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  • 1
    facet.materialart.
    Unknown
    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
    Publication Date: 2023-01-13
    Description: 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.
    Keywords: DATE/TIME; EasternBanks; Moreton Bay, Brisbane, South East Queensland, Coral Sea, Australia; MULT; Multiple investigations; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 27 data points
    Location Call Number Limitation Availability
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  • 2
    facet.materialart.
    Unknown
    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
    Publication Date: 2023-12-05
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 7333 data points
    Location Call Number Limitation Availability
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