Harmful algal blooms and GIS

Harmful Algal Blooms and GIS

     Harmful algal blooms are periods of intense planktonic algal growth which typically produce toxins and also deplete oxygen from large areas of the ocean. These blooms have become a major problem for the world we live in, both in terms of biological and economic loss, and particularly in aquaculture and fishing. In order to deal with this problem, governments and scientists have banded together to evaluate and understand the risk, and to build systems which can detect the blooms and thus prevent most of the damage. Their solution? Using GIS remote sensing and satellite data to detect the blooms and provide forecast systems for the general public. All sources can be accessed through the Oregon State University Library e-journal, ScienceDirect.

Annotated Bibliographies


 Banzon Viva F., Baringer Warner, Carvalho Gustavo A.,Fleming Lora E. , Minnett Peter J. Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevisHarmful Algae (in press)

     This article was concerned with two algorithms, the Empirical Approach and the Bio-optical Method, and the results of their observations, which apparently leave a number of false readings that the other algorithm measures accurately. To see if they could improve accuracy, the authors then created a Hybrid Scheme which combines the two optimized algorithms sequentially, with two different configurations. The results were mixed, with the first Hybrid Scheme configuration about 10% more accurate, while the second configuration was about 10% less accurate than either of the original algorithms individually. In conclusion, the authors predicted the Hybrid Scheme could be used to improve the current system, and possibly be used for even other areas.

The article proves to be a great example of continuing improvement in existing technology. It also helped me by showing me a type of GIS analysis that can be used in detection which I wasn't previously aware of.

A warning about this article: this article is still in press and you won't be able to find it in the actual journal. You can download it as a pdf. file from the ScienceDirect entry, however.

Wynne T.T., Stumpf R.P., Tomlinson M.C., Ransibrahmanakul V., Villareal T.A. Detecting Karenia brevis blooms and algal resuspension in the western Gulf of Mexico with satellite ocean color imagery Harmful Algae, 4 (6), pp. 992-1003. (2005) 

 

     The article starts with a brief history of Karenia brevis in Texas, and shows that the system used in Florida, which uses anomalies based on ocean color imagery,  creates false positives in Texas because of  resuspension events.  By calculating  a linear regression between each images’ 670 reflectance anomaly and chlorophyll anomaly, an estimated amount of benthic chlorophyll could be subtracted to give an adjusted chlorophyll anomaly, which was effective in monitoring the blooms. However, the paper did mention that other unconfirmed anomalies may exist, and the reasons for their existence. The paper concludes with a hope the Western Gulf will eventually be incorporated into a monitoring system.

 

    This article surprised me, as I thought that harmful algal blooms would be a top priority in Texas coastal management. The fact that it isn’t shows me that there is still a large body of work to be done to include both GIS and algal blooms in policy.

 

  Cannizzaro J.P., Carder K.L., Chen F.R., Heil C.A., Vargo G.A. A novel technique for detection of the toxic dinoflagellate, Karenia brevis, inthe Gulf of Mexico from remotely sensed ocean color data Continental Shelf Research, 28 (1), pp. 137-158. (2008) 

 

     The authors wanted to find an accurate system with high spatial and temporal resolution for early detection and monitoring of harmful Karenia brevis algal blooms using shipboard data. They noted that the regular method used for algal detection, satellite chlorophyll concentration, cannot be used alone because it cannot distinguish Karenia brevis from other types of algae. Also, the standard algorithms used have underlying sources of error from colored dissolved organic matter (DOM) absorption, and bottom reflectance. Compounding the problem, identification of specific algal groups needs systematic deviations in spectral absorption and backscattering coefficients. However, Karenia brevis has a low backscattering which is generally weaker than its absorption, making it difficult to detect.  The authors prepared classifying and quantifying algorithms that rely on the relationship between chlorophyll concentrations and particular backscattering coefficients, rather than only chlorophyll as used by standard empirical algorithms.  A plan to use this method for monitoring algal blooms was suggested, using both satellites and in situ platforms outfitted with sensors to measure this relationship.

     This paper, in my view has a great deal in common with my seventh reference in that both deal with problems that standard GIS satellite faces. I’m still researching the situation off of our coasts, but these papers give me insights on possible difficulties I might encounter. Also the previous reference and next reference function almost like a history of the program, and how problems with the GIS were worked out and are still being worked on. 

Tomlinson M.C., Wynne T.T., Stumpf R.P. An evaluation of remote sensing techniques for enhanced detection of the toxic dinoflagellate, Karenia brevis  Remote Sensing of Environment, 113 (3), pp. 598-609. (2009)

 

      The study is essentially testing the several different methods in existence for their capabilities in accurately detecting harmful algal blooms.  The paper goes into the positive and negative aspects of each method briefly, and then suggests that an ensemble approach, which integrates multiple algorithms into one image, might be a better option. The results show that this is indeed the case, as all three of the algorithms tested showed false positives, while two other techniques had inconclusive results. An ensemble approach is expected to improve the forecast imagery significantly.

