Hyperspectral discrimination of coral reef benthic ...

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Acknowledgments. This research was supported by the Darwin Initiative, Onassis. Foundation, Carnegie Trust, Reef U.K and the Moray Development Fund. It.
Hyperspectral discrimination of coral reef benthic communities Evanthia Karpouzli and Tim Malthus School of Geosciences, University of Edinburgh, Edinburgh, U.K Fax: +44-131-6502524 E-mail: [email protected] Abstract- This study sought to extend the spectral library of reef associated species found in the literature as well as to investigate variation in optical reflectance within colonies of the same species from different geographical regions. We tested the spectral discrimination of species and taxonomic groups using a hierarchical classification system, to compare results from hyperspectral reflectance and derivative datasets to those simulated for three visible multispectral wavebands typical of the high spatial resolution optical sensors (e.g. IKONOS and QuickBird). Keywords: Coral reefs; San Andres; spectral reflectance; remote sensing; discriminant analysis, derivatives



In situ spectroradiometric measurements play an important role in the development of remote sensing applications. Analysis of these data may be used to determine optimal bandwidths, appropriate spectral and radiometric resolutions, and ways to analyse remotely sensed information to discriminate community types and species [1,2,3]. More recent studies have used in situ high spectral resolution measurements to investigate the discrimination of broad classes of coral, algae and sand [1,4,5], and have indicated the possibility of discriminating healthy from stressed corals [6,7,8,9]. However, a consistent methodology for measuring reflectance spectra of reef biota has been lacking. Standardisation of methods is required before the causes and extent of spectral variation between and within species can be understood [9], and greater replication is required to determine the degree of variation in reflectance within colonies of the same species and between different species of reef organisms [10]. II.


Using a Geophysical Environmental Research 1500 spectroradiometer fitted with a fibre optic a comprehensive set of 178 spectral reflectances were collected from the coastal waters of the Archipelago of San Andres and Providencia (Western Caribbean) in April 1999 and October 2000. They included in total twenty-eight species of healthy and diseased scleractinian corals, gorgonians, fire corals, green and brown algae, seagrasses, sponges, and differing sediment types. The non-healthy coral were categorized

according to [8] in the following groups: bleached, recently dead, and old dead. Species reflectance measurements were made in two distinct ways depending on the depth of the site and the availability of instruments. Submerged in situ measurements were made for sites less than 3 m in depth, and at deeper sites the samples were removed and measured above surface (emergent measurements). A robust methodology of water column correction and comprehensively defined expressions of radiance reflectance was applied to standardize the measurements made underwater so that they could be analysed as one group. This methodology used measurements of in situ light transmittance across the airwater interface, spectral attenuation, and water depth. Full description of this methodology can be found in [11]. Reflectance features were identified in the reflectance and first-order derivative spectra after smoothing [12,13]. Discriminant function analysis (DFA) was used to test the discrimination of species and sediment types at three hierarchical levels of descriptive resolution: Coarse resolution, (6 classes: healthy coral, diseased coral, algae, seagrass, sediment, and sponges), medium (order level) resolution (9 classes: brown algae, green algae, fire corals, gorgonian corals, scleractinian corals, diseased corals, seagrasses, sponges) and fine (species level) resolution (29 classes, each species of healthy coral, diseased coral, algae, seagrass and two classes of sediment types). DFA was performed three times using the species and sediment reflectance spectra dataset, their first order derivatives, and a dataset of simulated broad band reflectances of each species corresponding to the three visible IKONOS bands. The discriminant (or canonical) functions were calculated as linear combinations of the wavelengths or bands that best separated the classes. The relative importance of the variables (wavelengths or bands) selected by DFA were assessed by referring to the values of their standardized discriminant (beta) coefficients, and the success of the classifications for each dataset was assessed by comparing the a posteriori classification of the spectra to their a priori membership and calculating their classification success. The Wilks’ Lambda significance test was also used to assess the success of the classification.


healthy scleractinian coral spectra exhibited a reflectance maximum at around 600 nm, and a shoulder at around 650 nm. A shoulder at around 570 nm was generally observed in all healthy scleractinians and gorgonians.


Reflectances and first derivative spectra for the coarse descriptive level resolution are shown in Figure 1. Curves for the other levels can be found in [11].

There was less variation in shape and magnitude within the spectra of the algae species in comparison to the coral spectra which varied on the basis of pigment content. Seagrasses had a similar shaped reflectances as green algae, but showed a reflectance peak at 553 nm instead of 562 nm. Syringodium filiforme showed higher reflectance compared to Thalassia testudinum in the green region of the spectrum, indicative of its brighter yellowish green colour.

40 Red Sponge Sediment Algae Seagrasses Corals Non-healthy Corals

Apparent Reflectance (%)

35 30 25 20

The shape and magnitude of the non-healthy coral spectra varied considerably depending on their condition. Bleached coral had a distinct shape similar to sand and rubble. Recent dead coral before algal colonization showed flatter reflectance lacking peaks and dips found in living coral spectra, especially in the region of the red edge indicating lack of photosynthetic organisms. Spectra from old dead corals that were colonized by algae showed the reflectance properties of the algae themselves rather than the dead coral underneath.

