http://lod.bco-dmo.org/id/dataset/932211
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2024-07-11
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Manually annotated reef halos based on sattelite imagery from 6 study areas as training and test data for a deep learning model
2024-12-18
publication
2024-12-18
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2024-12-18
publication
https://doi.org/10.26008/1912/bco-dmo.932211.1
Elizabeth Madin
University of Hawaiʻi at Mānoa
principalInvestigator
Simone Franceschini
University of Hawaiʻi at Mānoa
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Madin, E., Franceschini, S. (2024) Manually annotated reef halos based on sattelite imagery from 6 study areas as training and test data for a deep learning model. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-12-18 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.932211.1 [access date]
Manually annotated reef halos Dataset Description: Methods and Sampling: <p>Our study area included 20 areas of interest) from 6 countries.</p>
<p>SkySat satellite images were acquired through Planet Inc. Planet Explorer Catalogue. Obtained as a SkySat Collect product, each image was roughly 20km x 5.9km, with a spatial resolution ranging between 0.5m and 0.8m. Before download, all images were orthorectified, radiometrically calibrated, and atmospherically corrected to surface reflectance following Planet’s standard procedures. All surface reflectance products are orthorectified using fine digital elevation maps (30-90m posting) and ground control points. Planet conducts atmospheric correction with the 6SV2.1 radiative transfer model which accounts for atmospheric absorption and scattering, with aerosol optical depth, water, vapor and ozone inputs from MODIS near-real-time data (MOD09CMA and MOD09CMG). All calculations were done in R. Satellite images cover a time interval from March 2019 to June 2021. The satellite images can not be shared due to file size issues and data-sharing policies, below are the Skysat unique identification numbers for each satellite image used in the project:</p>
<ul>
<li>20201107_025354_ssc9_u0001</li>
<li>20190510_152255_ssc4_u0003</li>
<li>20190625_182659_ssc7_u0001</li>
<li>20190309_183114_ssc11_u0001</li>
<li>20190531_182931_ssc6_u0001</li>
<li>20200919_182915_ssc7_u0002</li>
<li>20190626_182402_ssc8_u0003</li>
<li>20201129_191646_ssc8_u0001</li>
<li>20201022_191415_ssc6_u0001</li>
<li>20201022_191415_ssc6_u0002</li>
<li>20200626_192104_ssc6_u0001</li>
<li>20200626_192104_ssc6_u0002</li>
<li>20200627_190037_ssc7_u0001</li>
<li>20200627_191435_ssc8_u0001</li>
<li>20190413_081243_ssc13d3_0004</li>
<li>20190413_081243_ssc13d3_0005</li>
<li>20190413_081243_ssc13d3_0006</li>
<li>20200202_110140_ssc7_u0001</li>
<li>20210627_034355_ssc19_u0001</li>
<li>20210127_154738_ssc12_u0001</li>
</ul>
<p>&nbsp;</p>
<p>All non-overlapping halos in the AOIs were labeled, resulting in 4,127 manually annotated halos. Halos were labeled using ArcGIS software (ver. 2.9.1), allowing the geo-referenced information for all objects to be retained. To avoid biases due to a single user's perception of halos size, five users were trained to the same labeling procedure – zooming into each halo at a 1:600 scale and tracing light contours with a mouse – and labeled the same AOIs. The dataset generated from the imagery annotation was divided into training and test sets (~70% and ~30%, respectively). The training set was used for model implementation and optimization, while the test set was used for comparing model-predicted vs. manually annotated halos. In addition, we selected independent areas (i.e., AOIs) where no halos were used for the training process) for the test set to estimate the model generalization properties better.</p>
<p>Simone Franceschini and Amelia C. Meier downloaded the satellite images used for this project. Halos data were labeled by Simone Franceschini, Amelia C. Meier, Aviv Suan, Kaci Stokes, and Elizabeth M.P. Madin. Simone Franceschini developed the model and estimated performance metrics.</p>
<p>&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1941737 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1941737
completed
Elizabeth Madin
University of Hawaiʻi at Mānoa
8084399506
2440 Campus Road, Box 368
HONOLULU
HI
968222234
US
emadin@hawaii.edu
pointOfContact
Simone Franceschini
University of Hawaiʻi at Mānoa
Hawai'i Institute of Marine Biology
Kane'ohe
HI
96744
USA
simonefr@hawaii.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
AOI
Object_Id
SkySate_image_ID
Classname
Mean_Latitude
Mean_Longitude
Subset
theme
None, User defined
region
object ID
file_name
sample description
latitude
longitude
featureType
BCO-DMO Standard Parameters
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
CAREER: Decoding seascape-scale vegetation patterns on coral reefs to understand ecosystem health: Integrating research and education from organismal to planetary scales
http://oceansphere.org
CAREER: Decoding seascape-scale vegetation patterns on coral reefs to understand ecosystem health: Integrating research and education from organismal to planetary scales
<p><em>NSF Award Abstract:</em><br />
