Detecting the 2006 coral bleaching event at Keppel Isles using MERIS FR data: a feasibility study

Transcription

1 Detecting the 2006 coral bleaching event at Keppel Isles using MERIS FR data: a feasibility study Prepared for the Great Barrier Reef Marine Park Authority by M Wettle, A G Dekker, and D Blondeau-Patissier CSIRO Land and Water Science Report 69/06 October 2006

2 Copyright and Disclaimer 2006 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO Land and Water. Important Disclaimer: CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. ISSN:

3 Keppel Isles using MERIS FR data: a feasibility study Prepared for the Great Barrier Reef Marine Park Authority by M Wettle, A G Dekker, and D Blondeau-Patissier CSIRO Land and Water October 2006 Keppel Isles using MERIS FR data Page i

4 Executive summary Following encouraging results from a physics-based remote sensing approach using MERIS sensor data to detect a minor coral bleaching event at Heron Island (Wettle, 2005; Wettle et al 2005), work was undertaken to develop and test this approach for Great Barrier Reef-wide application. Four months of continuous full resolution MERIS acquisition covering the entire Great Barrier Reef were ordered from the European Space Agency to coincide with a Great Barrier Reef-wide coral bleaching event anticipated for the Austral summer of Benthic survey results revealed that the anticipated bleaching event was intense but confined to localised areas surrounding the Keppel Isles. The physics-based approach was not able to be successfully tested on the identified bleaching locations, as the reefs experiencing bleaching were adjacent to land features (the Keppel Isles) and/or did not cover a surface area representing a sufficient amount of contiguous MERIS pixels. Difficulties posed by the limited spatial extent and proximity to land of the bleached Keppel reefs were compounded by (terrestrially-sourced) plumes of turbid waters obscuring the reefs being imaged. Although the change in benthic substrate cover as estimated by our approach conformed with the timing of the surveyed bleaching event, it was not possible to discount this as a coincidence due to the lack of available pixels. The MERIS approach should be applied to a whole of reef scale rather than individual reefs. The Keppel Islands were (in retrospect) not suitable for testing this approach compared to a typical GBR reef as they are fringing reefs with exposed land around them. Our MERIS approach needs preferably large submerged reef areas versus little or no land (rock or reef).the results reported here do not confirm or reject the MERIS approach. The MERIS Full resolution imagery coral bleaching approach remains valid as it has not been proven to not work. It needs applications at the advised scale of whole of (sections of) GBR scale or applied to a suitable large subset (say 30 to 50 suitable reefs). For single reef systems and for fringing reef systems around islands (e.g. the Whitsundays and Keppels) higher spatial resolution satellite images are advised. We perceive the role of a MERIS-based approach as that of initial, broad-scale, GBR-wide detection of bleaching, where high resolution sensors such as Quickbird can then be focused on identified locations. A strategic way forward is to investigate at whole of reef scale (in order to try and avoid difficulties posed by proximity of land, geo-location discrepancies and insufficient number of available pixels) the contents of the MERIS data of 2006 and to order another bleaching season coverage for 2007 from the European Space Agency (in case a suitable large bleaching event occurs). Keppel Isles using MERIS FR data Page ii

5 Acknowledgements This work was funded by the Great Barrier Reef Marine Park Authority (GBRMPA). We also thank GBRMPA for inviting us to partake in the Southern GBR March 2006 fieldwork, Garry McKechnie and the crew of Pelican 1 for their enthusiasm and support, and Paul Daniel of CSIRO Land and Water for help with preparing and testing and the fieldwork equipment. Chris Roelfsema and Stuart Phinn of University of Queensland are thanked for both fieldwork support, and field and image data. SAMBUCA was developed in collaboration with Vittorio Brando of CSIRO Land and Water. FR MERIS data was generously provided by the European Space Agency. Keppel Isles using MERIS FR data Page iii

6 Table of Contents Executive summary...ii Table of Contents... iv 1. Introduction Monitoring coral bleaching Remote sensing A physics-based model Optical properties of coral waters A space-borne sensor Applying a physics-based approach to MERIS data in order to detect bleaching Aims and objectives Specific objectives SAMBUCA: Semi-Analytical Model for Bathymetry, Un-mixing, and Concentration Assessment Optical properties of coral waters and benthos Introduction Optical properties of Southern GBR waters MERIS imagery MERIS introduction MERIS products and pre-processing MERIS FR Keppel Isles data inventory Qualitative assessment of spatial and spectral variation in subset of MERIS time series Overview of the eight Category 1 MERIS FR scenes MERIS FR absolute geolocation accuracy Spectral variability within the time series Coral bleaching locations and MERIS sampling SAMBUCA parameterisation Benthic substrate reflectance libraries Water column parameterisation and MERIS validation Benthic cover estimation and bleaching detection Conclusions and recommendations References...58 Keppel Isles using MERIS FR data Page iv