15 10 5

0 0.8

Red Sponge Sediment Algae Seagrasses Corals Non-healthy Corals First Derivative

The first-order derivative spectra were useful in highlighting more subtle variations in spectral reflectances. This was demonstrated by the results of the DFA which yielded higher accuracy results using derivatives in all levels of classification.

-0.2 400



550 Wavelength (nm)



Figure 1. Mean apparent radiance reflectance spectra (top graphs) and first derivatives spectra (bottom graphs) of each of the coarse descriptive level resolution classes.

The sediment spectra had the highest reflectance values, and showed the least variation in shape. San Andres sediment types, of coralline origin, exhibited higher reflectances compared to the volcanic sediments from Providencia. Healthy coral, algae, seagrass and sponge species had lower reflectances compared to the sediment types. Bare sediments around the islands could thus be easily separated from other bottom types on the basis of brightness alone. All healthy photosynthetic organisms (coral, algae, and seagrass species) displayed a reflectance minimum at approximately 670 nm, related to the presence of chlorophyll α. That minimum was not found in the sponge spectrum but was exhibited weakly in some of the San Andres sediment spectra indicating the possible presence of benthic microalgae within the sediment samples. It was also less pronounced in the reflectance spectra of recent dead corals not yet overgrown by algae but was absent from bleached corals that have lost their symbiotic algae. The coral reflectance spectra showed the most variation in shape and magnitude among species in comparison with the other groups. Although some variation existed, most


The DFA yielded accuracies ranging from 92-99% based on 20-25 non-contiguous wavebands (eg. 418, 424, 434, 442, 486, 494, 512, 524, 546, 564, 590, 610, 616, 628, 638, 656, 670, 682, 698, 700 nm) for the derivatives dataset. Second best accuracy levels were achieved using the hyperspectral reflectance dataset with accuracies ranging from 72-86 % based on as few as 5-6 non-contiguous wavebands. Discrimination on the IKONOS wavebands yielded the lowest accuracies (31-68 % depending on the level of the classification).

Classes Algae Healthy corals Diseased corals Sediment Seagrass Sponges

Figure 2. Scores from the DFA to coarse level classes using the derivative dataset projected into the discriminant function space of the two first functions that separate best the groups. (ellipses are drawn to aid the eye only and do not represent confidence intervals).

e.g. [15]. Alternatively the spectra measured in this study could serve as end-members in a sub-pixel based analysis of species contributions (spectral mixture modeling). These, and other influences such as that of water column correction warrant further investigation. Brown algae Green algae Fire corals Gorgonian corals Scleractinian corals Diseased corals Seagrass Sponges Sediment

Acknowledgments This research was supported by the Darwin Initiative, Onassis Foundation, Carnegie Trust, Reef U.K and the Moray Development Fund. It was conducted in collaboration with CORALINA in San Andres. The spectroradiometer was obtained on loan from the NERC Equipment Pool for Field Spectroscopy, UK. Special thanks are extended to Phil Lovell, Callan Duck, and James Mair for their valuable help during fieldwork.

References [1]

Figure 3. Scores from the DFA to medium level classes using the derivative dataset projected into the discriminant function space of the two first functions that separate best the groups. (ellipses are drawn to aid the eye only and do not represent confidence intervals).

For all datasets the classification accuracies obtained were dependent on the level of the classification, but surprisingly the highest accuracies were observed at the fine level of descriptive resolution (species level) followed by the coarser level. The choice of the classification scheme i.e. the way the members are assigned a priori memberships is thus of critical importance to how well DFA will perform since this will affect the between-group and within-group variances. The reasons why the coarser level classifications yielded slightly lower accuracies than the fine level is partly attributed to the way the classes were assigned at these two levels, suggesting that the grouping at species level was more appropriate in yielding more distinct clusters. The number of members in each class in the fine resolution was smaller than the number in each of the medium or coarse resolution classes, and this would also be expected to affect the analysis. The same number of individuals divided in a larger number of groups represents an easier classification problem due to the lower number of individuals in each group, therefore improving the classification. The limited capability of broad band sensors for shallowwater mapping in comparison to hyperspectral sensors (eg CASI) is well documented, (i.e. [14,15,16]). At the coarse and medium resolution scale only sediment, sponges and one species of seagrass could be well discriminated on the basis of IKONOS band reflectances while the algal and coral classes had a large potential for misclassification. However, at the species level the IKONOS data showed an improved classification accuracy, and in addition to the sediment and seagrass classes a number of coral species (Diploria sp., Siderastrea sp., A. agaricites, A. undata, Acropora sp., and Millepora), algal species (Halimeda opuntia, Microdictyon sp.) and the seagrass Syringodium were also discriminated. The higher spatial resolution offered by the IKONOS and Quickbird spaceborne sensors offers increased classification accuracy if individual pixels are dominated by single species and on the basis of other spatial information such as texture



[4] [5]






[11] [12]





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