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Globally, coral reefs are under threat, with many exhibiting degradation or a shift towards algal-dominated regimes following marine heat waves, and other disturbance events. Marginal coral reefs existing under naturally extreme conditions, such as turbid water reefs, may be more resilient than their clear water counterparts as well as offer some insight into how reefs could look in the future under climate change. Here, we surveyed 27 benthic habitats across an environmental stress gradient in the Exmouth Gulf region of north Western Australia immediately following a marine heatwave event. We used multidecadal remotely sensed turbidity (from an in-situ validated dataset) and temperature, to assess how these environmental drivers influence variability in benthic communities and coral morphology. Long-term turbidity and temperature variability were associated with macroalgal colonisation when exceeding a combined threshold. Coral cover was strongly negatively associated with temperature variability, and positively associated with depth, and wave power, while coral morphology diversity was positively associated with turbidity. While moderate turbidity (long-term average ~ 2 mg/L suspended matter) appeared to raise the threshold for coral bleaching and macroalgal dominance, regions with higher temperature variability (> 3.5 °C) appeared to have already reached this threshold. The region with the least turbidity and temperature variability had the highest amount of coral bleaching from a recent heatwave event and moderate levels of both these variables may confer resilience to coral reefs.

Seasonal variations in weather have significant impacts on crop yields. The accuracy of weather data is therefore an important consideration for crop yield models. This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data across the South West Agricultural Region of Australia from the in-situ weather station network operated by the Bureau of Meteorology (BOM), interpolated grids from this network and satellite weather data. This region covers five classes of the Koppen-Geiger climate classification system and is responsible for $10 billion AUD of agricultural produce annually. A strong bias was found for the maximum temperatures in the Copernicus Land Surface Temperature (LST) satellite product. This bias was linearly correlated to the in-situ temperature and exceeded 20C in warmer months. Due to the bias’s linear nature, a linear correction was able to reduce the RMSE of the Copernicus LST product by 82%. This process was tested for other regions of Australia. The initial bias correction reduced RMSE by 80% and region-specific bias correction reduced RMSE by 84%. The validation process demonstrated that BOM’s interpolated grid dataset reliably has the lowest RMSE. Nearest neighbour in-situ weather stations generally had the next lowest RMSE, followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products. While the gridded product generally had the lowest RMSE, there were spatial and seasonal variations. Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents (where there is a lower density of in-situ stations). Copernicus LST monthly minimum temperatures had similar to slightly better RMSE than the gridded product for the southern half of the study area and the rain-gauge corrected Global Satellite Mapping of Precipitation product performed similarly to the gridded product in drier months.

In the last year, three imaging spectroscopy spaceborne sensors were launched and, a number of others are expected to follow suit in the next few years. Australia has developed extensive infrastructure and associated expertise in calibration and validation, which supports scientific, operational and commercial EO missions. Much of the infrastructure are already relevant for imaging spectroscopy and can be extended for the specificity required for the technology. This paper describes the various calibration and validation infrastructure and how they are relevant for the recently launched and upcoming spaceborne imaging spectroscopy sensors.

Background: When treating superficial tumors with external beam radiation therapy, bolus is often used. Bolus increases surface dose, reduces dose to underlying tissue, and improves dose homogeneity. Introduction: The conventional bolus types used clinically in practice have some disadvantages. The use of Three-Dimensional (3D) printing has the potential to create more effective boluses. CT data is used for dosimetric calculations for these treatments and often to manufacture the customized 3D-printed bolus. Purpose: The aim of this review is to evaluate the published studies that have compared 3D-printed bolus against conventional bolus types. Methods and Results: A systematic search of several databases and a further appraisal for relevance and eligibility resulted in the 14 articles used in this review. The 14 articles were analyzed based on their comparison of 3D-printed bolus and at least one conventional bolus type. Conclusion: The findings of this review indicated that 3D-printed bolus has a number of advantages. Compared to conventional bolus types, 3D-printed bolus was found to have equivalent or improved dosimetric measures, positional accuracy, fit, and uniformity. 3D-printed bolus was also found to benefit workflow efficiency through both time and cost effectiveness. However, factors such as patient comfort and staff perspectives need to be further explored to support the use of 3Dprinted bolus in routine practice.

