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  • 1
    Publication Date: 2023-02-24
    Keywords: calculated, Lagrangian method; DATE/TIME; DEPTH, water; LATITUDE; LONGITUDE; Oxygen; Oxygen, flux; Oxygen, net production; Pau_Pau_2016; Saipan Lagoon, Commonwealth of the Northern Mariana Islands; Speed; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 54 data points
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  • 2
    Publication Date: 2023-02-24
    Keywords: calculated, Lagrangian method; DATE/TIME; DEPTH, water; LATITUDE; LONGITUDE; Oxygen; Oxygen, flux; Oxygen, net production; Saipan Lagoon, Commonwealth of the Northern Mariana Islands; San_Roque_2016; Speed; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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  • 3
    Publication Date: 2023-02-24
    Keywords: calculated, Lagrangian method; DATE/TIME; DEPTH, water; Heron Island, southern Great Barrier Reef, Australia; LATITUDE; LONGITUDE; Oxygen; Oxygen, flux; Oxygen, net production; Shark_Bay_2016; Speed; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 108 data points
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  • 4
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    PANGAEA
    In:  Supplement to: Roelfsema, Christiaan M; Phinn, Stuart R; Jupiter, Stacy D; Comley, James; Albert, Simon (2013): Mapping coral reefs at reef to reef-system scales, 10s–1000s km**2, using object-based image analysis. International Journal of Remote Sensing, 34(18), 6367-6388, https://doi.org/10.1080/01431161.2013.800660
    Publication Date: 2023-05-12
    Description: Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel 〈10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10-100 km**2), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10-1000 km**2) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10-35 km**2) in Australia, Fiji, and Palau; and for three complex reef systems (300-600 km**2) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: 'reef', 'reef type', 'geomorphic zone', and 'benthic community'. The overall accuracy of the 'geomorphic zone' classification for each of the six study sites was 76-82% using 6-10 mapping categories. For 'benthic community' classification, the overall accuracy was 52-75% with individual reefs having 14-17 categories and reef systems 20-30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 5
    Publication Date: 2023-05-12
    Description: In 2014, UniDive (The University of Queensland Underwater Club) conducted an ecological assessment of the Point Lookout Dive sites for comparison with similar surveys conducted in 2001. Involvement in the project was voluntary. Members of UniDive who were marine experts conducted training for other club members who had no, or limited, experience in identifying marine organisms and mapping habitats. Since the 2001 detailed baseline study, no similar seasonal survey has been conducted. The 2014 data is particularly important given that numerous changes have taken place in relation to the management of, and potential impacts on, these reef sites. In 2009, Moreton Bay Marine Park was re-zoned, and Flat Rock was converted to a marine national park zone (Green zone) with no fishing or anchoring. In 2012, four permanent moorings were installed at Flat Rock. Additionally, the entire area was exposed to the potential effects of the 2011 and 2013 Queensland floods, including flood plumes which carried large quantities of sediment into Moreton Bay and surrounding waters. The population of South East Queensland has increased from 2.49 million in 2001 to 3.18 million in 2011 (BITRE, 2013). This rapidly expanding coastal population has increased the frequency and intensity of both commercial and recreational activities around Point Lookout dive sites (EPA 2008). Methodology used for the PLEA project was based on the 2001 survey protocols, Reef Check Australia protocols and Coral Watch methods. This hybrid methodology was used to monitor substrate and benthos, invertebrates, fish, and reef health impacts. Additional analyses were conducted with georeferenced photo transects. The PLEA marine surveys were conducted over six weekends in 2014 totaling 535 dives and 376 hours underwater. Two training weekends (February and March) were attended by 44 divers, whilst biological surveys were conducted on seasonal weekends (February, May, July and October). Three reefs were surveyed, with two semi-permanent transects at Flat Rock, two at Shag Rock, and one at Manta Ray Bommie. Each transect was sampled once every survey weekend, with the transect tapes deployed at a depth of 10 m below chart datum. Fish populations were assessed using a visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape), 5 m high and 20 m in length. Fish families and species were chosen that are commonly targeted by recreational or commercial fishers, or targeted by aquarium collectors, and that were easily identified by their body shape. Rare or otherwise unusual species were also recorded. Target invertebrate populations were assessed using visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The diver surveying invertebrates conducted a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Target impacts were assessed using a visual census along the 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The transect was surveyed via a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Substrate surveys were conducted using the point sampling method, enabling percentage cover of substrate types and benthic organisms to be calculated. The substrate or benthos under the transect line was identified at 0.5m intervals, with a 5m gap between each of the three 20m segments. Categories recorded included various growth forms of hard and soft coral, key species/growth forms of algae, other living organisms (i.e. sponges), recently killed coral, and, non-living substrate types (i.e. bare rock, sand, rubble, silt/clay).
