Benthic cover from automated annotation of benthic images collected at coral reef sites in the Pacific Remote Island Areas and American Samoa from 2018-06-08 to 2018-08-11 (NCEI Accession 0204646)
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title: Benthic cover from automated annotation of benthic images collected at coral reef sites in the Pacific Remote Island Areas and American Samoa from 2018-06-08 to 2018-08-11 (NCEI Accession 0204646)
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abstract: The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.
purpose: The use of the CoralNet machine-learning, image annotation tool is essential to the timely and cost-effective analysis of benthic imagery collected Pacific-wide under the auspices of the NOAA National Coral Reef Monitoring Program (NCRMP). Timely analysis and reporting of cover estimates are fundamental to understanding status and change of coral reef benthic communities, particularly in a fast-changing climate. NCRMP details a long term approach to provide an ecosystem perspective via monitoring climate, fish, benthic, and socioeconomic variables in a consistent and integrated manner. The NCRMP coordinates various NOAA Coral Reef Conservation Program (CRCP) biological, physical, and human dimensions activities into a cohesive NOAA-wide effort. Through the implementation of the NCRMP, NOAA is able to clearly and concisely communicate results of national-scale monitoring to national, state, and territorial policy makers, resource managers, and the public on a periodic basis. NCRMP is a framework for conducting sustained observations of biological, climate, and socioeconomic indicators at some ~40 priority coral reefs systems across the U.S. and its territories. This integrated approach consolidates monitoring of coral reefs under a uniform method in the Pacific, Atlantic, Caribbean, and the Gulf of Mexico for the first time. NCRMP is funded by the CRCP and supported by NOAA Fisheries, NOAA National Centers for Coastal Ocean Science (NCCOS), and many other partners. The Ecosystem Sciences Division at NOAA Fisheries is leading biological and climate monitoring in the U.S. Pacific Islands Region. The biological component of NCRMP in the Pacific provides a triennial ecological characterization at a broad spatial scale of general reef condition for reef fishes, corals and benthic habitat (i.e., fish species composition/density/size, benthic cover, and coral density/size/condition). The climate component of NCRMP in the Pacific provides a comprehensive view of climate change impacts on coral reef ecosystems and helps identify areas of resilience and vulnerability. The key indicators used to identify and monitor climate-driven trends include 1) thermal stress caused by changes in sea temperature, 2) ocean acidification resulting from changes in carbonate chemistry, and 3) ecological impacts by collecting data on coral growth rates, erosion, and community structure to understand the impacts of thermal stress and ocean acidification on the ecosystem. Each year, ESD scientists work closely with CRCP and partners during Pacific RAMP missions to collect data using moored oceanographic (subsurface temperature recorders) and ecological (calcification accretion units [CAUs] and autonomous reef monitoring structures [ARMS]) instruments stationed at fixed sites in the Pacific Ocean, and water samples collected by divers. The in-situ data (along with satellite-based observations) are also used in modeling efforts. Innovative analysis techniques are used to develop products that give fellow scientists, managers, decision makers and the public a better understanding of a region's resources and how they are changing over time.
credit: Related Funding Agency: NOAA Coral Reef Conservation Program
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description: Parameter or Variable: BENTHIC SPECIES - TAXA IDENTITIES (measured); Units: unitless; Observation Category: other; Sampling Instrument: photograph; Sampling and Analyzing Method: Benthic habitat images collected during photoquadrat surveys at stratified random (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites (30 images per site) were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). The description of each functional group is included in the benthic image analysis classification scheme.; Data Quality Method: Data sets derived from the machine-learning, image annotation tool, CoralNet, are highly reliable within the stipulated taxonomic context (Tier 1). Human, expert-analyzed imagery from NOAA’s Pacific Reef Assessment and Monitoring Program (Pacific RAMP) is used to train the machine-learning image analysis tool, CoralNet (Beijbon et al 2015), to generate fully-automated Tier 1 benthic estimates of site-level coral cover that are highly comparable to those generated by human analysts (Pearson’s r > 0.97, and with bias of 1% or less; Williams et al. 2019). CoralNet was generally effective at estimating coral cover (Pearson’s r > 0.92 and with bias of 2% or less in 6 of 7 cases), but performance was mixed for other groups including algal categories. Pre- and post-automated benthic image analysis quality control procedures include checks for: 1) image quality integrity (focus, white balance, distance from seafloor); 2) image inventory (all images acquired for each island are included in the analysis); 3) duplicate sites; 4) complete and coherent upload of imagery to CoralNet; and 5) integrity and completeness of robot-generated image annotations..
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