Recent studies in the application of geostationary satellite sensors, such as the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European Union’s meteorological satellite, and the Geostationary Ocean Color Imager (GOCI) from South Korea, to monitor the Total Suspended Sediment (TSS) in coastal waters have shown that the higher temporal dynamics in the coastal processes can be better achieved with high temporal resolution geostationary satellite sensors. The availability of the geostationary satellite, Himawari-8 carrying the Advanced Himawari Imager (AHI) sensor with visible, Near InfraRed (NIR) and Shortwave InfraRed (SWIR) bands over the coastal waters of Australia has prompted this study to test the feasibility of AHI in mapping TSS concentration. In this study, we present an atmospheric correction method for AHI data using two different aerosol correction methods, a combination of two SWIR bands, and a combination of one NIR and one SWIR band. The outcome from this study indicated that the atmospherically corrected AHI data at 10 min temporal resolution can be used to discern TSS concentrations greater than ∼0.15 mg/L when solar zenith angles (θ0) are less than 60°. The result of the cross-validation of AHI derived TSS concentration, with MODIS-Aqua and Lansat-8 Operational Land Imager (OLI) derived TSS concentration showed good correlation, with correlation coefficients r = 0.71 and r = 0.91, respectively.

Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (Corymbia calophylla) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92 % of the wavelengths between 350 nm and 1800 nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10 % of the wavelengths, with peak separation between 518 nm and 557 nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520 nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80 % accuracy.

With Apis mellifera (the European Honey Bee) having an average forage radius of less than one kilometre from their hive, selecting the best location for beehives is critical for commercial beekeepers to optimise their honey production. In this study, we have used standard three-band digital cameras to develop and assess a simple parallelpiped algorithm to detect the flowers of the Corymbia calophylla tree in Western Australia, the largest source of honey in the state. The algorithm has been tested in a number of situations and, within the bounds of the study, works to a better than 90% classification accuracy for Digital Single Lens Reflex camera images of trees within 15 m distance, and often better than 95%. To determine how this approach could be used by Unmanned Aerial Vehicle platforms to detect flowering Corymbia calophylla flowering, image resolution was progressively degraded until flowers could not be detected. It was found that the cluster size of flowers (i.e. whether flowers occur individually or in groups) played an important role in determining the accuracy of flower detection, but overall a minimum resolution of 10 pixels per flower is required for reliable detection of flower pixels. While there is some improvement above this resolution, the effect is minimal. The key to accurate measurement of percentage flower cover is the accuracy of the classification of the background. If the background classification error is known for an image or scene, the percentage flower cover can be calculated with as little as 2% flower cover. Based on this study, UAV platforms with standard RGB cameras appear to be suitable for detection of Corymbia calophylla flowers if surveys are designed within the bounds described. Thus, UAV surveys may prove to be useful for beekeepers to optimise the location of their beehives amongst a choice of different locations.

This study was funded by the Chevron-operated Wheatstone Project's State Environmental Offsets Program and administered by the Department of Biodiversity, Conservation and Attractions. The Wheatstone Project is a joint venture between Australian subsidiaries of Chevron, Kuwait Foreign Petroleum Exploration Company (KUFPEC), Woodside Petroleum Limited and Kyushu Electric Power Company, together with PE Wheatstone Pty Ltd (part owned by TEPCO). Funding support was also received from the Gorgon Barrow Island Net Conservation Benefits Fund, which is administered by the Department of Biodiversity, Conservation and Attractions. Thank you to Helen Chedzey and Mark Broomhall from the Remote Sensing and Satellite Research Group at Curtin University for generating and providing the remotely sensed total suspended solids data for this study.