    Type: Dataset
    Format: application/zip, 6 datasets
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  • 6
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    PANGAEA
    In:  Supplement to: Roelfsema, Christiaan M; Kovacs, Eva M; Phinn, Stuart R (2015): Field data sets for seagrass biophysical properties for the Eastern Banks, Moreton Bay, Australia, 2004–2014. Scientific Data, 2, 150040, https://doi.org/10.1038/sdata.2015.40
    Publication Date: 2023-05-12
    Description: This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.
    Type: Dataset
    Format: application/zip, 9 datasets
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  • 7
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    PANGAEA
    In:  Supplement to: Perez, Denise Ivette; Phinn, Stuart R; Roelfsema, Christiaan M; Shaw, Emily; Johnston, Lyza; Iguel, John (2018): Primary Production and Calcification Rates of Algae-Dominated Reef Flat and Seagrass Communities. Journal of Geophysical Research: Biogeosciences, 123(8), 2362-2375, https://doi.org/10.1029/2017JG004241
    Publication Date: 2023-01-13
    Description: Primary production and calcification are two processes that form the basis for coral reefs through energy production, and construction of complex habitats needed to support highly diverse systems. Monitoring of these processes is essential for understanding the carbon cycle for coral reef habitats, and how this will change over time. This project measured primary production and calcification for two different coral reef systems at Heron Island, southern Great Barrier Reef, Australia and Saipan Lagoon, Commonwealth of the Northern Mariana Islands. This included three sites: (1) coral reef flat at Shark Bay, Heron Island, (2) seagrass habitat at San Roque, Saipan Lagoon, and (3) coral reef flat at Pau Pau, Saipan Lagoon. A combination of water sampling and in situ dissolved oxygen sensors were used to determine productivity and calcification rates with the Lagrangian method. Flow respirometry drifts were conducted daily over a two week period in Jan. 2016 for Heron Island, and from May - June 2016 in Saipan Lagoon. Data presented here includes results from the flow respirometry drifts including GPS tracks, carbonate chemistry data from water samples, water quality data from in situ sensors, and current speed and direction from current meters placed on the coral reef flats.
    Type: Dataset
    Format: application/zip, 15 datasets
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  • 8
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    PANGAEA
    In:  Centre for Remote Sensing & Spatial Information Science, School of Geography, Planning & Environmental Management, University of Queensland, Brisbane, Australia
    Publication Date: 2023-01-13
    Description: Underwater spectral reflectance was measured for selected biotic and abiotic coral reef features of Heron Reef from June 25-30, 2006. Spectral reflectance's of 105 different benthic types were obtained in-situ. An Ocean Optics USB2000 spectrometer was deployed in an custom made underwater housing with a 0.5 m fiber-optic probe mounted next to an artificial light source. Spectral readings were collected with the probe(bear fibre) about 5 cm from the target to ensure that the target would fill the field of view of the fiber optic (FOV diameter ~4.4 cm), as well as to reduce the attenuating effect of the intermediate water (Roelfsema et al., 2006). Spectral readings included for one target included: 1 reading of the covered spectral fibre to correct for instrument noise, 1 reading of spectralon panel mounted on divers wrist to measure incident ambient light, and 8 readings of the target. Spectral reflectance was calculated for each target by first subtracting the instrument noise reading from each other reading. The corrected target readings were then divided by the corrected spectralon reading resulting in spectral reflectance of each target reading. An average target spectral reflectance was calculated by averaging the eight individual spectral reflectance's of the target. If an individual target spectral reflectance was visual considered an outlier, it was not included in the average spectral reflectance calculation. See Roelfsema at al. (2006) for additional info on the methodology of underwater spectra collection.
