Keywords: hyperspectral, SPOT HRV, coral reef degradation.
Abstract
Analysis of hyperspectral data has produced encouraging results in the discrimination of common and
optically similar coral reef substrates such as healthy corals, bleached corals, sea grass, and algae-covered
surfaces, but at the present time, such high spectral resolution data is unavailable from a satellite platform. If
currently available satellite imagery is to be used to map and monitor changes in coral reef geographic extent
and health, a quantitative procedure must be developed to discriminate healthy coral from other optically similar
benthic substrates with coarse spectral resolution. The primary goal of this study is to evaluate the feasibility of
using coarse spectral resolution data to map the geographic extent and monitor the changes in coral reef
ecosystems. While previous studies have based analysis upon reflectance values extracted from images, an
attempt is made here to discriminate common coral reef features using in situ spectral reflectance measurements
with spectral resolution equivalent to SPOT HRV data. Results of a one-way analysis of variance suggest that
the broad categories of in situ reflectance measurements (n=596) can be considered separate populations with
respect to broadband reflectance characteristics.s
1. Introduction
Coral reef ecosystems are difficult and expensive to survey on a regular basis, so remote sensing is being
explored as a feasible alternative to aerial photography and traditional ground survey techniques as a means of
monitoring reef features (Hardy et al., 1992; Bour et al., 1986). Conventional methods of mapping the
geographic extent of coral reefs rely on examination of nautical charts, aerial photography, and underwater
observations, which are accomplished following a sampling methodology dependent on substrate type
(Luczkovich et al., 1993; de Vel and Bour, 1990; Jupp et al., 1985). These conventional techniques may be
sound if the area is small, but they are inadequate for large and/or inaccessible areas, so an alternative means of
monitoring coral ecosystem health is required (Holden and LeDrew, 1998b).
Remote sensing is espoused as an ideal tool for resource management and ecosystem monitoring due to its
ability to provide quantitative information quickly and relatively inexpensively compared to the cost of
employing researchers to observe an equivalent area (Mumby et al., 1998; Hardy et al., 1992). While currently
available satellite imagery has global mapping and monitoring capabilities, the accuracy and precision attainable
is relatively low due to the large pixel size and broad spectral bandwidths of these sensors (Holden, 1999). Due
to the urgency with which coral reef ecosystems need to be mapped and monitored (Wilkinson et al., 1999;
Muller-Parker and D’Elia, 1997), waiting for the ideal technology for accurate and precise spatial, spectral, and
temporal imaging of submerged coral reef ecosystems is not realistic. Instead, there is a need to utilize the
available imaging technology, assess the accuracy and acknowledge the limitations.
The specific objectives of this paper are to examine the in situ spectral reflectance characteristics of
common coral reef features such as algae-covered dead coral, bleached coral, and healthy coral to determine the
degree to which they are discernible at spectral resolutions equivalent to SPOT HRV. We believe our approach
based upon measured in situ spectral reflectance data and calculated SPOT HRV equivalent broadband
reflectance data to be an effective means of identifying individual substrate types. This work is performed in
anticipation of a project using an historical SPOT HRV data archive at the Centre for Remote Imaging, Sensing
and Processing, National University of Singapore, to inspect and document the changes over time in coral reef
geographic extent and health in the South China Sea.
2. Study Areas and Data Collection
Data collection has been on going since 1996 in the South Pacific, South East Asia and the Caribbean in an
effort to create a global database of coral reef reflectance signatures. Field data collection took place in Beqa
Lagoon, Fiji during July and August 1996, Manado, Sulawesi, Indonesia in July and August 1997, Savusavu
Bay, Fiji in July and August 1998, and Buck Island National Monument, St. Croix, U.S. Virgin Islands in
February 1999.