The worlds' coral reefs are declining due to the combined effects of natural disturbances and anthropogenic pressures including thermal coral bleaching associated with global climate change. Nearshore corals are receiving increased anthropogenic stress from coastal development and nutrient run-off. Considering forecast increases in global temperatures, greater understanding of drivers of recovery on nearshore coral reefs following widespread bleaching events is required to inform management of local stressors. The west Pilbara coral reefs, with cross-shelf turbidity gradients coupled with a large nearby dredging program and recent history of repeated coral bleaching due to heat stress, represent an opportune location to study recovery from multiple disturbances. Mean coral cover at west Pilbara reefs was monitored from 2009 to 2018 and declined from 45% in 2009 to 5% in 2014 following three heat waves. Recruitment and juvenile abundance of corals were monitored from 2014 to 2018 and were combined with biological and physical data to identify which variables enhanced or hindered early-stage coral recovery of all hard corals and separately for the acroporids, the genera principally responsible for recovery in the short-term (

Three-decades of observations of tropical fish recruitment on the south coast of Rottnest Island, Western Australia (WA), have indicated that settlement of two tropical damselfish (Abudefduf sexfasciatus and A. vaigiensis) peaks each autumn with the seasonal strengthening of the Leeuwin Current (LC). Historically these fish have not bred at Rottnest Island or at other adjacent coastal locations although an active breeding population exists at the Houtman Abrolhos Islands some 330 km to the north. Record levels of recruitment in early 2011 followed extremely strong southward advection in the LC accompanied by unprecedented high water temperatures associated with a marine heat wave. Settlement numbers the following year were almost as high, again associated with a very strong LC and high water temperatures, while 2013 saw lower but still above average recruitment. Against a background of gradual ocean warming along the WA coast, one of the world’s 30 hotspots for increasing ocean temperature, the potential for these species to establish a breeding population at Rottnest Island is explored by comparing the water temperatures during the presumed spawning period at the Abrolhos Islands with those at Rottnest Island together with winter temperatures and the abundance of what are believed to be mature Abudefduf successfully over-wintering at Rottnest Island. “The results indicate that establishment of a breeding population at Rottnest Island does not appear to be limited by water temperatures, and raises the question as to why a breeding population does not already exist as the settlement habitat appears very similar to that at the Abrolhos Islands.

The World Heritage microbialites, coquinas, and microbialitic-sediments of Hamelin Pool, Shark Bay, Western Australia, form through complex community and environmental interactions resulting in surficial CO2 sequestration. Predicted climate-change impacts threaten the stability of this setting and the balance of biogeomorphological processes that generate them. In this setting, long-term surficial CO2 sequestration occurs through the combination of biomineralisation, organomineralisation and lithification, which locks atmospheric CO2 into CaCO3 rocks and sediments. Biogeomorphological processes that control the distribution and effectiveness of these mechanisms including biostabilisation, bioconstruction, bioerosion and bioturbation have not been addressed in this setting to date. A three-dimensional biogemorphological mapping unit framework is defined at three locations in Hamelin Pool within the shallow-subtidal to supratidal zones based on historical and recent multiscale datasets. This framework is used to review the complex distribution of geomorphological and biogeomorphological processes. The distribution of lithification within a biogeomorphological process context is examined, and current geological analogue models of microbialite and coquina development are expanded to include biogeomorphological processes. The framework is used to understand the impacts of increased water elevation in response to intensification of the Leeuwin Current during the 2010–2012 Ningaloo Nino event. Potential climate change impacts are identified including reduced intertidal bioconstruction, and a shift from microbialitic structure generation to increased grain generation reducing stromatolite development. Review of the uncertainty of impacts of ocean acidification and temperature shifts on biomineralisation, organomineralisation and lithification processes, crucial to the maintenance of the outstanding universal values in this World Heritage location, suggests urgent research is required.

