Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019 (NCEI Accession 0269246)
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title: Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019 (NCEI Accession 0269246)
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abstract: This data package includes datasets and benthic imagery that support the development of a semi-automated coral bleaching classifier in CoralNet. The datasets in this package include annotations of imagery produced to test and train the CoralNet Bleaching Classifier, the annotation label set used in CoralNet, and site-level metadata for annotated imagery. The datasets were generated by scientists of the Ecosystem Sciences Division (ESD) at the Pacific Islands Fisheries Science Center (PIFSC) at NOAA, funded by the Coral Reef Conservation Program (CRCP). This data package includes non-ESD imagery used to develop this classifier, including images of sites in the Papahānaumokuākea National Monument (collected in 2014 by Courtney Couch, a scientist of the ESD), and of sites in Kaneohe Bay, Oahu (in 2015 by The Nature Conservancy). Other source imagery that was used to develop this classifier was collected by the ESD during the 2019 Hawaii coral bleaching event, as well as during the 2015, 2016, and 2019 NWHI National Coral Reef Monitoring Program (NCRMP) survey missions following the traditional photoquadrat collection methodology for NCRMP surveys; this imagery has already been archived, or is a pending archive package; they are described and accessible at https://www.fisheries.noaa.gov/inport/item/59193 and https://www.fisheries.noaa.gov/inport/item/36152. To create this classifier, named the NOAA ESD Coral Bleaching Classifier (accessible at: https://coralnet.ucsd.edu/source/2947/), a set of training imagery with corresponding point coordinates and label annotations were uploaded to the classifier's source. The training annotations were made by human annotators on both randomly generated and targeted points. Once the training imagery and annotations were uploaded, the automated classification abilities of the classifier were tested by uploading imagery and point locations, but without corresponding label annotations. The machine generated labels for these points were then compared against the human generated labels, which had not been uploaded. See the CoralNet Bleaching Classifier SOP for more information: https://doi.org/10.25923/d0re-9y93.
purpose: Over the past decade, there has been a notable increase in the frequency and severity of global bleaching events. Since 2014, the Hawaiian Archipelago has experienced three mass coral bleaching events, which resulted in high coral mortality throughout the archipelago. With more frequent and severe global bleaching events on the horizon and the efficiency of collecting bleaching data using digital imagery, monitoring programs in Hawaiʻi need tools to efficiently extract bleaching data from images. Creating tools to quickly and accurately quantify the extent of coral bleaching is an important next step in developing strategies to appropriately respond to and monitor future mass bleaching events.
credit: Related Funding Agency: US DOC; NOAA; NOS; Coral Reef Conservation Program (Coral Reef Conservation Program - CRCP)
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description: Global Change Master Directory (GCMD). 2023. GCMD Keywords, Version 18. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords
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keyword: Papahanaumokuakea Marine National Monument
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otherConstraints: Cite as: Ecosystem Sciences Division, Pacific Islands Fisheries Science Center (2022). Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019 (NCEI Accession 0269246). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0269246. Accessed [date].
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useLimitation: Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
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title: Pacific Islands Fisheries Science Center, 2022: Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019, https://www.fisheries.noaa.gov/inport/item/67962.
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title: Pacific Islands Fisheries Science Center, 2020: National Coral Reef Monitoring Program: Benthic Images Collected from Stratified Random Sites (StRS) across the Hawaiian Archipelago from 2013 to 2019, https://www.fisheries.noaa.gov/inport/item/36152.
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title: Ehrenberg J., Winston, M., Oliver, T., and Couch, C. 2022. Development of a Semi-automated Coral Bleaching Classifier in CoralNet: A Summary of Standard Operating Procedures and Report of Results. U.S. Dept. of Commerce, NOAA Technical Memorandum NOAA-TM-NMFS-PIFSC-133, 9 p. https://doi.org/10.25923/d0re-9y93
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supplementalInformation: Submission Package ID: 04ACLX
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description: NCEI Accession 0269246 v1.1 was published.
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description: Parameter or Variable: CORAL - BLEACHING (measured); Units: unitless; Observation Category: benthic; Sampling Instrument: photograph; Sampling and Analyzing Method: Photographs were annotated with the web-based tool CoralNet into three categories: hard coral, bleached hard coral, and unknown. Human annotations were used to train the CoralNet Bleaching Classifier, and rounds of training with human and machine annotations followed. Refer to SOP for details; Data Quality Method: Annotators are well trained and calibrated to ensure the source’s AI is being trained correctly.
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description: Parameter or Variable: images (measured); Units: unitless; Observation Category: in situ; Sampling Instrument: camera; Sampling and Analyzing Method: A total of 5207 images were used, with approximately 30 images for each site; however, image numbers ranged from 5 - 72 images per site. Images were captured in 2015 by the Nature Conservancy and in 2014 by ESD scientist, Courtney Couch. Other imagery used to develop the classifier tool followed the standard NCRMP stratified-random site (StRS) photoquadrat protocols and has already been archived and is accessible at https://www.fisheries.noaa.gov/inport/item/36152#itemIdentification.; Data Quality Method: The photos are reviewed by scientists for accuracy and usability. To ensure consistency, images were color corrected using Adobe Lightroom if it appeared that the camera they were taken from was not properly white balanced in the field..
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description: still images used as the basis for making parameter determinations
