Monitoring Algal Blooms with Remote Sensing

Algal blooms present a major problem, as they are often instigated by pollution and changing temperature and can kill a variety of marine and freshwater life through eutrophication. Usually these blooms give a distinct coloration visible in imagery,  such as the red tide, although the coloration does vary depending on the type of bloom.

Given the importance of knowing how these blooms affect aquatic life, remote sensing techniques using a variety of available imagery have been developed. The southern Benguela in South Africa is one rich area for fishing that algal blooms threaten. Variation in chlorophyll is one major variable in algal blooms in the area; using the Sentinel-3 Ocean and Land Colour Instrument  (OLCI) system, this instrument has been designed to capture signatures of biogeochemicals that affect algal bloom growth.

Using Remote Sensing to Distinguish Areas of High Algal Growth

In low biomass areas, an adapted version of the OC4MEblue-green band-ratio algorithm can be used to distinguish high concentrations of algal growth, while a red-Near Infrared (NIR) band-ratio algorithm can be applied in high biomass areas. While algal blooms are often a seasonal event, thresholds within the algorithms could allow the tracking of events over time and space when more extreme events take place. This has the benefit of improving data collection for future assessments that attempt to forecast these blooms as well as knowing what areas are impacted.[1]

Detecting Chlorophyll a With Remote Sensing

Challenges in oceanic observations have been seen particularly in coastal environments, where band-ratios algorithms, which work best with sensors in open ocean water, are less accurate in coastal regions, particularly in the detection of chlorophyll a (Chl-a). This is why Sentinel-3’s OLCI system provides a major upgrade to scientific capabilities in detecting this important chemical that relates to major algal blooms. In particular, improvements in red-NIR band ratio detection has been a major reason for increased detection in coastal water for phytoplankton that have chlorophyll a.[2]

Differentiation of Algal Species in Inland Water Regions

Other challenges identified in the monitoring of algal blooms using remote sensing data include viewing blooms in inland water regions, particularly in small bodies of water, and in highly turbid environments that block out easier views of algal growth. In fact, most papers in recent years have focused on inland regions, as open ocean areas have been seen to be better captured by low resolution systems such as MODIS and MERIS. Improvements in adjacency correction, inversion-based retrieval models and optical property measurements have, however, allowed advancements to be made in these inland areas in the last few years.[3]

Increased spatial and spectral range of hyperspectral sensors on airborne instruments, such as MASTER, HICO, and AVIRIS data, have also enabled better differentiation of algal species in smaller, inland bodies of water. One study in Pinto Lake California demonstrated that Aphanizomenon and Microcystis species could be separated using a spectral shape algorithm. Regular monitoring allows a better understanding of seasonal variations as rainfall conditions and surface water temperatures change.[4]

Given that algal blooms are best covered within different parts of the electromagnetic spectrum, seasonal variation, resolution, and other parametric factors, combining multiple imagery using spatial-temporal analysis and image merging and interpolation techniques to best estimate and determine algal bloom regions. Older systems, such as AVHRR and CZCS, are also available, while also providing historical data. Sea surface and water surface temperatures, along with Chl-a, have also been used to capture existing data and have shown a strong link to algal growth. Thus, systems that can integrate such key parameters and utilize multiple and historical data could, in the future, best predict how algal blooms may grow and affect different regions.[5]

References

[1] For more information on the use of Sentinel-3 OLCI for algal blooms, see:  https://www.eumetsat.int/website/home/Images/ImageLibrary/DAT_3798232.html.

[2] For more on detection issues raised by remote sensing literature on algal blooms, see:  Blondeau-Patissier, D., Gower, J.F.R., Dekker, A.G., Phinn, S.R., et al. (2014) A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography. [Online] 123, 123–144. Available from: doi:10.1016/j.pocean.2013.12.008.

[3] For more on improvements in inland environments, see:  Palmer, S.C.J., Kutser, T. & Hunter, P.D. (2015) Remote sensing of inland waters: Challenges, progress and future directions. Remote Sensing of Environment. [Online] 157, 1–8. Available from: doi:10.1016/j.rse.2014.09.021.

[4] For more on the use of hyperspectral imagery, see:  Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., et al. (2015) Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment. [Online] 167, 196–205. Available from: doi:10.1016/j.rse.2015.01.025.

[5] For more on integrative systems approach to remote sensing of algal blooms, see:  Shen, L., Xu, H. & Guo, X. (2012) Satellite Remote Sensing of Harmful Algal Blooms (HABs) and a Potential Synthesized Framework. Sensors. [Online] 12 (12), 7778–7803. Available from: doi:10.3390/s120607778.

See Also

• Mapping Jellyfish
• Ready for Summer – Remote Sensing of Bathing Water Quality
• Blooms and Scums in Lake Erie