      The paper explained to me what exactly an ensemble system is.  From comparing notes to the other  papers,  this paper showed me several positive and negative factors to consider when using a particular method to detect algal blooms, most specifically the problem of false positives associated with the data.

 Stumpf R.P., Tomlinson M.C., Calkins J.A., Kirkpatrick B., Fisher K., Nierenberg K., Currier R., Wynne T.T. Skill assessment for an operational algal bloom forecast system.  Journal of Marine Systems, 76 (1-2), pp. 151-161. (2009)

 

    The authors evaluated a forecasting system for Karenia brevis in Southwest Florida. The  different types of forecasts were: identification, intensification, transport, extent and impact.  The article went on to discuss each forecast separately and what problems in forecasting were associated with each. The study concluded that while large parts of the model were fairly accurate at a county level, and also pointed to limitations in the impact forecast, extent and transport could not be effectively evaluated. This occurred because of the low resolution of the forecasts and the validation data, which works at a county wide level, but not at a beach level. The authors seemed to conclude that the only way to properly improve the model for finer scales would be to improve the model resolution and the validation resolution together.

   I found this article particularly useful because it exemplifies, at least to me, a core component of GIS, the importance of resolution. Without a proper resolution with which to view data, then an analysis just doesn’t work. It also completes the Karenia brevis story with GIS going from basic beginnings to the eventual conclusion of practical "new science", and continued work in this area.

Ahn Y.-H., Shanmugam P., Ryu J.-H., Jeong J.-C. Satellite detection of harmful algal bloom occurrences in Korean waters. Harmful Algae, 5 (2), pp. 213-231. (2006)

 

 

   The authors wanted to understand the temporal and spatial aspects of Cochlodinium polykrikoides in Korean bays by analysis of chlorophyll-a from the 1998-2002 SeaWIFS ocean color imagery, a form of remote sensing.  However, the chlorophyll-a data alone proved insufficient for identifying the algal blooms from sediment dominated and non-bloom waters, although the data was helpful in determing the spatial aspects (locality, spatial extent, and distribution).  Forward Principal Component Analysis (FPCA) and Minimum Spectral Distance (MSD), two spectral enhancement and classification techniques, were used to identify the algal blooms successfully from SeaWiFS and Landsat-7 ETM+ images. However, MSD could not retrieve dynamic patterns from the SeaWiFS images during the mixed algal bloom phase in combination with turbid waters. This was because the SeaWiFS had an inadequate spatial resolution. The authors then state that new generation ocean color sensors which have high temporal and spatial data would make monitoring effective, as the temporal resolution of much of the data was insufficient.

   I feel this paper is informative because of the latter half of the paper rather than the former. While it reiterates the same themes of resolution’s importance and identification problems that occur with chlorophyll data, it also informed me of the two standard classification techniques.

Ahn Y.-H., Shanmugam P. Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters  Remote Sensing of Environment, 103 (4), pp. 419-437.(2006)

    Detecting “red tide” algal blooms in Northeast Asia correctly has presented a major problem for    standard algorithms using SeaWIFS satellite ocean color data, because Northeast Asia often contains suspended sediments and dissolved organic matter which interfere with the data’s accuracy. The authors developed a new method they called the red tide index to deal with the problem.  The index technique differs from similar techniques by collecting radiometric measurements of SeaWIFS bands at 443, 510, and 555 nm. These bands are related to harmful algal  water-leaving radiances(Lw).  A best fit cubic polynomial function was produced from the radiances at these bands which provides the authors with  high ranges for  harmful algal blooms. Similar techniques, such as the OC4 algorithm, are taken from remote sensing reflectance and normalized water-leaving radiances. The RI method and chlorophyll (Chl) concentration’s relationship was examined and used to create a Red Tide index Chlorophyll algorithm (RCA). When compared with other algorithms and methods, the RI method and RCA proved to be superior to the other standard methods presented in the article.

    This article’s primary interest to me was as background research.  I have not seen all the different techniques or problems that come from satellite remote sensing, and this one gives me ideas for possible projects in native Oregon waters.

