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| griddap | Subset | tabledap | Make A Graph | wms | files | Accessible | Title | Summary | FGDC | ISO 19115 | Info | Background Info | RSS | Institution | Dataset ID | |
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| log in | [Manually annotated reef halos from 6 study areas] - Manually annotated reef halos based on sattelite imagery from 6 study areas as training and test data for a deep learning model (CAREER: Decoding seascape-scale vegetation patterns on coral reefs to understand ecosystem health: Integrating research and education from organismal to planetary scales) | Reef halos are rings of bare sand that surround coral reef patches. Halo formation is likely to be the indirectly result of interactions between relatively healthy predator and herbivore populations. To reduce the risk of predation, herbivores preferentially graze close to the safety of the reef, potentially affecting the presence and size of the halo. Reef halos are readily visible in remotely sensed imagery, and monitoring their presence and changes in size may therefore offer clues as to how predator and herbivore populations are faring. However, manually identifying and measuring halos is slow and limits the spatial and temporal scope of studies. There are currently no existing tools to automatically identify single reef halos and measure their size to speed up their identification and improve our ability to quantify their variability over space and time. \n\nHere we present a set of convolutional neural networks aimed at identifying and measuring reef halos from very high-resolution satellite imagery (i.e., ∼0.6 m spatial resolution). We show that deep learning algorithms can successfully detect and measure reef halos with a high degree of accuracy (F1 = 0.824), thereby enabling faster, more accurate spatio-temporal monitoring of halo size. This tool will aid in the global study of reef halos, and potentially coral reef ecosystem monitoring, by facilitating our discovery of the ecological dynamics underlying reef halo presence and variability.\n\ncdm_data_type = Other\nVARIABLES:\nAOI (unitless)\nObject_Id (unitless)\nSkySate_image_ID (unitless)\nClassname (unitless)\nlatitude (Mean_latitude, degrees_north)\nlongitude (Mean_longitude, degrees_east)\nSubset (unitless)\n | BCO-DMO | bcodmo_dataset_932211_v1 | ||||||||||||
| log in | [Mask R-CNN and U-Net models and reef halo ouput calculations] - Mask R-CNN and U-Net model and output of coral reef halo measurements based on global multispectral satellite imagery (CAREER: Decoding seascape-scale vegetation patterns on coral reefs to understand ecosystem health: Integrating research and education from organismal to planetary scales) | Reef halos are rings of bare sand that surround coral reef patches. Halo formation is likely to be the indirectly result of interactions between relatively healthy predator and herbivore populations. To reduce the risk of predation, herbivores preferentially graze close to the safety of the reef, potentially affecting the presence and size of the halo. Reef halos are readily visible in remotely sensed imagery, and monitoring their presence and changes in size may therefore offer clues as to how predator and herbivore populations are faring. However, manually identifying and measuring halos is slow and limits the spatial and temporal scope of studies. There are currently no existing tools to automatically identify single reef halos and measure their size to speed up their identification and improve our ability to quantify their variability over space and time. \n\nHere we present a set of convolutional neural networks aimed at identifying and measuring reef halos from very high-resolution satellite imagery (i.e., ∼0.6 m spatial resolution). We show that deep learning algorithms can successfully detect and measure reef halos with a high degree of accuracy (F1 = 0.824), thereby enabling faster, more accurate spatio-temporal monitoring of halo size. This tool will aid in the global study of reef halos, and potentially coral reef ecosystem monitoring, by facilitating our discovery of the ecological dynamics underlying reef halo presence and variability.\n\ncdm_data_type = Other\nVARIABLES:\nAOI (unitless)\nobject_id (unitless)\nhArea_m2 (square meter (m2))\nrArea_m2 (square meter (m2))\nlog_hArea_m2 (square meter (m2))\nlog_rArea_m2 (square meter (m2))\n | BCO-DMO | bcodmo_dataset_943698_v1 | ||||||||||||
