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  • 1
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    Results of wave modelling for Moreton Bay, Southeast Queensland, and a reef lagoon off Lizard Island, Great Barrier Reef, Australia (2015)
    PANGAEA
    In:  Supplement to: Callaghan, David P; Leon, Javier X; Saunders, Megan I (2015): Wave modelling as a proxy for seagrass ecological modelling: Comparing fetch and process-based predictions for a bay and reef lagoon. Estuarine, Coastal and Shelf Science, 153, 108-120, https://doi.org/10.1016/j.ecss.2014.12.016
    Details
    Publikationsdatum: 2023-01-13
    Beschreibung: The distribution, abundance, behaviour, and morphology of marine species is affected by spatial variability in the wave environment. Maps of wave metrics (e.g. significant wave height Hs, peak energy wave period Tp, and benthic wave orbital velocity URMS) are therefore useful for predictive ecological models of marine species and ecosystems. A number of techniques are available to generate maps of wave metrics, with varying levels of complexity in terms of input data requirements, operator knowledge, and computation time. Relatively simple "fetch-based" models are generated using geographic information system (GIS) layers of bathymetry and dominant wind speed and direction. More complex, but computationally expensive, "process-based" models are generated using numerical models such as the Simulating Waves Nearshore (SWAN) model. We generated maps of wave metrics based on both fetch-based and process-based models and asked whether predictive performance in models of benthic marine habitats differed. Predictive models of seagrass distribution for Moreton Bay, Southeast Queensland, and Lizard Island, Great Barrier Reef, Australia, were generated using maps based on each type of wave model. For Lizard Island, performance of the process-based wave maps was significantly better for describing the presence of seagrass, based on Hs, Tp, and URMS. Conversely, for the predictive model of seagrass in Moreton Bay, based on benthic light availability and Hs, there was no difference in performance using the maps of the different wave metrics. For predictive models where wave metrics are the dominant factor determining ecological processes it is recommended that process-based models be used. Our results suggest that for models where wave metrics provide secondarily useful information, either fetch- or process-based models may be equally useful.
    Materialart: Dataset
    Format: application/zip, 2 datasets
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
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    A 20 m spatial resolution seamless multisource Digital Elevation/Depth Model for Lizard Island, northern Great Barrier Reef (2012)
    PANGAEA
    In:  Supplement to: Leon, Javier X; Phinn, Stuart R; Hamylton, Sarah; Saunders, Megan I (2013): Filling the 'white ribbon' - A seamless multisource Digital Elevation/Depth Model for Lizard Island, northern Great Barrier Reef. International Journal of Remote Sensing, 34(18), 6337-6354, https://doi.org/10.1080/01431161.2013.800659
    Details
    Publikationsdatum: 2023-01-13
    Beschreibung: Hydrographers have traditionally referred to the nearshore area as the "white ribbon" area due to the challenges associated with the collection of elevation data in this highly dynamic transitional zone between terrestrial and marine environments. Accordingly, available information in this zone is typically characterised by a range of datasets from disparate sources. In this paper we propose a framework to 'fill' the white ribbon area of a coral reef system by integrating multiple elevation and bathymetric datasets acquired by a suite of remote-sensing technologies into a seamless digital elevation model (DEM). A range of datasets are integrated, including field-collected GPS elevation points, terrestrial and bathymetric LiDAR, single and multibeam bathymetry, nautical chart depths and empirically derived bathymetry estimations from optical remote sensing imagery. The proposed framework ranks data reliability internally, thereby avoiding the requirements to quantify absolute error and results in a high resolution, seamless product. Nested within this approach is an effective spatially explicit technique for improving the accuracy of bathymetry estimates derived empirically from optical satellite imagery through modelling the spatial structure of residuals. The approach was applied to data collected on and around Lizard Island in northern Australia. Collectively, the framework holds promise for filling the white ribbon zone in coastal areas characterised by similar data availability scenarios. The seamless DEM is referenced to the horizontal coordinate system MGA Zone 55 - GDA 1994, mean sea level (MSL) vertical datum and has a spatial resolution of 20 m.
    Schlagwort(e): Lizard_Island; Lizard Island, northern Great Barrier Reef
    Materialart: Dataset
    Format: application/zip, 391.2 kBytes
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
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    Benthic and substrate cover data derived from photo-transect surveys in Lizard Island Reef conducted on December 10-15, 2011 (2013)
    PANGAEA
    Details
    Publikationsdatum: 2023-01-13
    Beschreibung: Underwater georeferenced photo-transect surveys were conducted on December 10-15, 2011 at various sections of the reef at Lizard Island, Great Barrier Reef. For this survey a snorkeler or diver swam over the bottom while taking photos of the benthos at a set height using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A standard digital compact camera was placed in an underwater housing and fitted with a 16 mm lens which provided a 1.0 m x 1.0 m footprint, at 0.5 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey diver/snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry-bag and logged the position as it floated at the surface while being towed by the photographer. A total of 5,735 benthic photos were taken. A floating GPS setup connected to the swimmer/diver by a line enabled recording of coordinates of each benthic photo (Roelfsema 2009). Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 78 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.