    Keywords: 01_Montipora; 02_Chlorodesmis; 03_Montipora-blue; 04_Montipora-brown; 05_Gorgonian; 06_Soft-coral; 07_Sand/rock; 08_Foliose-coral; 09_Montipora-purple; 10_Dead-branching; 100_Acropora-light-brown; 101_Acropora-dark-brown; 102_Montipora-green; 103_rec:1062-1069; 104_Holothurian; 11_Laurencia; 12_Gorgonian; 13_Fluoresent-coral; 14_Encrusting-brown; 15_Digitate-coral; 16_Chlorodesmis-not-shaded; 16_Chlorodesmis-shaded; 17_Acropora-light; 18_Brown-coral; 19_Rubble-2; 20_Fire-coral; 21_Acropora; 22_Montipora-green; 23_Dead-plate-200; 24_Dead-plate-100; 25_Coral-line; 26_Acropora-brown-1; 27_Acropora-yellow-1; 28_Acropora-turf-1; 29_Acropora-brown-2; 30_Acropora-yellow-2; 31_Acropora-turf-2; 32_Goniostera; 33_Digitate; 34_Rubble/turf; 35_Coral-encrusting; 36_Coraline; 37_Montipora-blue; 38_Acropora-brown; 39_Digitate-green; 40_Green-algae; 41_Acropora-blue; 42_Acropora-light-brown; 43_Acropora-light; 44_Acropora-brown; 45_Halimeda; 46_Coraline; 47_Rock; 48_Massive-live; 49_Foliose-turf; 50_Foliose-white; 51_Foliose-live; 52_Digitate-live; 53_Digitate-dead; 54_Acropora-turf; 55_Acropora-live; 56_Acropora-turf; 57_Acropora-live; 58_Acropora-blue; 59_Lobophora; 60_Porites; 61_Turf-on-porites; 62_Live-encrusting; 63_Turf-encrusting; 64_Laurencia-green; 65_Laurencia-red; 66_Halimeda; 67_Cholorodesmis; 68_Caulerpa; 69_Caulerpa-2; 70_Caulerpa-3; 71_Dictyota; 72_Dictyota-sand; 73_Turbinaria; 74_Enteromorpha; 75_Algae-sand; 76_Algae-no-sand; 77_Algae-green; 78_Turbinaria; 79_Sand; 80_Sand-BMA; 81_Pocillopora; 82_Laurencia-red; 83_Laurencia-green; 84_Fuzzy-algae; 85_Porites; 86_Giant-clam; 87_Red-sponge; 88_Turtle; 89_Encrusting-20-ms; 90_Encrusting-50-ms; 91_Encrusting-50-ms-shade; 92_Turf; 93_Encrusting; 94_Zoanthid; 95_Brown-acropora; 96_Sponge; 97_Blue-acropora; 98_Pink-sponge; 99_Turf-on-massive; Blue Pools, Heron Reef, Australia; Bommie, Heron Reef, Australia; Canyon, Heron Reef, Australia; Comment of event; Date/Time of event; Elevation of event; Event label; Latitude of event; Location of event; Longitude of event; North Bommie, Heron Reef, Australia; Plate ledge, Heron Reef, Australia; Reef flat, Heron Reef, Australia; Reflectance, total; SPEC; Spectrophotometer; Spectrophotometer, Ocean Optics USB2000; Wavelength
    Type: Dataset
    Format: text/tab-separated-values, 240368 data points
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  • 9
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    PANGAEA
    In:  Supplement to: Hedley, John; Roelfsema, Christiaan M; Phinn, Stuart R (2009): Efficient radiative transfer model inversion for remote sensing applications. Remote Sensing of Environment, 113(11), 2527-2532, https://doi.org/10.1016/j.rse.2009.07.008
    Publication Date: 2023-01-13
    Description: A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.
    Keywords: Heron_Reef; Heron Reef, Great Barrier Reef, Queensland
    Type: Dataset
    Format: application/x-gzip, 30.1 MBytes
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  • 10
    Publication Date: 2023-01-13
    Description: This airborne hyperspectral (19 bands) image data of Heron Reef, Great Barrier Reef, Australia is derived from Compact Airborne Spectrographic Imager (CASI) data acquired on 1st and 3rd of July 2002, latitude -23.45, longitude 151.92. Processing and correction to at-surface data was completed by Karen Joyce (Joyce, 2004). Raw imagery consisted several images corresponding to the number of flight paths taken to cover the entire Heron Reef. Spatial resolution is one meter. Radiometric corrections converted the at-sensor digital number values to at surface spectral radiance values using sensor specific calibration coefficients and CSIRO's c-WomBat-c atmospheric correction software. Geometric corrections were done using field collected coordinates of features identified in the image. Projection used was Universal Transverse Mercator Zone 56 South and Datum used was WGS 84. Image data is in TIFF format.
    Keywords: DATE/TIME; Date/time end; File content; File format; File size; Heron_Reef; Heron Reef, Great Barrier Reef, Queensland; LATITUDE; Latitude 2; LONGITUDE; Longitude 2; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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