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae.

A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002–2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, at(443), the particulate backscattering coefficient at 443 nm, bbp(443), and the diffuse attenuation coefficient at 488 nm, Kd(488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of at(443), bbp(443), and Kd(443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

In coastal regions, shallow water semi-analytical inversion algorithms may be used to derive geophysical parameters such as inherent optical properties (IOPs), water column depth, and bottom albedo coefficients by inverting sensor-derived sub-surface remote sensing reflectance, rrs. The uncertainties of these derived geophysical parameters due to instrumental and environmental noise can be estimated numerically via the addition of spectral noise to the sensor-derived rrs before inversion. Repeating this process multiple times allows the calculation of the standard error and average for each derived parameter. Apart from spectral non-uniqueness, the optimization algorithm employed in the inversion must converge onto a single minimum to obtain a true representation of the uncertainty for a given set of noise-perturbed rrs. Failure to do so inflates the uncertainty and affects the average retrieved value (accuracy). We show that the standard approach of seeding the optimization with an arbitrary, fixed initial guess, can lead to the convergence to multiple minima, each having substantially different centroids in multi-parameter solution space. We present the Update-Repeat Levenberg-Marquardt (UR-LM) and Latin Hypercube Sampling (LHS) routines that dynamically search the solution space for an optimal initial guess, that when applied to the optimization allows convergence to the best local minimum. We apply the UR-LM and LHS methods on HICO-derived and simulated rrs and demonstrate the improved computational efficiency, precision, and accuracy afforded from these methods compared with the standard approach. Conceptually, these methods are applicable to remote sensing based, shallow water or oceanic semi-analytical inversion algorithms requiring nonlinear least squares optimization.

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallow water inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used. Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than ± 20%. A per-pixel t -test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as low as 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed.

Quantifying the spatial coverage of floating macroalgae from satellite imagery, using methods such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI), requires the use of a scene-wide threshold to isolate and then compute the number of floating macroalgae pixels. The problem faced is the sensitivity of the NDVI and, to a lesser extent, the FAI to radiance contributions from atmospheric aerosols and turbid water. Both these factors can vary significantly across a satellites' field-of-view generating irregular apparent reflectance of ocean and floating macroalgae pixels across an NDVI/FAI scene, leading to inaccuracies in spatial coverage estimates. We present a simple image processing algorithm, termed the scaled algae index (SAI), that removes any variability present in ocean and floating macroalgae pixels in NDVI or FAI imagery. The SAI does this by subtracting a given pixel's index by that of a local ocean pixel, effectively scaling ocean pixels to values near zero, and macroalgae pixels to positive values. The SAI algorithm has been tested on NDVI and FAI scenes of the 2008/2009 floating macroalgae blooms that occurred in the Yellow Sea, China. These SAI images show a major reduction in variability with scene-wide histograms being unimodal. Histogram analysis also indicates that sufficient contrast exists between ocean and floating macroalgae pixels to enable segmentation by a scene-wide threshold. A semiautomated threshold determination procedure is also presented, which together with the SAI algorithm can be used to compute accurate estimates of the spatial coverage of floating macroalgae.