    Schlagwort(e): File content; File name; Lizard_Island; Lizard Island, northern Great Barrier Reef; Uniform resource locator/link to file
    Materialart: Dataset
    Format: text/tab-separated-values, 6 data points
    Standort Signatur Einschränkungen Verfügbarkeit
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    DATA PANGAEA
  • 4
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    Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles (2014)
    PANGAEA
    In:  Supplement to: Roelfsema, Christiaan M; Lyons, Mitchell B; Kovacs, Eva M; Maxwell, Paul; Saunders, Megan I; Samper-Villarreal, Jimena; Phinn, Stuart R (2014): Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach. Remote Sensing of Environment, 150, 172-187, https://doi.org/10.1016/j.rse.2014.05.001
    Details
    Publikationsdatum: 2023-01-13
    Beschreibung: The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (〉 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.
    Schlagwort(e): DATE/TIME; EasternBanks; Moreton Bay, Brisbane, South East Queensland, Coral Sea, Australia; MULT; Multiple investigations; Uniform resource locator/link to file
    Materialart: Dataset
    Format: text/tab-separated-values, 27 data points
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Wind and ocean swell wave model (SWAN) results for coastal waters of Lizard Island, Queensland, Australia (2015)
    PANGAEA
    Details
    Publikationsdatum: 2023-01-13
    Schlagwort(e): Lizard_Island; Lizard Island, northern Great Barrier Reef
    Materialart: Dataset
    Format: application/zip, 679.2 MBytes
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
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    Wind and ocean swell wave model (SWAN) results for coastal waters of Moreton Bay, Queensland, Australia (2015)
    PANGAEA
    Details
    Publikationsdatum: 2023-01-13
    Schlagwort(e): Moreton_Bay; Moreton Bay, Brisbane, South East Queensland, Coral Sea, Australia
    Materialart: Dataset
    Format: application/zip, 21.2 MBytes
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
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    Benthic and substrate cover data derived from photo-transect surveys in Lizard Island, Great Barrier Reef conducted on October 3-7, 2012 (2013)
    PANGAEA
    Details
    Publikationsdatum: 2023-01-13
    Beschreibung: Underwater georeferenced photo-transect surveys were conducted on October 3-7, 2012 at various sections of the reef and lagoon at Lizard Island, Great Barrier Reef. For this survey a snorkeler swam while taking photos of the benthos at a set distance from the benthos using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A Canon G12 digital camera was placed in a Canon underwater housing and photos were taken at 1 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry bag and logged the position at the surface while being towed by the photographer (Roelfsema, 2009). A total of 1,265 benthic photos were taken. Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 79 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.
    Schlagwort(e): File content; File name; Lizard_Island; Lizard Island, northern Great Barrier Reef; Uniform resource locator/link to file
    Materialart: Dataset
    Format: text/tab-separated-values, 6 data points
    Standort Signatur Einschränkungen Verfügbarkeit
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    DATA PANGAEA
  • 8
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    Habitat Map for Lizard Island reef, Australia derived from a photo-transect survey field data collected in December 2011 and September/October 2012
    PANGAEA
    In:  Supplement to: Saunders, Megan I; Bayraktarov, Elisa; Roelfsema, Christiaan M; Leon, Javier X; Samper-Villarreal, Jimena; Phinn, Stuart R; Lovelock, Catherine E; Mumby, Peter John (2015): Spatial and temporal variability of seagrass at Lizard Island, Great Barrier Reef. Botanica Marina, 58(1), 35-49, https://doi.org/10.1515/bot-2014-0060
    Details
    Publikationsdatum: 2023-05-12
    Beschreibung: An object based image analysis approach (OBIA) was used to create a habitat map of the Lizard Reef. Briefly, georeferenced dive and snorkel photo-transect surveys were conducted at different locations surrounding Lizard Island, Australia. For the surveys, a snorkeler or diver swam over the bottom at a depth of 1-2m in the lagoon, One Tree Beach and Research Station areas, and 7m depth in Watson's Bay, while taking photos of the benthos at a set height using a standard digital camera and towing a surface float GPS which was logging its track every five seconds. The camera lens provided a 1.0 m x 1.0 m footprint, at 0.5 m height above the benthos. Horizontal distance between photos was estimated by fin kicks, and corresponded to a surface distance of approximately 2.0 - 4.0 m. Approximation of coordinates of each benthic photo was done based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the gps coordinates that were logged at a set time before and after the photo was captured. Dominant benthic or substrate cover type was assigned to each photo by placing 24 points random over each image using the Coral Point Count excel program (Kohler and Gill, 2006). Each point was then assigned a dominant cover type using a benthic cover type classification scheme containing nine first-level categories - seagrass high (〉=70%), seagrass moderate (40-70%), seagrass low (〈= 30%), coral, reef matrix, algae, rubble, rock and sand. Benthic cover composition summaries of each photo were generated automatically in CPCe. The resulting benthic cover data for each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 56 South. The OBIA class assignment followed a hierarchical assignment based on membership rules with levels for "reef", "geomorphic zone" and "benthic community" (above).
