Optical oceanography or Marine optics is the study of light propagation in the ocean

surface through absorption or scattering processes. Marine bio-optics is the term used

when the absorption and scattering by particles and dissolved substances are of biological

origin. Ocean color is defined as the spectral variation of the water leaving radiance that

can be related to the optical constituents present in the medium (Jerlov, 1976; Morel, 1974).

Visible Spectral radiometry or Ocean colour remote sensing is the study on spectral signals

of optically active materials using satellite observations. When sunlight reaches the upper

water column or the photic zone of the ocean surface, the light propagation is determined

by the optical properties of seawater

George, Grinson

Monolisha, S

Platt, Trevor

Sathyendranath, Shubha

CMFRI Digital Repository

Winter School on Structure and Function of the Marine Ecosystem : Fisheries240Optical ClassificationMarine OpticsOptical oceanography or Marine optics is the study of light propagation in the oceansurface through absorption or scattering processes. Marine bio-optics is the term usedwhen the absorption and scattering by particles and dissolved substances are of biologicalorigin. Ocean color is defined as the spectral variation of the water leaving radiance thatcan be related to the optical constituents present in the medium (Jerlov, 1976; Morel, 1974).Visible Spectral radiometry or Ocean colour remote sensing is the study on spectral signalsof optically active materials using satellite observations. When sunlight reaches the upperwater column or the photic zone of the ocean surface, the light propagation is determinedby the optical properties of seawater.  It depends on concentration of the optical constituentsof seawater containing Coloured dissolved organic matter (CDOM), suspended sedimentsand phytoplankton (IOCCG, 2000). The contribution of particulate and dissolved constituentsto the variability of optical properties and ocean color in coastal waters requires a betterunderstanding of the linkages between the concentration of these constituents, the inherentoptical properties (IOPs) of absorption and scattering coefficients, and the apparent opticalproperties (AOPs) such as the spectral attenuation for downward irradiance Kd(λ) and remotesensing reflectance Rrs(λ). Knowledge of these relationships is important for characterizingthe marine optical environment and developing remote sensing ocean color algorithms forcoastal waters.The approach of Optical classification is the analysis on identification of a water type interms of dissolved and suspended organic and inorganic materials, biological substancesor the phytoplankton diversity. It has practical applications including the quantitativedescription of ocean color and the satellite remote sensing of chlorophyll (Wozniak andPelevin, 1991).  Based on the optical properties, different versions of optical classification ofwater types were proposed by many authors (Jerlov, 1976; Kirk, 1976; Morel and Prieur,1977; Sathyendranath and  Morel, 1989; Smith and Baker, 1977). Jerlov (1976) introduced aclassification of water bodies based on their spectral optical attenuation depth. Jerlovclassified his observations into a set of five typical oceanic spectra and nine typical coastalspectra. Morel and Prieur, (1977) carried out an independent analysis of spectral irradiancewith the aim of describing ocean color in terms of dissolved and suspended material, inparticular, phytoplankton pigment concentrations. Morel’s analysis makes use of the inherentoptical properties of water (Preisendorfer, 1976). Morel and Prieur (1977) presented the28C H A P T E R OPTICAL CLASSIFICATIONMonolisha S., Grinson George, Shubha Sathyendranathand Trevor PlattFishery Resources Assessment DivisionICAR-Central Marine Fisheries Research InstituteWinter School on Structure and Function of the Marine Ecosystem : Fisheries 241Optical Classificationrationale for separating all water masses into two types: Case 1 and Case 2 waters. Accordingto the classif ication system used in remote sensing studies, Case-1 represents thephytoplankton-dominated waters and the clear waters with algal or biological materials,and Case-2 represents all other possible water bodies rich in organic and inorganicsubstances.  In Case-1 water, simple algorithms to retrieve pigment concentrations areused globally, but for Case-2 waters, the use of site-specific algorithms is necessary. Smithand Baker, (1977) used the apparent optical property KT, the optical parameter that relatesthe spectral irradiance just beneath the ocean surface Ed (0, A) to the downwelling spectralirradiance at depth Ed(Z,A). Their classification also provides direct input to mathematicalmodels of phytoplankton dynamics. Sathyendranath and Prieur, (1989) used a triangularplot, on which a point would represent the relative contributions from phytoplankton, non-algal particles, and CDOM to the total absorption coefficient (excluding that of pure seawater),at a specific wavelength. A point at the center of the triangle designates equal contributionsby each component, while each point at the apex designates cases where all the contributionis from a single component. There are several studies on optical classification of the watertypes based on insitu optical measurements by Hoepffner, 