Shanmugam P., Ahn Y.-H., Ram P.S. SeaWiFS sensing of hazardous algal blooms and their underlying mechanisms in shelf-slope waters of the Northwest Pacific during summer  Remote Sensing of Environment, 112 (7), pp. 3248-3270 (2008)

 

    The authors are concerned with the recent harmful algal blooms in the Northwest Pacific region. The current in situ and satellite chlorophyll and sea surface temperature estimates are questioned as to their validity, particularly under cloudy conditions. Therefore, the authors combined the RCA-Chl algorithm  (from SeaWiFS), sea surface temperature, sea surface height/geostrophic currents, and wind, in conjugation with in-situ observation data, to show the spatial and temporal relationships between  1998-2006 summer algal blooms and the mechanisms underlying their development. The study was divided into 5 different segments: the SCS–Taiwan segment, Taiwan–ECS segment, YS-BS segment, KS-JS-RS (Korean Sea-Japan Sea-Russian Sea) segment, and RS-North Korean segment. Four common hydrodynamically active regions (coastal cold/estuary water zones, upwelling zones next to the coast, repeated meanders/eddies, and frontal regimes induced by the Kuroshio Current) were identified in each segment. These regions were the source of dinoflagellate dominant blooms and transported them via currents and eddy systems. This made it difficult for physical data and biological data to be collected for the larger blooms because they became widespread across the ocean. With cover areas of > 20 × 103 km2, this causes these large blooms to be undetected and occur without warning.  Besides these regions, an inverse correlation exists between the RCA-Chl and SSH and SST. Based on this and where the chlorophyll occur,the authors came to a fairly clear picture of algal bloom density from this find, with high density blooms occurring in cold waters with nutrient upwelling, and the low density blooms occurring in warmer waters. Another phenomenon of note was the presence of meanders in the ocean currents  which "injected" the blooms into an interior ocean circulatory system by means of a "conveyor belt" of eddies.

    In conclusion, the authors suggested that HABs are in fact closely associated with ocean processes,  and also primary production conditions. They also effectively declared that HABs had been mapped from SeaWiFS images. However they also mention that the RCA-Chl had been the most helpful in a wide range of situation.

  This article is another fine example of building on previous research, as well as the difficulties of using GIS on a wide scale, and how the practical problem of algal blooms is extremely complex. However, it also shows that as GIS continues to improve, the problem may become manageable.

. Kutser T., Metsamaa L., Strombeck N., Vahtmae E. Monitoring cyanobacterial blooms by satellite remote sensing Estuarine, Coastal and Shelf Science, 67 (1-2), pp. 303-312. (2006)

    The article discusses the growing problems that cyanobacteria blooms present to human and animal health, with a severe problem happening in the Baltic Sea region. These blooms, although widespread, are also extremely patchy, and the regular monitoring system cannot detect them. The paper examined if spectral resolution of multispectral sensors could quantitatively map cyanobacteria, and ,if possible,  to use ocean color satellites to separate potentially harmful cyanobacterial blooms from nontoxic algal blooms. The results proved negative, as all three sensors used couldn’t detect the primary absorber in the cyanobacteria, phycocyanin,  although the sensors could be used for quantitative mapping, but not without often unavailable in situ data. Also, one of the sensors, MERIS, could detect the cyanobacteria in high concentrations, but remained unhelpful as the level characterized as a bloom are lower than these required concentrations.

    This article definitely identifies a major practical problem in GIS, the sensitivity of our instruments.  It also drives home the point of the need for GIS research, and how dynamic GIS is as a relatively new science.

Wang J., Wu J. Occurrence and potential risks of harmful algal blooms in the East China Sea . Science of The Total Environment Volume 407, I 13, , pp. 4012-4021. (2009)

  This study used GIS to study the East China sea and its harmful algal bloom patterns from 2000-2006, and created a method to evaluate the risks harmful algae blooms pose. They mapped the frequency of the blooms using a kernel density estimation (KDE).  KDE take a specific point’s value and spreads it across a predefined area and generates a map that shows the density of the events modeled as a continuous field.  Nearest neighbor analysis and time series analysis were used to evaluate the patterns. The results identified 40 causative species with Prorocentrum dentatum being the most common species (dominated in over twice as many events as the second most common species). At any rate, the harmful algal bloom frequency increased, possibly due to environmental deterioration. Also, the blooms show a clustered pattern. The model for evaluating the algal blooms’ risk was not detailed, merely using physical-biochemical characteristics. The paper then went on to discuss the importance of the harmful algal blooms and how their results can be used in policy management. Finally, the authors also went into the data and into certain choices they made that may have limited the data, such as treating the polygons from the kernel estimation as points. In conclusion, the authors claimed their results showed both the uses of GIS and that harmful algal blooms are continuing to increase.

    Looking at the time period that this paper was originally submitted (2007), it provides important background information for the long-term effects of algal blooms. Also, the basic points that the paper made about the policy management are deeply connected to my final plans, to involve harmful algal blooms in Oregon resource management.