Data

EcoRRAP Benthic Data 2021-2023

Australian Institute of Marine Science
Australian Institute of Marine Science (AIMS)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
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A 1000-pixel bounding box was applied to the edges of the image to remove areas of distortion (original image size was 8256x5504 pixels). Images were extracted for 352 plots in 2021, and 336 plots in 2022, and 327 plots in 2023 due to field imaging constraints. Twelve images per plot with 50 points per image were analysed in ReefCloud. Images from a subset of plots were manually annotated in ReefCloud to train an automated image analysis model and the remaining plots were analysed using the automated model. Images were annotated using the EcoRRAP Community Composition Label Set. The ReefCloud ML model was trained with a goal of achieving 80% accuracy for identification of benthos. Training proceeded as an iterative process with model diagnostics assessed after each round of training. 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While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.&rft_rights=Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: Australian Institute of Marine Science (AIMS). (2023). EcoRRAP Benthic Data 2021-2023. https://apps.aims.gov.au/metadata/view/48de2e3c-52a9-42f0-84ac-eb13baab0b07, accessed[date-of-access].&rft_subject=oceans&rft.type=dataset&rft.language=English Access the data
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http://creativecommons.org/licenses/by-nc/3.0/au/

Use Limitation: All AIMS data, products and services are provided "as is" and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.

Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: "Australian Institute of Marine Science (AIMS). (2023). EcoRRAP Benthic Data 2021-2023. https://apps.aims.gov.au/metadata/view/48de2e3c-52a9-42f0-84ac-eb13baab0b07, accessed[date-of-access]".

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Brief description

This dataset contains benthic data from generated from imagery collected by divers conducting photogrammetric monitoring at EcoRRAP sites (see SOP #14: Reef monitoring sampling methods | AIMS). These images used are the underlying images of the EcoRRAP 3D models and are used here to assess benthic community composition within EcoRRAP plots. A subset of images were extracted for this dataset by using a python script that filtered for image position within 3D models, to enable the best spatial stratification, as well as for aspect to maximise extraction of images facing the benthos. A 1000-pixel bounding box was applied to the edges of the image to remove areas of distortion (original image size was 8256x5504 pixels). Images were extracted for 352 plots in 2021, and 336 plots in 2022, and 327 plots in 2023 due to field imaging constraints. Twelve images per plot with 50 points per image were analysed in ReefCloud. Images from a subset of plots were manually annotated in ReefCloud to train an automated image analysis model and the remaining plots were analysed using the automated model. Images were annotated using the EcoRRAP Community Composition Label Set. The ReefCloud ML model was trained with a goal of achieving 80% accuracy for identification of benthos. Training proceeded as an iterative process with model diagnostics assessed after each round of training. Once performance targets were reached across course taxonomic classes, targeted annotation was undertaken to improve the model performance for specific classes.

Lineage

Maintenance and Update Frequency: asNeeded

Notes

Credit
Ferrari Legorreta, R
Credit
Smallhorn-West, JB
Credit
The images used in this data were collected by the 'Field integrated testing project' of EcoRRAP. We thank all team members, students, and volunteers for their efforts
Credit
Remmers, T
Credit
Toor, M
Credit
Boutros, N

Modified: 17 10 2024

This dataset is part of a larger collection

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Subjects
oceans |

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Other Information
Preliminary report on benthic data, including summary plots

uri : https://api.aims.gov.au/data-v2.0/48de2e3c-52a9-42f0-84ac-eb13baab0b07/files/Report_Benthic_Annotation_20230905.pdf

EcoRRAP Benthic Data 2021-2023 [size: 1215kb]

uri : https://api.aims.gov.au/data-v2.0/48de2e3c-52a9-42f0-84ac-eb13baab0b07/files/EcoRRAP_Benthic_Data_202405.zip

global : fe9659f1-12d6-4acf-ab89-67acdd37efe5

Identifiers
  • global : 48de2e3c-52a9-42f0-84ac-eb13baab0b07
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