7 Report Title Page 1

8 1. Introduction 1.1. Monitoring coral bleaching There is a defined need for effective, regional and worldwide monitoring of the health of coral reef systems, in particular coral bleaching. However, monitoring coral bleaching events is impeded by the transient nature of bleaching events (Holden and LeDrew 1998),(Clark et al. 2000), as well as the extent and remoteness of tropical reefs. The conspicuous bleaching event (where a coral polyp expels its symbiotic zooxanthellae and generally turns a pale to bright shade of white) normally lasts in the order of weeks. The coral will typically then either recover or die. In the latter case, it will be overgrown by colonizing turf algae, similar in appearance (from a remote sensing perspective) to many live corals. The GBR illustrates the problems associated with the remoteness and extent of tropical coral reefs, as it contains approximately 2900 reefs and covers 350,000 km 2. During the last two major bleaching events, (1998 and 2002) The Great Barrier Marine Park Authority (GBRMPA) attempted to establish the extent and locations of the bleaching. Deploying divers for in-water surveys has proved inadequate for such broad scale assessments, and GBRMPA has resorted to deploying observers in aircraft. However, only approximately 600 reefs were able to be surveyed each year. Furthermore, this approach has several drawbacks, including the subjectivity of the observers (qualitative data at best), the dependency on flight conditions, as well as the logistical costs in general. Space-borne remote sensing, with its repetitive, broad scale coverage providing quantitative data in a spatial context, is often cited as a potential alternative tool for monitoring these ephemeral and often remote bleaching events, but this has yet to be proven Remote sensing For a review of the radiative transfer theory (including a comprehensive summary of relevant equations, formulas and nomenclature as used in this report), and selected case studies of aquatic remote sensing, we refer to (Dekker et al. 2001) A physics-based model The bulk of coral reef remote sensing work to date has been image-based. Examples include exploring statistics within the imagery, developing band ratios and indices in an effort to circumvent the difficulties posed by bathymetry and water color, and incorporating site specific data into supervised classifications. These approaches have the advantage of often being relatively straightforward to apply, but offer several limitations including that they tend to be site specific, sensor specific, and/or time specific (Kutser et al. 2003). An alternative to these approaches is a physics-based, radiative transfer method. Advantages to such methods include: the application of one algorithm to time series of remote sensing data, the ability to simulate the relevance of various sensors to a particular application, and a reduction in the amount of field and laboratory-based measurements (Dekker et al. 2001). A drawback to the physics-based approach is that it is relatively complex in its application. In the case of coral remote sensing from satellite sensors, a physics-based method would allow for temporal and spatial variations in the reef water optical properties and be able to cope with spatially varying bathymetry. This is in contrast to the afore mentioned image- Keppel Isles using MERIS FR data Page 2

9 based methods, which typically need to be calibrated using site-specific data on a case-bycase basis. Such a physics-based method could therefore significantly reduce the requirement for field or aerial survey approaches for mapping bleaching using remote sensing. Recent reviews on the status of mapping coral reefs demonstrate the need for a mapping procedure able to work across a variety of depths, water column types and substrates. Several special journal issues on this subject ((Special Edition 2003b) and (Special Edition 2003a)) cover a variety of mapping approaches, all highlighting problems of lack of repeatable techniques in variable water depth, color and substrate type. Previous attempts to solve this problem have extracted depth, water column constituents or substrate, but not all three (Goodman and Ustin. 2003),(Hedley and Mumby 2003) Optical properties of coral waters An important limitation of current coral reef remote sensing work is the lack of detailed in situ measurements of the optical properties of coral reef waters. Aside from (Boss and Zaneveld 2003) and (Kutser et al. 2003), there is little published work in this area. A consequence of this is that coral reef remote sensing studies, such as the radiative transfer modeling done by Yamano and Tamura (2004), assume that the water column overlying corals is of the ocean type Case 1 (phytoplankton dominated water). We have reason to believe this is a wrong assumption (see section 2.2) A space-borne sensor It is generally agreed that coral remote sensing applications require data of sufficient spectral resolution in order to account for the heterogeneity of coral benthos and the confounding effects of the overlying water column. Several imaging spectrometry sensors offer this resolution (e.g. CASI and HYMAP), but deploying such airborne platforms with the frequency and distribution necessary to monitor bleaching events is prohibitively expensive. There are several space-borne sensors available (Table 1) for coral mapping, notably the hyperspectral sensor Hyperion, but to date none has offered the combination of frequent re-visit time, spatial cover, high radiometric sensitivity, and sufficient spectral resolution (as perceived necessary for detecting bleaching events) as does the recently launched MERIS sensor. Keppel Isles using MERIS FR data Page 3

10 Table 1. Specifications of space-borne sensors potentially available for coral reef remote sensing applications. Launch Viewing swath Global coverage (revisit time) Bands in the visible Spatial Resolution SEAWIFS August km 1 day m MODIS Terra: December 1999 Aqua: May km 1-2 days m 500 m 250 m MERIS March km 2-3 days m (RR) 300 m (FR) Landsat Landsat 7 April 1999 SPOT Quickbird IKONOS Hyperion Spot 5 May 2002 October 2001 September 1999 November km 16 days 3 30 m 60 km 1 4 days 3 20 m 16.5 km 1 3 days m 11.3 km 3-5 days 3 4 m 7.5 km 16 days m 1.6. Applying a physics-based approach to MERIS data in order to detect bleaching Accordingly, Wettle (2005) and Wettle et al (2005) implemented a physics-based approach and applied it to MERIS data in order to try and detect a validated bleaching event at Heron Island in Using optical properties of the waters and benthos measured in situ, the model was parameterized and applied to three MERIS scenes of Heron Island reef, two of which were known to contain bleaching and the third presumed to contain no bleaching. Despite the difficulties posed by the relatively coarse spatial resolution of the MERIS full resolution (FR) data, the results suggested that they were able to detect changes in benthic substrate composition that pointed to the confirmed bleaching event. Specifically they detected a reef-wide brightening of the benthic substratum along the seaward perimeter of the reef for the two scenes acquired during the bleaching event. Although the results were encouraging, it was concluded that the approach needs testing using a larger time series of images, preferably over a larger geographic area: the latter would lend statistical validity to the approach and the former should help circumvent problems (which were encountered) of sensors alignment and pixel miss-registration between scenes. Keppel Isles using MERIS FR data Page 4