| log in | [ZooProcess and Ecotaxa Output Along Physical Gradients from OAPS] - ZooProcess and Ecotaxa output from ZooSCANs of zooplankton collected along physical gradients during OAPS MOCNESS tows during R/V Oceanus northwest Atlantic 2011 cruise OC473 and R/V New Horizon northeast Pacific 2012 cruise NH1208 and imaged in 2021-2022 (Quantifying the drivers of midwater zooplankton community structure) | This dataset consists of the imaging portion of the study described below and includes ZooProcess and Ecotaxa outputs from ZooSCANs performed of zooplankton collected during Multiple Opening-Closing Net and Environmental Sensing System (MOCNESS) tows during R/V Oceanus cruise OC473 in the Northwestern Atlantic in 2011 and R/V New Horizon cruise NH1208 in the Northeastern Pacific in 2012. It includes data for this project from Ecotaxa (export v1.0), an online machine-learning platform that assists in identifying organisms and particles. The dataset also includes particle measurements generated by ZooProcess software. Day and night stations were sampled between 0 to 1000m depths from 35 to 50 N in the northwest Atlantic in 2011, and from 35 and 50N along CLIVAR line P17N in 2012. These representative subsamples of the formalin-preserved zooplankton community from each net were imaged in 2021 and 2022.\n\nProject description: The objective of this study was to determine how environmental variables shape zooplankton community structure in the midwater. Our primary overarching hypothesis was that the abundance and size class distribution of the zooplankton community are decoupled and are influenced by different environmental variables. Furthermore, differences in zooplankton community composition and diversity in the observed distinct oceanic biogeographical provinces additionally influences both factors. Since zooplankton contributions to biogeochemistry are size dependent, standard descriptions of zooplankton community (biomass, which is a product of size and abundance) are insufficient to generate a predictive understanding of the role of zooplankton in biogeochemical cycles. The project uses particle imaging technology and metabarcoding of archived biological samples in conjunction with open access hydrographic data from two cruises conducted in the N. Atlantic and N. Pacific to test these hypotheses.\n\ncdm_data_type = Other\nVARIABLES:\nobject_id (unitless)\nlatitude (Object_lat_start, degrees_north)\nlongitude (Object_lon_start, degrees_east)\nobject_date (unitless)\nobject_time (unitless)\ntime (Object_iso_datetime_utc, seconds since 1970-01-01T00:00:00Z)\n... (146 more variables)\n | BCO-DMO | bcodmo_dataset_932252_v1 | ||||||||||||
| log in | [ZooProcess and Ecotaxa Output for Zooplankton Mediated Aggregates] - ZooProcess and Ecotaxa output from ZooSCANs of zooplankton collected with MOCNESS tows during six R/V Atlantic Explorer cruises from 2021 to 2023 (Collaborative Research: Zooplankton mediation of particle formation in the Sargasso Sea) | This dataset consists of ZooProcess and Ecotaxa outputs from ZooSCANs of plankton caught in the upper 600m using Multiple Opening-Closing Net and Environmental Sensing System (MOCNESS) tows during day- and night-time. It includes data for this project from Ecotaxa (export v1.0), an online machine-learning platform that assists in identifying organisms and particles. The dataset also includes particle measurements generated by ZooProcess software. These samples were collected and processed over two years, with three cruises a year to capture distinct seasons. The goal of this data was to assess high-resolution vertical distribution of zooplankton in order to distinguish diel vertical migrators from resident populations and to quantify contributions to particulate organic carbon flux via fecal pellet production. \n\nProject description: The oceanic biological carbon pump refers to the export of dissolved and particulate organic carbon to the deep ocean, and it is a significant driver of atmospheric carbon uptake by the oceans. Evidence from long-term research carried out at the Bermuda Atlantic Time-series Study (BATS) site suggests that the spectrum of particles collected by gel-traps below the euphotic zone changes drastically below 150 m, which is attributed to resident populations of zooplankton that feed on vertically migrating zooplankton as well as sinking particles. The goals of this study are to investigate the role of different zooplankton taxa on both particle aggregate formation and in particle transformation, and to compare and characterize the particles generated by the zooplankton communities with those collected by particle traps.\n\ncdm_data_type = Other\nVARIABLES:\nobject_id (unitless)\nlatitude (Object_lat_start, degrees_north)\nlongitude (Object_lon_start, degrees_east)\nobject_date (unitless)\nobject_time (unitless)\ntime (Object_iso_datetime_utc, seconds since 1970-01-01T00:00:00Z)\nobject_link (unitless)\nobject_depth_min (Meters)\nobject_depth_max (Meters)\n... (143 more variables)\n | BCO-DMO | bcodmo_dataset_931883_v1 |