Application of an individual-based particle tracking model to the migration of tropical fish larvae along the continental shelf between the Houtman Abrolhos Islands and Rottnest Island (Western Australia) has shown that there is potential for the southwards advection of passive particles/larvae in the Leeuwin Current system throughout the year. However, seasonal variations in the prevailing wind field result in corresponding seasonal changes in the surface current flow (both alongshore and cross-shelf) on the continental shelf, leading to a pulse of modelled particles arriving at Rottnest Island during the autumn months. This matches, within a month, the observed April/May peak in annual recruitment of 2 species of damselfish ( Abudefduf sexfasciatus and A. vaigiensis ), at the time when the Leeuwin Current is strengthening. It is assumed that the larvae are in the uppermost 20 m of the water column and that there are no vertical diurnal movements. The model has a 10 km grid spacing, and so can resolve some of the current gradients across the continental shelf. Comparison of the modelled near-surface currents with ADCP measurements at 2 current mooring sites as well as with a broader range of historical current measurements off south-western Australia indicates that the alongshore net transport is reasonably well reproduced by the model, whereas agreement with the cross-shelf flow is not as good (this may be partly attributed to the paucity of high-quality near-surface current measurements in the area of study). Because of limitations in our knowledge of the swimming ability, choice of swimming direction and habitat selection of larval fish, as well as the inability of the model to reproduce the small-scale circulation around Rottnest Island, the swimming capacity of the late-stage larval fish is not specifically included; they are considered as potential settlers once they have reached within 20 km of the Island. The observed inter-annual variability in recruitment is not as well matched by the model as is the seasonal pattern, but this is almost certainly due to uncertainties in biological factors such as spawning strength, food and predation en route , which are not known. The modelled results are also applied to a more general discussion of the transport of eggs and larvae of commercial fish and invertebrate species on the Western Australian continental shelf, and it is shown that the seasonality and position on the shelf of the spawning may play a large role in the movement (and hence survival and ultimate recruitment) of different species.

During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastal region of Western Australia. These data were processed by inversion of a semi-analytical shallow water optical model to classify the substrate. Inputs to the optical model include water column constituent specific inherent optical properties (SIOPs), view and illumination geometry, surface condition (based on wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap data covering approximately 4 km 2 was processed such that each 3×3 m 2 pixel was classed as sand, seagrass, brown algae or various mixtures of these three components. Coincident video data were collected and used to estimate substrate types. We present comparisons of the habitat classifications determined by these two methods and show that the percentage validation of the remotely sensed habitat map may be optimised by selection of appropriate optical model parameters. The optical model was able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentages of approximately 50% for sand and seagrass classification, and 90% for brown algae classification. The semi-analytical model inversion approach to classification can be expected to be applied to any shallow water region where substrate reflectance spectra and SIOPs are known or can be inferred.

The large green-tide events that occurred in the Yellow Sea in 2008 (3489 km(2)) and 2009 (4994 km(2)) are shown to be novel events preceded only once by a much smaller event in 2007 (82 km(2)). The blooms originated in the coastal area of Jiangsu province and spread north-east towards the Shandong Peninsula. The blooms grew at different rates and mesoscale variability in surface winds explained the differences in the spatial and temporal patterns of blooms in 2008 and 2009. The 2009 bloom was tracked to its origin immediately offshore of extensive intertidal flats between Yancheng and Nantong where recent rapid expansion of Porphyra aquaculture has occurred. We review published hypotheses which have been advanced to explain the occurrence of blooms and in light of our findings, we conclude that the accumulation and disposal of waste Ulva prolifera from Porphyra aquaculture rafts is the most likely cause of the blooms. (C) 2011 Elsevier Ltd. All rights reserved.

In the summer of 2008, the world’s largest green-tide occurred in the Yellow Sea, China. The hypothesized cause was the expansion of Porphyra yezoensis aquaculture along the Jiangsu coastline and the re-occurrence of a green-tide in 2009 was predicted. In this study, satellite and field images showed the formation of the June 2009 green-tide which again originated from the Jiangsu coast. The responsible species, its source and biomass accumulation were studied to support the previous hypothesis. Morphological and phylogenetic analysis demonstrated the homology of Ulva prolifera in the 2008 green-tide with the U. prolifera from P. yezoensis aquaculture rafts. About 91–505 kg/ha of U. prolifera was attached to the P. yezoensis aquaculture rafts and a total biomass of 4956 tonnes was estimated during the harvesting of P. yezoensis. This is sufficient to seed a bloom when they are dislodged from the rafts as a result of harvesting practices.

