Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019

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 Papahanoumokuakea 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missions following the traditional photoquadrat collection methodology for NCRMP surveys; this imagery has already been archived, or is a pending archive package; they are described and accessible at https://www.fisheries.noaa.gov/inport/item/59193 and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.

This data package supports the detailed guidelines for developing a semi-automated classification tool in CoralNet to assist in the identification of coral bleaching.

Over the past decade, coral bleaching events have been increasing both in frequency and severity. In the Hawaiian Archipelago, three mass coral bleaching events have been recorded since 2014. With more frequent and severe global bleaching events on the horizon and the efficiency of collecting bleaching data using digital imagery, monitoring programs in Hawai'i 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.

The CoralNet Bleaching Classifier SOP will be publicly accessible at: https://doi.org/10.25923/d0re-9y93

Ehrenberg J, Winston M, Oliver T, 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, 11 p. https://doi.org/10.25923/d0re-9y93.

Images used to create this coral bleaching classifier were collected in 2014, 2015, 2016, and 2019 during coral reef surveys across the Hawaiian Archipelago (including the Papahanoumokuakea Marine National Monument), by the Ecosystem Sciences Division (ESD) of the NOAA Pacific Islands Fisheries Science Center (PIFSC) and its partners with funding from NOAA's Coral Reef Conservation Program (CRCP), Project ID # 31361.

The imagery included in this data package are non-ESD owned and selected to train the tool based on benthic features.

This data package includes:

Labelset used to train classifier (and defines annotations in datasets):

1) NOAA_ESD_CoralBleachingClassifier_Labelset

Annotations for the Training Sites:

2) NOAA_ESD_CoralBleachingClassifier_TrainingAnnotations

Human/Machine Annotations for Test Sites (results):

3) NOAA_ESD_CoralBleachingClassifier_TestAnnotations

Image Metadata:

4) NOAA_ESD_CoralBleachingClassifier_ImageMetadata

Imagery:

5) 2015 imagery owned by The Nature Conservancy

6) 2014 imagery owned from Courtney Couch from the Northwestern Hawaiian Islands, Hawaii Island and Kaneohe Bay (Oahu), archived with NCEI; used as training source

NOAA ESD Coral Bleaching Classifier Annotations Data Dictionary defines columns of annotation datasets and can be accessed at:

https://www.fisheries.noaa.gov/inport/item/68138

NOAA ESD Coral Bleaching Classifier Image Metadata Data Dictionary defines the image metadata file and can be accessed at:

https://www.fisheries.noaa.gov/inport/item/68141

While every effort has been made to ensure that these data are accurate and reliable within the limits of the current state of the art, NOAA cannot assume liability for any damages caused by errors or omissions in the data, nor as a result of the failure of the data to function on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

Not applicable

Not applicable

NOAA Ecosystem Science Division (ESD) Data Sharing Recommendations, version 9.0 updated August 12, 2015:

ESD welcomes the opportunity to collaborate on research issues contributing to the scientific basis for better management of marine ecosystems. ESD has a very diverse set of field activities that generates large volumes of data using an array of data collection protocols.

The following recommendations are for your consideration as you use this data:

1) Data analyses should take all field exigencies into account. The most effective way to do this would be active collaboration with ESD principal investigators.

2) In all presentations, product releases, or publications using data generated by ESD, proper acknowledgement of both ESD and the individuals responsible for data collection is expected. Citing the DOI (if available) is preferred, a non-DOI example is listed below.

3) If you collect or generate data for the same study areas, ESD requests that you share relevant information on complimentary data collections.

4) Those receiving data are strongly urged to inform the ESD Data Management Team of any errors and discrepancies that are discovered during the course of using these data. They are further urged to bring to the attention of the Team all problems and difficulties encountered in using these data. This information is necessary in order to improve the collections and to facilitate more efficient and economical data processing and retrieval. The users are asked to supply copies of any missing data that may be located, and to provide information as to significant subsets and special aggregations of data that are developed in using the material provided.

Example citation:

"This publication makes use of data products provided by the Ecosystem Science Division (ESD), Pacific Islands Fisheries Science Center (PIFSC), National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), with funding support from the NOAA Coral Reef Conservation Program (CRCP). The analysis and interpretations presented here are solely that of the current authors."

Data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.

None

Please cite NOAA Fisheries, Ecosystem Science Division (ESD) when using the data.

Example:

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. NOAA's National Center for Environmental Information, https://www.fisheries.noaa.gov/inport/item/67962

None

None

Rstudio for working with annotations and web-based CoralNet annotation tool.

Good image quality is essential for using this classifier, also referred to as source in CoralNet, for developing a new classifier . Poor image quality (pixelated, discolored, or unfocused) will make it difficult for annotators/the classifier to correctly label annotations. Annotators should be well trained and calibrated to ensure the source's classifier is being trained correctly. Lastly, labels need to be clearly defined to reduce confusion for annotators.

Only shallow water (0 to 30 meter depth range) hard-bottom habitats were surveyed.

The same labelset was used throughout the training of the classifier.

Quality control is enforced by means of point-to-point, inter-observer calibration exercises that are conducted before each image analysis production series. Training modules and standard operating procedures have also been developed and documented to ensure improved performance and consistent imagery analysis results produced by multiple analysts.

NOAA IRC and NOAA Fisheries ITS resources and assets.

To create this classifier, named the NOAA ESD Coral Bleaching Classifier (see 'URLs' in this InPort record for a link to the CoralNet source), a set of training imagery with corresponding point coordinates and label annotations were uploaded to the classifier's source. The training annotations (hard coral, bleached hard coral, and unknown) 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 (link included under 'URLs' in this InPort record).