A hyperspectral radiometer (Analytical Spectral Devices Personal Spectrometer II) with a 20m underwater
optical cable and an underwater cosine collector and 22 degree field of view foreoptics allowed reflectance
measurements of submerged features while scuba diving. An underwater reference panel enabled determination
of reflectance by measuring the nadir radiance of the reference panel immediately prior to each nadir radiance
measurement of the feature of interest. The radiometer operator remained above water to take note of sky and
water surface conditions and set the integration time. In an effort to reduce the noise factor within each
measurement, five reflectance measurements were averaged automatically for each saved spectra. The water
present between the radiometer foreoptic and the feature (10cm) is considered negligible, so no correction for
attenuation was performed. Underwater photographs were taken of each of the features measured and notes
were taken describing the depth, feature type, surrounding substrate, water quality, feature size and morphology
as well as any other pertinent information.
In total, 596 spectral reflectance measurements are utilized in this study, each integrated to equal SPOT’s
first two wavebands: (1) 500-590nm and (2) 610-680nm. Only the first two wavebands of the SPOT HRV are
used here since light in the third band (790-890nm) is completely attenuated in the water column. The bottom
type categories were assigned based on identification of the feature in the field and confirmed by inspection of
the underwater photographic record. The broad categories are healthy coral (n=276), bleached coral (n=114),
macro algae (n=105), rubble (n=77), and sea grass (n=24).
3. Spectral Data Analysis
The original measured spectra can be considered hyperspectral since they consist of 205 contiguous
waveband channels with 1.4nm bandwidths. Previous analysis has shown that principal components analysis
(PCA) enables reduction of the dataset to “representative” spectra for each of the broad bottom type categories
defined: healthy coral; bleached coral; algae-covered surfaces; rubble; and seagrass (Holden and LeDrew, 1999;
Myers et al., 1999). Discrimination of bottom type categories was successfully performed using spectral
derivative analysis whereby the slope (first derivative) and change in slope (second derivative) of the spectral
curves in specific wavelength regions was used as a discriminating variable (Holden and LeDrew, in press) and
confirmed by (Myers et al., 1999) using an independent dataset. The wavelength regions allowing
discrimination of bottom type with over 80% accuracy when applied to the entire spectral dataset (n=596) are
shown in Figure 1. The spectral regions are numbered according to the order in which the derivative procedure
follows such that the first derivative is found in region 1 (if positive slope, identify as rubble), then in region 2
(if positive slope, identify as bleached coral), and finally, second derivative is found in region 3 (if positive then
identify as healthy coral) (Holden and LeDrew, in press).
Figure 1. Representative spectra for four bottom types were identified using principal components analysis, and
regions were identified to allow discrimination of bottom type based derivative analysis.
Because such high spectral resolution data is not available from a satellite platform at this time, equivalent
medium resolution reflectance was calculated to simulate the expected SPOT HRV response. The in situ
spectral dataset was investigated to determine the relationship between SPOT bands 1 (500-590nm) and 2 (610-
680nm). The measured reflectance values for SPOT band 1 and SPOT band 2 are highly correlated (multiple R
= 0.853). Considering only SPOT band 1 reflectance (because of this strong association), the cross correlation
coefficients for all substrate types are shown in Table 1. The correlation coefficients are generally small
indicating a weak relationship between SPOT 1 reflectance and the 5 groups of substrate types. Only the
correlation coefficient for rubble vs. sea grass (-0.73) is encouraging with respect to discrimination.
Nevertheless, further examination of the coarse spectral resolution dataset is required to determine the extent to
which identification and discrimination is possible on a SPOT image within a certain error range.
Table 1. Correlation coefficients of SPOT1 reflectance values.
| |
Grass |
Rubble |
Algae |
Bleached |
Healthy |
| Grass |
1.00 |
|
|
|
|
| Rubble |
-0.73 |
1.00 |
|
|
|
| Algae |
0.09 |
-0.38 |
1.00 |
|
|
| Bleached |
0.28 |
-0.13 |
0.00 |
1.00 |
|
| Healthy |
-0.41 |
0.22 |
-0.10 |
0.03 |
1.00 |