An interdisciplinary study of the waters across the continental shelf off Perth, Western Australia, has provided the first detailed climatology of the physical, chemical, optical, and biological processes across the shelf. In support of this work, remote-sensing data were utilised to provide a broad view of the spatial and temporal chlorophyll concentration dynamics, to support in situ observations, and to help "fill the gaps" inherent in in situ point sampling. In situ validation of remote-sensing products was carried out monthly off Perth over a 27-month period. Biological and physical measurements were made along a 40 km east-west transect, 20 km north of Perth. Results of the study have shown that in water deeper than 30-35 m, and where viewing conditions were suitable, the in situ measurements of chlorophyll concentration were within the 35% uncertainty of the SeaWiFS product. An increase in the in situ measurements of near-coastal chlorophyll concentration was evident during the 1998 winter period (May-July), but the apparent seasonal increase was not evident in the in situ data for 1997. The SeaWiFS chlorophyll concentration estimates have been used to show the seasonal fluctuation of chlorophyll concentration in Perth coastal waters from November 1997 to the end of 2004. The remotely sensed data show a clear seasonal cycle, with maximum chlorophyll concentration occurring during May, June and July for waters off Perth. Also evident from the remotely sensed data was an increase in water column light attenuation during winter, coincident with the increase in chlorophyll concentration. The potential now exists to further develop remote-sensing techniques and to integrate remotely sensed products into routine water-quality monitoring programmes for WA waters.

Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr &lt, 0.0005) of bottom reflectance on the spectrally-averaged remote sensing reflectance signal at depths &gt, 17 m for MODIS and &gt, 19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters, and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments.

This chapter presents aspects of underwater hyperspectral imaging (UHI) techniques aimed at mapping biogeochemical objects of interest (OOI) on the seafloor. Case examples of instrument-carrying platforms and biogeochemical applications are given. We discuss how to create high resolution, georeferenced, optically corrected digital underwater maps of different habitats, minerals, substrates, and organisms. Corrections for platform speed and direction, inherent optical properties (IOP), optical path length, dynamic positioning, and pitch/roll/yaw are discussed in the context of using UHI-based optical fingerprints (i.e. spectral reflectance in visible wavelengths) of different targets to create maps that can help discriminate, identify, and quantify OOI, and provide statistical information on relevant seafloor features.

Technological advancements in remote sensing and GIS have improved natural resource managers' abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and associated algorithms to predict the total suspended solids (TSS) concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L(-1). However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L(-1), and began providing false-negatives (excluding actual plume) at a threshold as low as 4 mg L(-1). Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.

[1] Unlike most eastern boundary currents, the Leeuwin Current off the west coast of Australia flows poleward and is therefore warm, nutrient-poor and suppresses upwelling. As a result, the waters off Western Australia are relatively oligotrophic. Primary productivity and concentrations of chlorophyll are particularly low in summer, when the water column is stratified and most chlorophyll is concentrated in a layer above the nutricline at approximately 100 m depth. The phytoplankton blooms in late autumn and winter, coinciding with the period of strongest Leeuwin flow. The bloom in winter is maintained by cooling and storms, which promote convective mixing of the upper water column and a shoaling of the nutricline and chlorophyll maximum layer. However, the late autumn bloom coincides with the initial intensification of the Leeuwin Current. Eddies and meanders spin up just beyond the shelf break, and Leeuwin Current water was observed at this time to destratify the water column and flood the shelf with relatively nutrient-rich water. Several potential sources for the nutrients are proposed: upwelling by the eddies and meanders as they induce cross-shelf motions; advection from the north, where the nutricline is shallower; and seasonal cooling.