11 1.7. Aims and objectives The purpose of this project is to 1) further investigate the potential for applying a physicsbased approach to MERIS data using a larger image data set for the Keppel Islands and, optionally, 2) explore alternative, broad scale change detection techniques for mapping coral bleaching events. Ultimately, this could provide the basis for a coral health monitoring system. However, we must emphasize that the research presented here, as it focuses on the Keppel Islands, does not have the scale benefit of looking at all the reefs in the GBRWHA. This project must be seen as a stepping stone towards the larger goal of all-of-reef bleaching detection and monitoring Specific objectives Determine/define the optical properties of waters for the study area Acquire a time series of MERIS FR data containing the Keppel Isles and spanning (approximately January 2006 to -June 2006) a known bleaching event. Undertake an inventory and initial assessment of this unique MERIS FR (Full Resolution at 300 m pixels-standard is RR Reduced Resolution at 1200 m pixels) data set, including estimates of re-visit frequency and data quality. Perform initial geo-correction of MERIS time series in order to evaluate: - spatial accuracy/variability - spectral variability (including specifically in relation to bleaching) Parameterise CSIRO SAMBUCA code for inversion of MERIS images to bleaching maps Atmospheric correction/validation of MERIS data Apply SAMBUCA to MERIS data in order to try and detect coral bleaching and Create MERIS-based coral bleaching maps (provided the satellite sensor data quality is sufficient) Following an introduction to our physics-based approach below, the outline of this interim report broadly follows these objectives. Keppel Isles using MERIS FR data Page 5

12 1.8. SAMBUCA: Semi-Analytical Model for Bathymetry, Un-mixing, and Concentration Assessment At the core of our physics-based method, we chose to implement a modified version of the semi-analytical model and optimization approach originally developed by (Lee et al. 1999;Lee et al. 2001;Lee et al. 1998), dubbed SAMBUCA (Semi-Analytical Model for Bathymetry, Un-mixing, and Concentration Assessment). A detailed description of SAMBUCA is provided by Wettle and Brando (2006). The essence of the approach lies in expressing the measured remote sensing reflectance r rs measured (obtained from remote sensing image data) as a function of a set of simple variables. This modeled remote sensing reflectance, r rs modelled, is then compared to r rs measured using a goodness-of-fit or, error function. The set of variables that minimizes the difference between these two spectra is retained as the result of the minimization. These variables are then used to estimate the environmental variables being sought. Taking the work reported here as an example, SAMBUCA estimates the concentrations of optically active constituents in the water column, chlorophyll, CDOM (colored dissolved organic matter) and tripton (nonalgal component of seston or total suspended matter); water column depth; and benthic substrate composition that produces the best fit between modeled and measured r rs. These five environmental parameters are solved on a pixel-by-pixel basis. The complete model parameterization is given below. r rs * ( ) mod elled f CCHL, CCDOM, CTR, X PHY, X TR, q1, H, SC, STR, atr 0, Y where C CHL is the concentration of chlorophyll a C CDOM is the concentration of CDOM where a* CDOM (550) is set to 1 S C is the slope of the CDOM absorption C TR is the concentration of tripton S TR is the slope of tripton absorption a* TR (550) is specific absorption of tripton at 550nm, which is sample dependent X PHY is the specific backscattering due to phytoplankton X TR is the specific backscattering due to tripton q1 is the ratio of substrate 1 to substrate 2 within each pixel Note that the set of environmental parameters for which SAMBUCA solves is configurable, and that SAMBUCA typically solves for water column depth, substrate composition, the concentrations of chlorophyll, CDOM, tripton. The remaining variables are determined through field work and laboratory analysis. Keppel Isles using MERIS FR data Page 6