    Schlagwort(e): Lizard_Island; Lizard Island, northern Great Barrier Reef
    Materialart: Dataset
    Format: application/zip, 4.5 MBytes
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
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    Benthic and substrate cover data derived from photo-transect surveys in a Sub Set of Roviana Reef, Solomon Islands, 2013-05-05 to 2013-05-09 (2014)
    PANGAEA
    Details
    Publikationsdatum: 2023-09-16
    Schlagwort(e): Roviana_Reef; SAT; Satellite remote sensing; Solomon Islands
    Materialart: Dataset
    Format: application/zip, 136.1 kBytes
    Standort Signatur Einschränkungen Verfügbarkeit
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    DATA PANGAEA
  • 10
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    Morphometrics of seagrasses at species level, Moreton Bay, Australia determined from core samples collected in 2012-2013 (2016)
    PANGAEA
    In:  Supplement to: Samper-Villarreal, Jimena; Lovelock, Catherine E; Saunders, Megan I; Roelfsema, Christiaan M; Mumby, Peter John (2016): Organic carbon in seagrass sediments is influenced by seagrass canopy complexity, turbidity, wave height, and water depth. Limnology and Oceanography, 61(3), 938-952, https://doi.org/10.1002/lno.10262
    Details
    Publikationsdatum: 2023-12-05
    Beschreibung: Seagrass meadows are important marine carbon sinks, yet they are threatened and declining worldwide. Seagrass management and conservation requires adequate understanding of the physical and biological factors determining carbon content in seagrass sediments. Here, we identified key factors that influence carbon content in seagrass meadows across several environmental gradients in Moreton Bay, SE Queensland. Sampling was conducted in two regions: (1) Canopy Complexity, 98 sites on the Eastern Banks, where seagrass canopy structure and species composition varied while turbidity was consistently low; and (2) Turbidity Gradient, 11 locations across the entire bay, where turbidity varied among sampling locations. Sediment organic carbon content and seagrass structural complexity (shoot density, leaf area, and species specific characteristics) were measured from shallow sediment and seagrass biomass cores at each location, respectively. Environmental data were obtained from empirical measurements (water quality) and models (wave height). The key factors influencing carbon content in seagrass sediments were seagrass structural complexity, turbidity, water depth, and wave height. In the Canopy Complexity region, carbon content was higher for shallower sites and those with higher seagrass structural complexity. When turbidity varied along the Turbidity Gradient, carbon content was higher at sites with high turbidity. In both regions carbon content was consistently higher in sheltered areas with lower wave height. Seagrass canopy structure, water depth, turbidity, and hydrodynamic setting of seagrass meadows should therefore be considered in conservation and management strategies that aim to maximize sediment carbon content.