Remote sensing algorithms for retrieving estimates of oceanic constituent concentrations, such as chlorophyll concentration, require as input measurements of spectral water-leaving radiance. This measurement is typically obtained from space-based sensors such as SeaWiFS or MODIS. Some polar orbiting sensors have tilt and scan capabilities such that sensor view angles can at times exceed 70 degrees from the zenith. Inherent in algorithms applied to such remotely sensed measurements are assumptions on how the off-nadir radiance varies in intensity compared to the total upwelling irradiance. Typical ocean reflectance equations incorporate two factors, f (in R = f bb/a) and Q (where Q = Eu/Lu), which relate the water-leaving radiance in 3 dimensions to the incident (sky and sun) irradiance. A Monte Carlo ocean optical model was developed and used to investigate the three dimensional nature of water leaving radiance for typical open ocean optical conditions. Equations for predicting f were developed as well as a data base of Q factors for different solar and viewing geometries as functions of b, bb and a. Results derived from the Monte Carlo model were then used here to develop a more in-depth study of f based on HYDROLIGHT. Improved equations for predicting f have been developed and are compared to predictions of f from HYDROLIGHT. Examples of how these improved estimates of f and Q may be applied to a chlorophyll concentration algorithm for open ocean waters will be presented.

Hyperspectral remote sensing provides a particularly useful means of determining inherent optical properties of coastal waters where constituents other than phytoplankton add to the optical complexity of the water column. The substantial number of channels, about 200 for most hyperspectral sensors, enables many of the constituents within the water column to be identified spectrally. Additionally, in shallow water the water leaving radiance may include a signal reflected from the sea bottom. A hyperspectral radiometer was deployed on monthly oceanographic cruises off the coast near Perth, Western Australia, to make observations. Field measured reflectance spectra were used as input into a slightly modified version of a reflectance model developed by Lee et al , 1999. Products, including the concentrations of chlorophyll-a (Chl-a ), coloured dissolved organic matter (CDOM), and suspended sediments (SS), as well as the water column depth (H), were extracted from the reflectance model by incorporating an optimisation technique. A Levenberg-Marquardt retrieval scheme was utilised in the optimisation. This scheme involved minimizing the difference between the modelled and measured spectral reflectance curves. Water samples were also collected on the monthly oceanographic cruises and used to determine the concentrations of chlorophyll-a , CDOM and SS. Water depth was measured using the boat's echo sounder. The model-derived products were compared to in situ measurements. The mean difference between model retrieved depth and in situ depth was 12.5 % or 1.4 m (R 2 = 0.98, N =11). Excluding two field measurements taken in the Marmion Marine Park, the mean RMS difference in depth was 7.6 % or 0.9 m (R 2 = 0.99, N =9). The mean RMS difference between retrieved Chl-a concentration and in situ measured Chl-a was 11.1 % or 0.044 mgm-3 (R 2 = 0.91, N =8). These preliminary results suggest that the reflectance model works well for depth and Chl-a retrieval for Western Australian coastal waters and their sandy substrate.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The relationships between inherent optical properties of ocean waters and the subsurface diffuse reflectance (R/sup -/) have been explored by a number of workers over the years. It is well accepted that R/sup -/ is related to the backscattering to absorption ratio b/sub b//a (or sometimes b/sub b//(a+b/sub b/)) through R=fb/sub b//a. The factor f has been described by a number of people, with various relationships being suggested for predicting its value. Until the development of "fast" oceanic radiative transfer models such as HYDROLIGHT, most workers have employed Monte Carlo techniques. This work presents results based on Monte Carlo investigations as well as comparisons with HYDROLIGHT results.

The aim of this work is to develop a satellite-based coastal water quality monitoring capacity to assist in management of key anthropogenic activities on the coastal zone (ie. nutrient-rich wastewater, industrial and urban discharges, river and estuarine outflows). This tool will be applicable for the monitoring of long term water quality trends over local to regional scales to provide data on natural variation and long term trends in anthropogenically induced impacts. The project aims to develop a fundamental management tool that will be geographically transferable around the State and Australian Coastal Zone and which will be able to provide information at local, regional and statewide spatial scales, as required for management.