13 The algorithm has been modified to account for more than one substrate. For the work presented here, we allowed for the presence of two substrates within each pixel. The relative composition of each substrate is determined by the variable q1, described above. SAMBUCA cycles through a given spectral library, retaining the two substrates that allow for the best spectral fit. As an option, SAMBUCA can be parameterized to account for more than one optical domain within a scene, choosing the domain that provides the best fit on a pixel-bypixel basis. Each inverted variable, as well as the substrate combination, can be output in a variety of map formats. SAMBUCA also provides a measure of the goodness-of-fit, between the measured and modeled spectra. In other words, each set of retrieved environmental variables is assigned a confidence rating based on SAMBUCA s ability to model a given subsurface reflectance spectrum. The goodness-of-fit can be estimated according to either a spectral albedo matching function or spectral shape matching function, referred to as f_val or alpha_val, respectively. For the results reported in this study, a product of f_val and alpha_val was used to assess the spectral match. Additional output includes a warning when the measured reflectance is able to be modeled using an optically deep system and the modeled spectrum as generated by SAMBUCA. SAMBUCA can also be used in simulation mode, applicable to, for example, feasibility studies. The model can be run in 'forward' mode in order to generate a set of r rs modelled spectra. For a given set of simulations, one or more variables can be varied, and the effect on r rs modelled can be evaluated. As an example, using a fixed set of concentrations and SIOPs (Specific Inherent Optical Properties), r rs modelled can be calculated for a substrate comprised of increasingly bleached coral through a range of water depths. The resulting subsurface remote sensing reflectance spectra can then be used to evaluate the ability of various remote sensors to detect these changes in bleaching at different water depths. 2. Optical properties of coral waters and benthos 2.1. Introduction When applied to remote sensing data, SAMBUCA should ideally be parameterized with the optical properties of the benthos and waters representative of the study area. A field campaign was undertaken in March 2006 in order to investigate the optical properties of Southern GBR waters, including waters surrounding the Keppel Isles. Results from these measurements will be used to improve the parameterization of SAMBUCA. This work included the deployment of in situ instruments including an AC-9 absorption meter, a HYDROSCAT backscattering profiler, a CTD, and and 3 RAMSES submersible spectroradiometers. Similar data is also available for Heron Island reef waters, collected in May 2004 (Wettle 2005, Wettle et al 2005). Figure 1 displays the majority of the sites visited. It is worth noting that this represents the first and only set of in situ optical property measurements for the outer reefs of the Southern GBR (Swains Reef, Figure 1). During the Southern GBR 2006 field campaign, spectral reflectance measurements were made of corals exhibiting differing degrees of bleaching. This data set complements the Keppel Isles using MERIS FR data Page 7

14 spectral reflectance library of GBR benthic substrates compiled by UQ during the past several years. Results from the reflectance measurements are not included in this report, but will be investigated and used in subsequent parameterization of SAMBUCA. Middle Percy reef 21529S Gannet Cay reef Chinaman North Keppel Middle Island (Keppel) Halfway Island Wreck Reef 50 km One Tree Island Figure 1. MERIS scene showing the field sites visited during the Southern GBR March 2006 field campaign Optical properties of Southern GBR waters Figures 2-6 display - as measured during the Southern GBR March 2006 fieldwork - the total attenuation, vertical attenuation coefficient, total absorption, CDOM absorption, and total backscatter, respectively. For comparison, equivalent measurements taken in Heron Island waters in 2004 are displayed in Figures 7-9 (total absorption, CDOM absorption and total backscatter, respectively). Total beam attenuation (sum of light absorption and scattering coefficients) is seen to be the highest for the Middle Percy Island site, with relatively higher values also recorded for the waters around the Keppel Isles (Figure 2). These sites feature the closest proximity to land, and relatively less clear waters are therefore to be expected. Currents, tide, and substratum further influence these measurements, and the Percy Island site was a sandy bay, and sampling took place after several days of sustained, strong winds. The negative vertical attenuation values reported for the Gannet Cay site (Figure 3) are not plausible (although theoretically possible in the presence of fluorescence) and the processing of these measurements therefore needs to be re-visited before further conclusions are drawn. Keppel Isles using MERIS FR data Page 8

15 The total absorption spectra for the Southern GBR 2006 sites again show relatively higher values for the majority of sites adjacent to islands (Figure 4). One Tree Island (situated relatively close to the mainland) is the exception; featuring low attenuation values comparable to the outer reef sites. However, the hydrology and reef habitat composition of One Tree Island classify it as an offshore reef site (Jo Johnson, pers. comm.), which conforms with the optical water quality findings reported here. Importantly, the range for these spectra is higher than what was found at Heron Island, suggesting that subsequent parameterisations of SAMBUCA may need adjusted absorption-related variables. For technical reasons, only two CDOM absorption measurements were undertaken, at Percy Island and Chinaman Reef. These two sites represent extreme systems for this campaign: Percy Island bay is sediment filled and surrounded by vegetated land, whereas as Chinaman reef is located on the outer edge of the Southern GBR. Figure 5 reveals that CDOM absorption did not vary significantly between these two sites, suggesting that this parameter is not highly variable in the system. Admittedly, two measurements are not enough to verify this hypothesis. The CDOM absorption spectra measured at Heron Island (Figure 8) are very similar in both shape and magnitude, with slightly higher values recorded for waters directly above reefs or in the lagoon. This is attributed to a higher particulate load in waters directly above corals and within the shallow and sediment filled lagoon. Overall, the variability in CDOM across time (two years apart) and locations (pseudo-coastal and outer reef) appears to be minimal for the GBR sites reported on here. With the exception of two measurements taken in the shallow (1-2 m) lagoon at Heron Island (where sediment re-suspension was evident), backscatter coefficient values at 500nm for the Heron data set were in the range of approximately to m -1 (Figure 9). By contrast, the equivalent range of values for the Southern GBR 2006 sites was between and 0.01 m -1 (Figure 6), with the lowest values measured at the two Swains reef sites. All the Keppel Island sites featured backscattering coefficients in the range of the Heron Island data set. c tot (m -1 ) Total attenuation (AC-9) Great Barrier Reef, 07-12/03/ wavelength (nm) One Tree Island Wreck Island Halfway Island, Keppel Isles Middle Island, Keppel Isles North Keppel Island reef no Gannet Cay Chinamon Reef site 1 Chinamon Reef site 2 reef no West bay, Middle Percy Island Figure 2. Total light attenuation as measured by AC-9 for waters during the March 2006 Southern GBR fieldwork.. Keppel Isles using MERIS FR data Page 9

