- Biomedical Optics & Medical Imaging
- Defense & Security
- Electronic Imaging & Signal Processing
- Illumination & Displays
- Lasers & Sources
- Micro/Nano Lithography
- Optical Design & Engineering
- Optoelectronics & Communications
- Remote Sensing
- Sensing & Measurement
- Solar & Alternative Energy
- Sign up for Newsroom E-Alerts
- Information for:
Red tides: combining satellite- and ground-based detection
Remote sensing of reflectance peak shifts and polarized radiance may detect red tides.
29 January 2011, SPIE Newsroom. DOI: 10.1117/2.1201012.003267
Algal blooms (red tides) are a phenomenon of clear ecological importance in many regions of the world. Caused by a nutrient influx (e.g. agricultural pollution) into the ocean, by either natural or anthropogenic causes, they can be toxic to marine life1 and humans under certain conditions. Consequently, monitoring the presence and composition of red tides is a public health and safety issue. Relevant to the question of how to detect red tides is that they change water color. Increasing phytoplankton concentrations induce changes in ocean surface color from blue to green, or red to brown, as a function of phytoplankton composition.2 Thus, it is feasible to detect red tides based on ocean color.
The red tide detection system proposed herein makes use of the moderate-resolution imaging spectroradiometer (MODIS). This is a satellite-based observation system, in use for the past ten years, that can image the entire planetary surface every one or two days. An iterative approach3,4 for sediment-rich waters, based on the Gordon and Wang algorithm,5 improves upon the data provided by MODIS. Specifically, correction of the atmospheric interference in the six ocean color bands in turbid coastal waters yields the water radiance. A band-ratio algorithm6 is then applied to estimate chlorophyll (e.g. red tide severity) in units of mg/m3. An estimate of suspended solids is achieved by substracting infrared bands from visible bands. Use of multiple channels for red tide detection algorithms — given by C=(Ri−Rj)/(Rk−Rl), where Ri, Rj, Rk and Rl are the reflectivity derived from bands i, j, k and l — is readily instituted. Alternatively, learning approaches based on k-nearest neighbors, random forests, and support vector machines have also been proposed.7
Conventional satellite-based detection provides insufficient information on red tides. It suffers from two limitations, namely detection accuracy and interference from suspended solids. These limitations are addressed herein with a combination of ground- and satellite-based detection, combining the advantages of both approaches. Red tide detection with a web camera, equipped with a band-pass filter at 550nm wavelength, is proposed together with data transmission through a wireless LAN (local area network) and the Internet. This is a ground-based measurement system which enables red tide detection even in cloudy and rainy weather conditions where satellite imagery is insufficient. In the method proposed here, polarization film attached to a camera enables us to discriminate between normal ocean water and red tide. This method is validated by detecting red tide species (with the possibility of discriminating between spherical and non-spherical shapes) in controlled laboratory conditions, with a polarization camera.
Figure 1.The proposed red tide monitoring system uses a band-pass filter attached to a web camera. Instruments for measuring water quality and meteorological data collection are also included.
Figure 1 illustrates the proposed red tide monitoring system, with the band-pass filter attached to the web camera, together with instruments to measure water quality and gather meteorological data. As an illustration of detection possibilities not feasible by satellite-based detection alone, polarization experiments were performed, with the goal of demonstrating the feasibility of red tide species discrimination. Degree of polarization (DP) images are shown in Figure 2 (a) and (b), respectively. The blue ellipsoids highlight the DP for water, with (left) and without (right) red tide species. The background and container both influence the surroundings. The difference between the DPs is approximately 20%. In other words, the DP of water is almost zero, while that of water containing chattonella antiqua microbes is approximately 0.2. This is a consequence of the microbes' shape, which resembles a football, and means its reflected radiance from the water has a detectable polarization signature. Thus, microbial shape (e.g. red tide composition) is readily discriminated by ground-based observation.
Figure 2.Degree of polarization (DP) images, acquired with a polarization camera. Left: Water containing chattonella antiqua. Right: Pure water.
Neither satellite imagery nor ground-based measurements are by themselves sufficient for detecting and characterizing red tides. Thus, neither approach in isolation is sufficient for addressing this global environmental and public health issue. Here, we have proposed a combination of satellite- and ground-based methods to detect red tides (see Figure 1), joining the unique advantages of both observational approaches. The satellite imagery has been validated by previous research.1 The ground-based component clearly discriminates water that does and does not contain red tide microbial species (see Figure 2), with the clear further potential of discriminating between microbial species, e.g. those which are and are not toxic to humans.
Kohei Arai received a PhD from Nihon University in 1982. He was subsequently appointed to the University of Tokyo, CCRS, and the Japan Aerospace Exploration Agency. He was appointed professor at Saga University in 1990. He is also an adjunct professor at the University of Arizona and is Vice Chairman of ICSU/COSPAR Commission A.
1. C. J. Walsh, S. R. Leggett, B. J. Carter, C. Colle, Effects of brevetoxin exposure on the immune system of loggerhead sea turtles, Aquat. Toxicol
. 97, no. 4pp. 293-303, 2010. doi:10.1016/j.aquatox.2009.12.014
2. H. M. Dierssen, R. M. Kudela, J. P. Ryan, R. C. Zimmerman, Red and black tides: Quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments, Limnol. Oceanogr
. 51, no. 6pp. 2646-2659, 2006. doi:10.4319/lo.2006.51.6.2646
3. R. A. Arnone, P. Martinolich, R. W. Gould, R. Stumpf Jr., S. Ladner, Coastal optical properties using SeaWiFS, SPIE Ocean Optics XIV (unpublished), 1998.
4. R. P. Stumpf, R. A. Arnone, R. W. Gould Jr., P. M. Martinolich, V. Martinuolich, A partially coupled ocean-atmosphere model for retrieval of water-leaving radiance from SeaWiFS in coastal waters., SeaWiFS Postlaunch Tech. Report Series 22, pp. 74, 2003. NASA Technical Memorandum, 2003-206892.
5. H. R. Gordon, M. Wang, Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm, Appl. Optics
33, pp. 443-452, 1994. doi:10.1364/AO.33.000443
6. J. E. O'Reilly, S. Maritorena, D. A. Siegel, M. C. O'Brien, D. Toole, F. P. Chavez, Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4, SeaWiFS Postlaunch Tech. Report Series 11, pp. 2000, 2000. NASA Technical Memorandum 2000-206892
7. C. Weijian, L. O. Hall, D. B. Goldgof, I. M. Soto, H. Chuanmin, Automatic red tide detection from MODIS satellite images, Systems, Man and Cybernetics, 2009, IEEE Sys. Man. Cybern. (SMC), 2009.