    Schlagwort(e): AM_T23-S; AM_T24-W; AM_T3-N; AM_T4-S; AM_T5-N; AM_T6-W; AM_T7-N; AP.1; AP.2; AP.3; AP.4; AP.5; AP.6; Biomass, dry mass per area; BM_WA_04; BM_WA_05; BMS_AM_06; BMS_AM_07; BMS_AM_08; BMS_AM_10; BMS_AM_11; BMS_AM_11-3; BMS_AM_16; BMS_AM_18; BMS_AM_7; BMS_AM_8; BMS_MA_01; BMS_MA_03; BMS_MA_05; BMS_MA_06; BMS_MA_07; BMS_MA_11; BMS_MA_18; BMS_MA_3; BMS_MA_5; BMS_MA_6; BMS_MO_01; BMS_MO_02; BMS_MO_09; BMS_MO_15; BMS_MO_16; BMS_MO_18; BMS_WA_0; BMS_WA_01; BMS_WA_02; BMS_WA_4; BMS_WA_5; C.1; C.2; C.3; C.4; C.5; C.6; Cymodocea serrulata, area; Cymodocea serrulata, biomass, dry mass; Cymodocea serrulata, length; Cymodocea serrulata, shoots; Cymodocea serrulata, width; DATE/TIME; Density, shoots; Detritus, biomass, dry mass; DIVER; Eastern Banks, Amity Banks; Eastern Banks, Chain Banks; Eastern Banks, Maroom Banks; Eastern Banks, Moreton Banks; Eastern Banks, Wanga Wallen Banks; Event label; Halodule uninervis, area; Halodule uninervis, biomass, dry mass; Halodule uninervis, length; Halodule uninervis, shoots; Halodule uninervis, width; Halophila ovalis, area; Halophila ovalis, biomass, dry mass; Halophila ovalis, length; Halophila ovalis, shoots; Halophila ovalis, width; Halophila spinulosa, area; Halophila spinulosa, biomass, dry mass; Halophila spinulosa, length; Halophila spinulosa, shoots; Halophila spinulosa, width; L.1; L.2; L.3; L.4; L.5; L.6; Latitude of event; Location of event; Longitude of event; M.1; M.2; M.3; M.4; M.5; M.6; MA_T2-E; MA_T3-E; MA_T4-E; MA_T5-E; Macroalgae, biomass, dry mass; Mangrove, biomass, dry mass; MB_AM_T10-E; MB_AM_T10-W; MB_AM_T1-E; MB_AM_T23-N; MB_AM_T23-S; MB_AM_T24-E; MB_AM_T24-W; MB_AM_T3-N; MB_AM_T3-S; MB_AM_T4-N; MB_AM_T4-S; MB_AM_T5-N; MB_AM_T5-S; MB_AM_T6_end; MB_AM_T7-N; MB_AM_T7-S; MB_AM_T8-W; MB_CH1_end; MB_CH1_start; MB_MA_T1-N_end; MB_MA_T1-S; MB_MA_T2_end; MB_MA_T2-W_start; MB_MA_T3_end; MB_MA_T3-W; MB_MA_T4_end; MB_MA_T4-S_start; MB_MA_T5_end; MB_MA_T5_start; MB_MO_T21-E_start; MB_MO_T21-W_end; MB_MO_T2-N_end; MB_MO_T2-S_start; MB_MO_T30_end; MB_MO_T30_start; MB_MO_T3-E_end; MB_MO_T3-W_start; MB_MO_T4_end_B2; MB_MO_T4-E_start_B20; MB_MO_T4-E_start_B5; MB_MO_T4-W_end_B19; MB_MO_T5_end; MB_MO_T5-E_start; MB_MO_T6-E_end; MB_MO_T6-W_start; MB_MO_T8-N_start; MB_MO_T8-S_end; MB_MO_T9_end; MB_MO_T9_start; MB_WA_T3-E; MB_WA_T3-W; MB_WA_T4-E; MB_WA_T4-W; MB_WA_T5-E; MB_WA_T5-W; MB_WA_T6-E; MB_WA_T6-W; MB_WA_TW1-E; MB_WA_TW1-W; MO_T2-S; MO_T30-E; MO_T3-E; MO_T3-W; MO_T4-E; MO_T5-E; MO_T6-W; MO_T9-S; Moreton Bay, Amity Point; Moreton Bay, Cleveland; Moreton Bay, Lota; Moreton Bay, Myora Springs; Moreton Bay, North Deception Bay; Moreton Bay, Port of Brisbane; Moreton Bay, Wellington; NDB.1; NDB.2; NDB.3; NDB.4; NDB.5; NDB.6; PoB.1; PoB.2; PoB.3; PoB.4; PoB.5; PoB.6; Sampling by diver; see Samper-Villarreal et al. (2016); Syringodium isoetifolium, area; Syringodium isoetifolium, biomass, dry mass; Syringodium isoetifolium, length; Syringodium isoetifolium, shoots; Syringodium isoetifolium, width; T2_Starbug-1; T2_Starbug-2; T2_Starbug-3; W.1; W.2; W.3; W.4; W.5; W.6; WA_T4-W; WA_T5-N; WW_TT1; WW_TT10; WW_TT11; WW_TT2; WW_TT3; WW_TT4; WW_TT5; WW_TT6; WW_TT7; WW_TT8; WW_TT9; Zostera muelleri, area; Zostera muelleri, biomass, dry mass; Zostera muelleri, length; Zostera muelleri, shoots; Zostera muelleri, width
    Materialart: Dataset
    Format: text/tab-separated-values, 7333 data points
    Standort Signatur Einschränkungen Verfügbarkeit
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    DATA PANGAEA
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