16 Kd (m -1 ) Vertical attenuation coefficient (RAMSES) Great Barrier Reef, 07-12/03/ wavelength (nm) Wreck Island Halfway Island, Keppel Isles Middle Island, Keppel Isles Gannet Cay Chinamon Reef site 1 Chinamon Reef site 2 reef no Figure 3. Vertical attenuation coefficients as calculated from RAMSES data for waters during the March 2006 Southern GBR fieldwork. a tot (m -1 ) Total absorption (AC-9) Great Barrier Reef, 07-12/03/ wavelength (nm) One Tree Island Halfway Island, Keppel Isles Wreck Island Middle Island, Keppel Isles North Keppel Island reef no Gannet Cay Chinamon Reef site 1 Chinamon Reef site 2 reef no West bay, Middle Percy Island Figure 4. Total absorption as measured by AC-9 for waters during the March 2006 Southern GBR fieldwork. This range is larger than for the Heron Island measurements of 2004 (See Fig 7.) Keppel Isles using MERIS FR data Page 10

17 0.25 cdom absorption (AC-9) Great Barrier Reef, 07-12/03/ a cdom (m -1 ) wavelength (nm) Chinamon Reef site 2 West bay, Middle Percy Island Figure 5. CDOM absorption as measured by AC-9 for waters during the March 2006 Southern GBR fieldwork. These values are very similar to the 2004 Heron Island values (See Fig 8) Total backscattering (HS-6) Great Barrier Reef, 07-12/03/ bb (m -1 ) wavelength (nm) Wreck Island Halfway Island, Keppel Isles Middle Island, Keppel Isles North Keppel Island reef no Chinamon Reef site 1 Chinamon Reef site 2 reef no Figure 6. Total backscattering as measured by HS-6 for waters during the March 2006 Southern GBR fieldwork. Keppel Isles using MERIS FR data Page 11

18 a atot (m -1 ) Total absorption (AC-9) Heron Island, 19-24/05/ wavelength (nm) MERIS Calv/Val lagoon triangle transect the bommie blue pools (rising tide) blue pools (draining reef flat) shark bay plate ledge Figure 7. Total absorption as measured by AC-9 for Heron Island reef waters during the May 2004 Heron Island fieldwork. a cdom (m -1 ) CDOM absorption (AC-9) Heron Island, 19-24/05/ wavelength (nm) MERIS Calv/Val lagoon triangle transect plate ledge blue pools (rising tide) blue pools (draining reef flat) the bommie Figure 8. CDOM absorption as measured by AC-9 for Heron Island reef waters during the May 2004 Heron Island fieldwork. Keppel Isles using MERIS FR data Page 12

19 Total backscatter (HS-6) Heron Island, 19-24/05/ bb tot (m -1 ) wavelength (nm) triangle transect MERIS Cal/Val lagoon the bommie shark bay plate ledge Figure 9. Total backscattering as measured by HS-6 for Heron Island reef waters during the May 2004 Heron Island fieldwork. Due to logistical limitations, it was not possible to process water samples on board the vessel for future laboratory analysis during Southern GBR March 2006 campaign. Therefore, no in situ data regarding concentrations of optically active constituents in the water is available. This is necessary in order to determine the SIOPs of the system, which are a critical component in the parameterisation of SAMBUCA. For future remote sensing fieldwork campaigns, we recommend that the necessary space and infrastructure (e.g. sheltered work surface with access to sink and mains outlet) be available on boat. However, SIOP data is available from the previous Heron Island field campaign. By comparing IOPs (Inherent Optical Properties) such as absorption and backscattering of the two systems (see above), a set of possible SIOPs can be constructed. Furthermore, water column concentrations may be able to be derived from other remote sensing products, and in addition, the CSIRO Land and Water remote sensing group has optical water quality data from previous field expeditions in the Keppel Islands area, which can be referred to. This is further investigated in section 6.2, where a suitable parameterisation will need to be constructed for SAMBUCA. 3. MERIS imagery 3.1. MERIS introduction The MERIS sensor is deployed on the ESA (European Space Agency) ENVISAT platform, which was launched in It is a push-broom imaging spectrometer measuring across 15 channels in the visible to near infra-red range. Full resolution data offers approximately 300 m pixel size, with a sensor swath of 1150 km. The platform has a re-visit time of two to three days (depending on latitude) (ESA ). Keppel Isles using MERIS FR data Page 13