2005; McKee and Cunningham,2006; Reinart et al., 2003; Roff et al., 2003. Babin et al., 2003 gave results based on absorptionat various wavelengths for European coastal waters. The study explains how at differentwavelengths different constituents (dissolved material, algae and non-algal particles)dominate absorption. Mélin and Vantrepotte, (2015) classified the global ocean into sixteenoptical classes using the normalised remotely sensed reflectance data, an apparent opticalproperty. He also classified the water types of each class into Case-1 and Case-2 using themean spectral reflectance values. Recent studies on optical classification in the Indian coastusing the empirical algorithms for the retrieval of chl-a from CZCS, MOS-B, IRS-P4-OCMand Sea-WiFS were carried out in the southeastern Arabian Sea (Nagamani et al., 2008;Chauhan et al., 2002; Sathe and Jadhav, 2001). Study on the phytoplankton communitycharacteristics using absorption properties in the coastal waters of the southeastern ArabianSea was done by  Minu et al., 2014; Minu et al., 2016, measured the in-situ remote sensingreflectance (Rrs) and optically active substances (OAS) using hyper spectral radiometer andclassified the coastal waters of the southeastern Arabian Sea. The authors proposed threedistinct water types: Type-I, Type-II and Type-III based on variability in Optically ActiveSubstance (OAS) such as chlorophyll-a (chl-a), chromophoric dissolved organic matter(CDOM) and volume scattering function at 650 nm (β650).Optical classification of Northern Indian OceanWe made an initial attempt to optically classify the coastal waters of the Northern IndianOcean using remotely-sensed ocean colour datasets. Monthly climatological dataset ofremote sensing reflectance for the years 1998 – 2013 was obtained from the Ocean ColourWinter School on Structure and Function of the Marine Ecosystem : Fisheries242Optical ClassificationClimate Change Initiative (OC-CCI, www.oceancolour.org). Normalization and log-transformation of remote sensing reflectance values were done according to the method ofMélin and Vantrepotte (2015). Optical classification implemented uses the fuzzy logicclassification method, based on Moore et. al., (2009). Optimal cluster validity methods suchas Xie-Beni Index and Partition Coefficient are computed to determine the optimal cluster(class) number to perform the classification.Optical classification techniques used for the study:Cluster validity measuresCluster validity measures are chosen to validate the quality of clustering algorithms.Cluster validity methods are statistical functions that determine the performance of aclustering procedure. Criteria of merit for a clustering method includes the distance betweenclusters (separation) and the distribution of points around a cluster (compactness) (Deborahet al., 2010). We can rely on multiple validity functions to aid selection of the optimal clusternumber. The principal strategy used is to cluster the data over a range of cluster values (nc)and evaluate each clustering result with each validity function (Moore et al., 2009).The Partition co-efficient and the Xie-Beni index are cluster validity methods designedspecifically for use with fuzzy algorithms. These two methods are preferred to select theoptimal number of clusters in fuzzy classification (Halkidi et al., 2002).Xie-Beni IndexXie-Beni index is used to determine the best cluster number for the fuzzy classificationmethod in a particular application. Xie-Beni index depends on the geometric properties ofthe dataset and the membership matrix. This index is defined as the ratio of the meanquadratic error to the minimum of squared distances between all points in the cluster.  Incluster validation, the quadratic error is defined as the mean of the squared distances of allthe points with respect to the centroid of the cluster they belong to (Xie and Beni, 1991).The variation in cluster i, (i = {1,…,nc}) is designated as ói, it is the sum of the squares ofthe fuzzy deviation of the data points in dataset X. The average variation in cluster i,ηi= (σi/ηi), ηi is the number of points in the cluster belonging to the cluster i.  Xie-Beni indexis defined as XB = η/N*Dmin,, N is the number of points in the dataset and Dmin is the minimumdistance between the centroids of the clusters.The smallest value of index indicates the optimal cluster number. When Xie-Beni index ismonotonically decreasing, the number of clusters nc becomes very large and close to ni.One way to eliminate the decreasing tendency of the index is to determine a starting point,the maximum of the cluster number (cmax) of the monotonic behavior and to search for theWinter School on Structure and Function of the Marine Ecosystem : Fisheries 243Optical Classificationminimum value of Xie-Beni in the range [2, cmax]. Moreover, the values of the Xie-Beniindex  depend on the fuzzy membership values (F) and the maximum cluster number of thedatasets (Halkidi et al., 2002; Zhao et al., 2009).Partition Co-efficientThe Partition Coefficient is a validity function that uses the membership values (Fij) toprovide the best cluster number.  