1996 marks the revival of the production of visible channel imagery of the ocean with the planned launches of Japan's ADEOS Ocean Color Temperature Sensor (OCTS), SeaWiFS, and the EOS MODIS-N, with launch dates 1996, 1997 and 1998 respectively. At least three other missions are at various stages of planning. The missions will initiate a continuous global time series of ocean color data that should extend well into the next century. The classification of water types and application of pigment retrieval algorithms specific to those water types may increase the accuracy of retrieved pigment concentrations. The aim is to develop a pigment concentration retrieval algorithm tunes to local oceanic conditions. Investigation of the effects of the physical and optical properties of the ocean on the water- leaving radiance, and the development of an inversion scheme for measurement of those physical and optical properties may be achieved through modeling. This approach also aids the design of validation programs. A simple relationship has been fond for predicting the reflectance of the ocean for different optical properties. The applicability of this relationship to chlorophyll concentration retrieval is being investigated. A multiple channel reflectance inversions has been used to derive pigment concentrations from model results. Preliminary results show that the scheme is sensitive to uncertainty in optical properties of water constituents. The scheme does however provide some indication of the confidence limits applicable to the retrieved concentration.

Bathymetry and the spatial distribution of benthic cover in coastal waters are of key importance in managing and monitoring our coastal water environments. Currently very little of the Western Australian shallow coastal water habitats are mapped, and for those maps that do exist, the spatial resolution generally is poor and the information is dated. Aircraft and space-borne hyperspectral sensors have been shown to be useful in imaging substrate features in shallow coastal waters. This paper describes a method for quantitatively estimating both bathymetry and benthic cover in shallow waters from hyperspectral imagery. The method incorporates a shallow water reflectance model, which accounts for the water column absorption and backscattering, water depth and substrate reflectance. The model was tested against simulated reflectance data, demonstrating the models' ability to retrieve appropriate fractional coverage of sediment, sea grass and brown algae for depths ranging from 1 - 12 m. The model was applied to a HyMap image encompassing a portion of the Jurien Bay Marine Park off the coast of Western Australia. The retrieved benthic cover products were compared to underwater video observations sampled within the image. The comparison shows the method's great potential for characterizing key aspects of marine ecosystems from remotely sensed hyperspectral data.

Data from bands 1–7 are the most common bands of the MODIS instrument used for near-real time terrestrial earth observation operations in Australia. However, many of Australia's bioregions present unique scenarios which constitute a challenge for quantitative environmental remote sensing. We believe that data from MODIS bands 8–19 may provide significant benefit to Earth observation over particular bioregions of the Australian continent. Examples here include the use of band 8 in characterising aerosol optical depth over typically bright land surfaces and accounting for anomalous retrievals of atmospheric water vapour obtained using MOD05 based on the abundance of Australia's 'red dirt', which exhibits absorption features in the near infrared bands 17–19 of MODIS. Bioregion-focused applications such as those mentioned above have driven the development of automated processing, infrastructure for the atmospheric and BRDF correction of the first 19 bands of MODIS rather than only the first 7, which is more often the case. This work has been facilitated by the AusCover project which is the remote sensing component of the Terrestrial Ecosystem Research Network (TERN), itself a program designed to create a new generation of infrastructure for ecological study of the Australian landscape.

Traditionally measurements of carbon uptake at discrete locations have been used to obtain estimates of oceanic primary production. These methods by their very nature produce estimates of primary production only at specific points within a water body. Thus estimates of primary production at ocean basin scales requires extrapolation of in situ measurements via broad and very general assumptions regarding the spatial variation in water conditions. Research by Platt, Sathyendranath, Behrenfeld, Falkowski and others has shown that remote sensing techniques may be used to provide a means of obtaining ocean primary production estimates at basin scales. The techniques discussed by Behrenfeld and Falkowski have been applied to locally obtained SeaWiFS ocean colour data, AVHRR SST data, and estimated PAR data to carry out a study of the annual primary production cycle in the waters associated with Western Australia's Abrolhos Islands.