20 Due to the high heterogeneity of tropical reef benthos, most coral reef applications of remote sensing rely on high spatial resolution data. The 300 m resolution of the MERIS sensor therefore poses a significant challenge. Figure 10 illustrates the ramifications of this: a grid corresponding to MERIS pixel size is mapped on 4 m IKONOS data of Heron Island reef. Nevertheless, from a regional/global coral bleaching assessment point of view (GBR scale) MERIS may the best compromise available. Related to the problem of spatial resolution is the issue of sensor wobble (experienced by all satellite-based sensors). For two given overpasses of the MERIS sensor, a specific pixel will not represent the exact same target for the two dates. As a rule of thumb, the positional accuracy of a remote sensor is considered to be +/- 0.5 of a pixel. Figure 10. A grid (red lines) corresponding to MERIS pixel size mapped on 4 m IKONOS data of Heron Island reef. Blue lines denote geo-coordinates. Note Wistari Reef in the southwest corner of the image. While acknowledging the potential limitations due to spatial resolution, there is to date no other remote sensing instrument that offers this combination of spectral resolution and temporal/spatial coverage, most suitable for coral bleaching detection MERIS products and pre-processing The primary mission of the MERIS sensor is the measurement of optical water properties such as chlorophyll, total suspended matter, and CDOM in the oceans and specifically coastal areas. Indeed, the MERIS product algorithms, including the atmospheric correction, assume an optically deep system (Doerffer 2002) (the atmospheric correction algorithm is based on zero water leaving radiance in the near-infrared, which will not necessarily be the case where the bottom substrate is contributing to the signal). This demands that care should be taken when using this product for substrate mapping (i.e. optically shallow system), and we were aware that it may prove necessary to derive reflectance (using in-house atmospheric correction procedures) from the MERIS level 1b (normalized water-leaving radiance) data. However, the potential of the MERIS standard level 2 reflectance product Keppel Isles using MERIS FR data Page 14

21 (derived from standard MERIS atmospheric correction algorithms on level 1 data) needed to be investigated. SAMBUCA requires the subsurface reflectance R(0-) as input. This is derived from the standard MERIS level 2 product for water leaving reflectance R(0+) M according to: R(0 ) 2 n R(0 ) T M Where n is the index of refraction of water, and T is the transmittance across the air-sea interface. The formulation for transmittance is taken from the SEADAS radiative transfer code (Feldman, 2005 ). Note that for the preliminary analysis of the MERIS data presented in the next section, the standard above surface R(0+) M product was used MERIS FR Keppel Isles data inventory Following reports of a potentially imminent bleaching event within the GBR for the Austral summer of 2006, full resolution data was ordered to cover the entire GBR region (Figure 11 for the period January through to April This represented over 350 MERIS scenes for a total of approximately 60 gigabytes (GB) of data (the largest single MERIS FR order received and accepted by the European Space Agency to date.) Figure 11. Northeast Australian coast. Area covered by January-April 2006 FR MERIS acquisition is shown in yellow. Location of Keppel Isles is denoted by a red circle. Keppel Isles using MERIS FR data Page 15

22 Diver surveys during the Southern GBR 2006 field campaign confirmed that extensive coral bleaching did indeed occur at the Keppel Isles, but little to no bleaching was found at the other study sites (Paul Marshall, pers. comm.). As stated previously, the evaluation of SAMBUCA-MERIS approach being reported here should be targeting relatively large in the order of 3x3 MERIS pixels (1 km 2 ) at a minimum - areas and ideally over several reefs in waters removed from direct coastal influence. These conditions do not apply to the Keppel Isles. However, as the bleaching in the Keppels area during this event was extensive, it was nonetheless decided to pursue the pilot study evaluation, using the reefs around Keppels as a study site. The MERIS satellite image data set was systematically assessed in order to compile all available image data of the Keppel Isles. In total, 24 dates of MERIS imagery containing the Keppel Isles were identified. (each date may represent one or two MERIS scenes, depending on the along track overlap of the scenes), with between 5 and 7 dates with Keppel Isles data for each calendar month (Table 2). Unfortunately, not all the imagery containing the Keppel Isles is useable: often because pixels may contain clouds obscuring targets of interest, but also because pixels may be masked out in the MERIS atmospheric correction process. The latter is a result of a quality check during the standardized atmospheric correction procedure, where pixels (presumed to contain a water-covered target) that are brighter than a set threshold are masked out. This bright pixel masking is in principle not applied to (bright) land targets, particularly if covered by vegetation. However, small islands such as Halfway and Middle Island, and other bright (and large enough) targets (e.g. Northern side of Great Keppel) may still be masked out (Figure 12). Anomalous water color conditions may also trigger this bright pixel masking. (An operational coral bleaching detection system would require a different level 1 to level 2 processing.) Middle Island Halfway Island Figure 12. Great Keppel Island and surrounding islands as seen from the a) MERIS and b) Quickbird sensor. Middle and Halfway Islands are labelled. Quickbird image courtesy of University of Queensland. Both images are cloud free, but note the pixels masked out in the MERIS scene (e.g. the smaller islands). Each MERIS FR Keppels scene was therefore inspected and evaluated based on an estimate of the number of potentially useable pixels within the scene. The pixels considered were within approximately a 15 x 15 km area, which approximately envelopes the islands in Figure 12. The scenes were grouped into one of thee categories: Keppel Isles using MERIS FR data Page 16