It measures the amount of “overlap” between clusters.Partition co-efficient (PC) is defined as follows:N nc1PC = –––Σ Σ Fij2N i=j j=1The PC index values lie in the range [1/nc, 1], where nc is the number of clusters. Thecloser this value is to one the better the data are classified. The cluster number with amaximum partition coefficient is said to be the best cluster number to choose forclassification. In case of a hard partition, we obtain the maximum value PC(nc) = 1. Thedisadvantages of the partition coefficient are its monotonic decrease with cluster numbersnc (Bezdek, 1974; Bezdek et al., 1984).Fuzzy C Mean ClassificationFuzzy classification evolved from classical set theory. The classical clustering approachdetermines whether the object is a member or non-member of a given set of that system.In contrast, fuzzy logic allows that an object or data may have partial memberships of morethan one set. This method allows for overlap between boundaries of particular classes orsets, and recognizes that more than one class may be represented at a particular location atany given time. The membership Fij of a class in the data from a particular point is given by(1-Fij(Zij2)) where Zij is the Mahalanobis distance given by (X-M)/S where M is the mean andS is the standard deviation, and F is a cumulative χ2 distribution (Zadeh, 1965).Results of the studyThe cluster validity measures showed that the cluster number 8 gave the best compromise,with low numbers of both under-classified and over-classified pixels. Therefore, eight classeswere selected as the optimal cluster number for further analyses. Preliminary analyses ofoptical classification showed month-to-month variations (See Figure 1.Optical classificationfor the summer monsoon seasons - June to September). The mean spectra of the eightselected optical classes were calculated and shown in Figure 2. The optical classes alsorelates to Case-1 and Case-2 waters as defined by Morel and Prieur (1977); Prieur andSathyendranath, (1989) based on spectral shapes. From the shapes of the spectra, it appearsWinter School on Structure and Function of the Marine Ecosystem : Fisheries244Optical Classificationthat classes 1-6 are representative of Case-1 waters and classes 6-8 of turbid Case-2 waters.On further, the resultant optical classes are analyzed to explore their biological significanceusing the available distribution datasets of different taxonomic groups of phytoplanktonand zooplankton from literature.Fig. 1. Optical classification of the monthly climatology datasets of summer monsoon seasonAlthough there have been many biogeographicstudies of the ocean with various approaches and manyapplications of remote-sensing to partition the oceansinto ecological zones or provinces, studies thatintegrates conventional biogeography with the resultsfrom remote sensing have been relatively few.Application of remotely sensed data to classify anyoptically distinct regions can result in providingunderstanding on the various bio-geochemical cyclesand dynamics of the ocean regions.Fig. 2. Mean Reflectance Spectra of theLog10-normalised Rrs valuesWinter School on Structure and Function of the Marine Ecosystem : Fisheries 245Optical ClassificationReferencesBabin, M., Stramski, D., Ferrari, G. M., Claustre, H., Bricaud, A., Obolensky, G., et al., (2003). Variations in thelight absorption coefficients of phytoplankton , nonalgal particles and dissolved organic matter incoastal waters around Europe. J. Geophys. 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A theoretical analysis of the contribution of algal cells to the attenuation of light withinnatural waters. New Phytol. 77, 341–358. doi:10.1111/j.1469-8137.1976.tb01524.x.McKee, D., and Cunningham, A. (2006). Identification and characterisation of two optical water types in theIrish Sea from in situ inherent optical properties and seawater constituents. Estuar. Coast. Shelf Sci. 68,305–316. doi:10.1016/j.ecss.2006.02.010.Mélin, F., and Vantrepotte, V. (2015). How optically diverse is the coastal ocean? Remote Sens. Environ. 160,235–251. doi:10.1016/j.rse.2015.01.023.Minu P., Shaju S. S, Muhammed Ashraf P., Meenakumari, B. (2014). Phytoplankton community characteristicsin the coastal waters of the southeastern Arabian Sea. Acta Oceanoglogia 33, 170–179. doi:10.1007/s13131-014-0571-x.Minu, P., Lotliker, A. A., Shaju, S. S., Ashraf, P. M., Kumar, T. S., and Meenakumari, B. (2016). 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Lett. 5, 527–531. doi:10.1109/LGRS.2008.923213.Winter School on Structure and Function of the Marine Ecosystem : Fisheries246Optical ClassificationChauhan, P., Mohan, M., Sarngi, R.K., MeenaKumari, B., Nayak, S. and  Matondkar, S.G.P. (2002). Surfacechlorophyll-a estimation in the Arabian Sea using IRS-P4 Ocean Colour Monitor (OCM) satellite data.Int. J. Remote Sens. 23, 1663–1676. doi:http://dx.doi.org/10.1080/01431160110075866.Preisendorfer, R. W. (1976). Hydrologic Optics. Honolulu/ : U.S. Dept. of Commerce, National Oceanic andAtmospheric Administration, Environmental Research Laboratories, Pacific Marine EnvironmentalLaboratory Available at: http://udspace.udel.edu/handle/19716/1577.Reinart, A., Herlevi, A., Arst, H., and Sipelgas, L. (2003). Preliminary optical classification of lakes and coastalwaters in Estonia and south Finland. J. Sea Res. 49, 357–366. doi:10.1016/S1385-1101(03)00019-4.IOCCG (2000). 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Optical classification

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Optical classification

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