Product Validation 5.2 Algal Bloom Toxicity 5.3 Vertical Bloom Variability 5.4 Variable Optical Properties 6 Conclusions References
Abstract Many regions of the Great Lakes now see recurring cyanobacterial harmful algal blooms (cyanoHABs), with documented repercussions for ecosystem services, public health, and ecosystem integrity. Early detection and comprehensive monitoring of cyanoHABs are fundamental to their effective management and mitigation of detrimental impacts. Satellite remote sensing has provided the means by which algal blooms in the Great Lakes can be observed with unprecedented frequency and spatial coverage. Algorithms have been developed and validated; fully automated data processing streams have been rendered operational; and C. E. Binding (*) Environment and Climate Change Canada, Burlington, ON, Canada e-mail: [email protected] R. P. Stumpf National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, USA R. A. Shuchman and M. J. Sayers Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, USA Jill Crossman, Chris Weisener (eds.), Contaminants of the Great Lakes, Hdb Env Chem, DOI 10.1007/698_2020_589, © Springer Nature Switzerland AG 2020
C. E. Binding et al.
stakeholders have been engaged in order to develop user-friendly end products. Such products have been integral in providing near-real-time monitoring of bloom conditions, documenting spatiotemporal trends, improving understanding of environmental drivers of blooms, and guiding nutrient management actions. In this chapter we present background information on remote sensing of algal blooms, document the current state of knowledge with a focus on Lake Erie, and discuss remote sensing products available to the Great Lakes community. Keywords Algal blooms, Electromagnetic spectrum, Hyperspectral, Phytoplankton, Spatiotemporal trends, Surface scum
1 Background The potential of satellite remote sensing of water quality in the Great Lakes was first documented in the 1970s, with Landsat data being explored in the identification of particulate contaminants [1], whiting events [2], and chlorophyll-a [3]. Since then, increasingly sophisticated satellite sensor technologies, novel algorithm development, and considerable improvements in data availability and image processing capabilities with modern computational power have resulted in major advancements in remote sensing of the Great Lakes. For example, cloud-based parallel computing tools available via Google Earth Engine have substantially reduced the time and resources required to process and analyze remote sensing imagery [4]. The combination of an increasing number of aquatic color satellite missions, the adoption by space agency data providers of service models supporting free and open data, and wides
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