23 Category 1: Almost all relevant pixels useable. See Figures Category 2: At least 50% of relevant pixels useable (excluding Category 1). See Figure 15. Category 3: Few relevant pixels useable. See Figure 16. An additional constraint on the useability of each scene is the optical quality of the Keppel waters at the time of acquisition. For example, turbid waters from the Fitzroy river may reach as far as the Keppel Islands and may limit the ability to use optical remote sensing for substrate mapping. This quality assessment is not taken into account for the three categories of MERIS images inventoried here, and is discussed further in a following section. Table 2. Summary MERIS FR data containing Keppel Isles for January April 2006.Please see text for an explanation of the categories. Month Total number of Category 1 Category 2 Category 3 image acquisition dates* January February March April * one date may be represented by up to two separate MERIS scenes due to along-track overlap. At least one Category 1 Keppels group scene was identified for each of the four months, with the January scene being relatively more cloud-covered. Inspection of the imagery suggests that the Keppels often feature a degree of cloud cover. By comparison, the Capricorn Bunker group (and the outer Swains reefs) consistently feature less cloud cover. Below is a list of the dates for which Category 1 scenes of the Keppels were identified. MERIS FR data archives at CSIRO Land and Water were further inspected and a set of pre Keppels scenes were identified and evaluated. The dates for these historical Category 1 and Category 2 scenes are also listed below: Category 1 dates for January-April / * 14/ * 24/ * 12/ * 25/ * 15/ / Keppel Isles using MERIS FR data Page 17

24 Historical MERIS FR Keppels data in CSIRO archive Category 1 Category 2 07/ * 07/ / * 31/ / * 03/ / * Scenes used in initial qualitative assessment Eight Category 1 scenes were chosen for an initial qualitative (spatial and spectral) evaluation of the MERIS Keppels data (marked with an asterisk, see above). Figure 13. Example of Category 1 MERIS FR image of the Keppels. Note the relatively turbid waters in comparison with Figure 14. Keppel Isles using MERIS FR data Page 18

25 Figure 14. Example of Category 1 MERIS FR image of Keppels. Note the relatively clear waters in comparison with Figure 13. Figure 15. Example of Category 2 MERIS FR image of Keppels. Portions of Keppel Island are visible (circled in green). Keppel Isles using MERIS FR data Page 19

26 Figure 16. Example of Category 3 MERIS FR image of Keppels. The two main causes for unuseable pixels are clouds and masked-out pixels (no data). Both are present in this image. A portion of Keppel Island is visible (circled in green). 4. Qualitative assessment of spatial and spectral variation in subset of MERIS time series 4.1. Overview of the eight Category 1 MERIS FR scenes This section is intended to give an overview of the MERIS FR data, with a focus on the region surrounding the Keppel Isles. The eight scenes identified as the most suitable for analyzing the Keppel Isles are shown in the broader context of each entire MERIS FR scene (Figures 17-24). Each scene is displayed in true-color mode, where care was taken to try and achieve as similar stretches as possible. Figures display the variability in cloud cover and optical water quality that can be encountered in this system. For example, on 25/04/2006 (Figure 17) and 07/09/2003 (Figure 24), the Keppels are surrounded by relatively clear GBR lagoon open water, whereas more turbid waters (caused by tide and wind-driven resuspension)) are reaching as far out as the Keppels on 24/03/2006 (Figure 19) and 12/01/2005 (Figure 22). The variability in optical water quality is an important constraint on optical remote sensing; there appears to be no bottom substrate visibility (optically deep) in the 12/01/2005 scene (Figure 22), whereas bottom substrate is visible (optically shallow) around Great Keppel island, notably along the Northwest edge (appearing as light blue patches in the dark blue water), in the 07/09/2003 scene (Figure 24). Note that following further analysis using SAMBUCA, not all of the eight Category 1 scenes identified here will be relevant for bleaching detection (section 7). Keppel Isles using MERIS FR data Page 20

27 Figure 17. The 25/04/2006 MERIS FR scene. Great Keppel island is seen in the crosshair of the zoom window. Land run-off and or resuspension is seen to be contributing relatively turbid waters to the otherwise dark blue open ocean waters (e.g. green circle). Heron Island reef is circled in blue. Figure 18. The 04/12/2006 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Keppel Isles using MERIS FR data Page 21

28 Figure 19. The24/03/2006 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Figure 20. The 14/02/2006 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Keppel Isles using MERIS FR data Page 22

29 Figure 21. The 26/01/2006 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Figure 22. The 12/01/2005 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Keppel Isles using MERIS FR data Page 23

30 Figure 23. The 09/01/2005 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Figure 24. The 07/09/2003 MERIS FR scene. Great Keppel Island is seen in the crosshair of the zoom window. Keppel Isles using MERIS FR data Page 24

31 4.2. MERIS FR absolute geolocation accuracy The eight Category 1 scenes were geo-corrected and calibrated to above surface R(0+) M using the map projection tool in the (purpose built) BEAM/VISAT software (Carsten Brockman). This was done to Universal Transverse Mercator (UTM) geographic coordinates. Using this approach, the MERIS FR geo-location accuracy is reported to be within one MERIS FR pixel (300 m) (Goryl and Saunier, 2006). The eight scenes were imported into ENVI/IDL, linked, and a vector based outline of the Queensland coast was overlayed on the eight scenes. Figures display the result of this operation using the Keppel Isles as an example. Overall, the geo-location is judged to be sufficiently accurate for a sensor of this spatial/temporal coverage. Confirming the conclusion drawn by Goryl and Saunier (2006), the discrepancies appear to be in the order of no more than one pixel. a b Figure 25. The vector-based outline of the Queensland coastline mapped on the a) 07/09/2003 and b) 12/01/05 scenes. a b c d Figure 26. The vector-based outline of the Queensland coastline mapped on the the a) 07/09/2003 b) 09/01/2005 c) 14/02/2006 and d) 24/03/2006 scenes. Note that the variation in distance from the centre of the crosshairs to the Western side of Halfway Island is in the order of one pixel. Keppel Isles using MERIS FR data Page 25

