). 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.

The susceptibility of reef-building corals to climatic anomalies is well documented and a cause of great concern for the future of coral reefs. Reef corals are normally considered to tolerate only a narrow range of climatic conditions with only a small number of species considered heat-tolerant. Occasionally however, corals can be seen thriving in unusually harsh reef settings and these are cause for some optimism about the future of coral reefs. Here we document for the first time a diverse assemblage of 225 species of hard corals occurring in the intertidal zone of the Bonaparte Archipelago, north western Australia. We compare the environmental conditions at our study site (tidal regime, SST and level of turbidity) with those experienced at four other more typical tropical reef locations with similar levels of diversity. Physical extremes in the Bonaparte Archipelago include tidal oscillations of up to 8 m, long subaerial exposure times (>3.5 hrs), prolonged exposure to high SST and fluctuating turbidity levels. We conclude the timing of low tide in the coolest parts of the day ameliorates the severity of subaerial exposure, and the combination of strong currents and a naturally high sediment regime helps to offset light and heat stress. The low level of anthropogenic impact and proximity to the Indo-west Pacific centre of diversity are likely to further promote resistance and resilience in this community. This assemblage provides an indication of what corals may have existed in other nearshore locations in the past prior to widespread coastal development, eutrophication, coral predator and disease outbreaks and coral bleaching events. Our results call for a re-evaluation of what conditions are optimal for coral survival, and the Bonaparte intertidal community presents an ideal model system for exploring how species resilience is conferred in the absence of confounding factors such as pollution.

Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember-a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

[1] 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.

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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

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. OPEN ACCESSRemote Sens. 2015, 7 13158