32 Although this is considered to be good absolute geo-location accuracy for a sensor with the high repeat frequency and global coverage of MERIS, it nonetheless poses problems for bleaching detection. Where a target covers and area at sea surface level equivalent to less than say 3 x 3 pixels (approximately 1km x 1 km), a time series of pixels cannot be assumed to be directly comparable: although they share the same geo-location through time, the target they represent may be slightly to considerably different. For coral reef applications, this problem is compounded by the presence of coral targets next to emergent features such as land (particularly bright beaches) where slight variations in pixel location will mean variations in the amount of (bright) land surface within the pixel. For the initial Heron Island study, exposed land features were not a significant problem, and indeed it was concluded from this work that this approach is intended principally for openocean, submerged reef environments. The problem of subtle miss-registration between pixels in a time series was circumvented by not comparing pixels directly. SAMBUCA was used to estimate changes in surface area (square kilometres) of bright and dark substrates for pixels known to contain live coral. As SAMBUCA can account for varying proportions of substrates within a pixel, this was in effect a reef-wide comparison as opposed to a pixel-bypixel comparison. However, for bleaching events spanning large enough geographic areas (and sufficient magnitude) it may be possible to use more traditional pixel-to-pixel comparison (change detection) techniques, and to this end the absolute geo-location of MERIS pixels is investigated here Spectral variability within the time series The variability of the reflectance spectra through time was investigated by choosing two reference targets (darkest ocean and vegetated area on Keppel Island) that may conceivably be changing less than a Keppel Island shallow water target. This was done for three types of targets: the darkest open waters identifiable in each scene, the vegetated area in the center of Great Keppel Island, and a set of arbitrarily chosen pixels in the waters surrounding the Keppel Isles. For the dark water spectral sampling, homogeneous areas of dark blue, GBR lagoon, open water were subjectively selected (i.e. not the same set of pixels for each scene) and a spectral average of 21 neighboring pixels was determined. The resulting spectra from the eight scenes are charted in Figure 27. Keppel Isles using MERIS FR data Page 26

33 Above surface reflectance R Above surface reflectance for 8 MERIS scenes average of 21 dark open water pixels wavelength (nm) 7/09/2003 9/01/ /01/ /01/ /02/ /03/ /04/ /04/2006 Figure 27. Above surface reflectance spectral averages of dark water for the eight MERIS scenes. Despite variations in shape and magnitude, the results in Figure 27 are considered encouraging. The spectra represent waters imaged over a three year period and computed using evolving processing algorithms, and for wavelengths higher than 480 nm they differ by less than 1% in R(0)+ terms. Allowing for variations in optical water quality, this is considered an acceptable variation, suggesting the MERIS FR above surface reflectance product is stable over time. (The larger variation near the 412 nm band wavelengths is noted.) For the spectral sampling of the vegetated area on Great Keppel, a polygon was arbitrarily defined that contained as many dark pixels in the center of island as possible, while avoiding clouds and beaches (Figure 28). The polygon was not identical for the eight scenes. Typically 15 (+/-3) pixels were within each polygon, and the spectral average of these was computed (Figure 29). Figure 28. Example of the polygon used to define pixels used for computing spectral averages of the vegetated areas towards the center of Great Keppel Island. Keppel Isles using MERIS FR data Page 27

34 Above surface reflectance R Above surface reflectance for 8 MERIS scenes average of vegetated Great Keppel Island area wavelength (nm) 7/09/2003 9/01/ /01/ /01/ /02/ /03/ /04/ /04/2006 Figure 29. Average reflectance spectra for the vegetated area towards the centre of Great Keppel Island (Figure 28). Here the variation in magnitude in visible part of the spectrum is up to 5% for the eight scenes. No trend with time is discernable, and the shape of the eight spectra is very similar. Illumination conditions and discrepancies in the selection of suitable pixels may account for the variation magnitude (e.g. brighter, spectrally flat pixels from the edges of the island may be contributing in varying amounts). Given the possible variations due to the subjectivity of the sampling, it is encouraging that four of the eight spectra are very similar in shape and magnitude, differing by no more than approximately 1% in the visible part of spectrum. Furthermore, the two spectra with significantly higher magnitude than the cluster of four are the spectra sampled from two scenes (26/01/2006 and 25/04/2006) featuring clouds over the island (Figures 21 and 17). Although care was taken to avoid directly sampling clouds, there may still be contamination in the form of spectrally flat brightening of neighboring pixels. Figure 30 displays the locations of the individual pixels sampled in each of the eight scenes, and Figures display the corresponding spectra for each location Figure 30. Locations for sampling spectra in eight MERIS scenes. Keppel Isles using MERIS FR data Page 28