Coral reefs worldwide are under increasing threat from a range of human-induced stressors. Climate change is understood to be a key global stressor threatening reefs, but the only proven levers for ecosystem managers to increase reef resilience is to mitigate local and regional stressors such as fishing pressure. A vexing question persists, however, which is how to measure the effects of fishing on ecosystems, particularly over the large spatial (e.g., >10s of meters) and temporal (multi-year) scales over which fishing occurs. One promising approach to doing so is using the large-scale vegetation patterns found on coral reefs globally, called “halos”, to remotely observe when, where, and to what extent fishing pressure is affecting community structure and function. This program combines lab- and field-based experiments with cutting-edge satellite imaging technology and computer science approaches to provide a leap forward in our understanding of how species-level interactions can scale up in space and time to shape coral reef seascapes around the world. By drawing on these approaches, the synergistic education program: 1) integrates science and art (i.e., murals and satellite imagery) to educate and inspire Hawai‘i’s students and general public about coral reef ecology; 2) builds technological capacity in Hawai‘i’s underrepresented minority high school to graduate students, and 3) empowers these students with science communication skills to communicate with diverse audiences. By leveraging this research program and the cutting-edge technologies it will involve, the investigator establishes a strong foundation for long-term teaching and mentoring activities focused on increasing capacity within STEM-underrepresented minorities with Hawaiian and other Pacific Islander backgrounds. Decoding what coral reef halos can tell us about the effects of fishing on reef ecosystem health provides valuable knowledge to reef ecosystem managers and conservation practitioners as reefs continue to rapidly change due to human stressors.</p>
<p>This project combines lab- and field-based experiments with cutting-edge satellite imaging technology and computer science approaches to address the goals of: 1) determining the mechanisms that create the “halos” that form around coral patch reefs, and 2) testing the predictions arising from these mechanisms in a global arena. This project uses a transdisciplinary approach – spanning ecology, oceanography, geospatial science, and computer science – to address these goals. This program has three scientific objectives: to determine 1) which species interaction mechanisms and environmental factors cause reef halos and what their relative importance is; 2) whether these mechanisms are globally consistent or vary geographically; and 3) whether halos can therefore be used as an indicator of aspects of coral reef ecosystem health. In the process, this research advances our understanding of how remote observation tools (satellite and drone imagery; camera traps) can be integrated with computer science (machine learning) and ecological approaches (mechanistic experiments) to generate emergent insights that would not otherwise be possible.</p>
<p>This project is jointly funded by the Biological Oceanography Program, the Established Program to Stimulate Competitive Research (EPSCoR), and Ocean Education Programs.</p>
<p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p>
Coral Reef Halos
largerWorkCitation
project
eng; USA
oceans
-87.87905306
151.9375651
-23.47546301
27.74960245
2024-12-18
Hawai’i (field components) and global (synthetic components)
0
BCO-DMO catalogue of parameters from Manually annotated reef halos based on sattelite imagery from 6 study areas as training and test data for a deep learning model
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/943764.rdf
Name: AOI
Units: unitless
Description: <p>Area of interest. Twenty areas in total. Country codes: AUS = Australia, BHS = Bahamas, BLZ = Belize, EGY = Egypt, SAU = Saudi Arabia, USA = United States of America (Florida)</p>
http://lod.bco-dmo.org/id/dataset-parameter/943765.rdf
Name: Object_Id
Units: unitless
Description: <p>ID of each specific object within each area of interest</p>
http://lod.bco-dmo.org/id/dataset-parameter/943766.rdf
Name: SkySate_image_ID
Units: unitless
Description: <p>Name of image used for manual annotation</p>
http://lod.bco-dmo.org/id/dataset-parameter/943767.rdf
Name: Classname
Units: unitless
Description: <p>Class of annotation: Halo</p>
http://lod.bco-dmo.org/id/dataset-parameter/943770.rdf
Name: Mean_Latitude
Units: decimal degrees
Description: <p>Mean latitude of halo object (center)</p>
http://lod.bco-dmo.org/id/dataset-parameter/943771.rdf
Name: Mean_Longitude
Units: decimal degrees
Description: <p>Mean longitude of halo object (center)</p>
http://lod.bco-dmo.org/id/dataset-parameter/943772.rdf
Name: Subset
Units: unitless
Description: <p>Indication if halo was used for as training or test data</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
362918
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/f14fc226-1046-4136-8b1b-92cf798d7a4b/content
download
https://doi.org/10.26008/1912/bco-dmo.932211.1
download
onLine
dataset
<p>Our study area included 20 areas of interest) from 6 countries.</p>
<p>SkySat satellite images were acquired through Planet Inc. Planet Explorer Catalogue. Obtained as a SkySat Collect product, each image was roughly 20km x 5.9km, with a spatial resolution ranging between 0.5m and 0.8m. Before download, all images were orthorectified, radiometrically calibrated, and atmospherically corrected to surface reflectance following Planet’s standard procedures. All surface reflectance products are orthorectified using fine digital elevation maps (30-90m posting) and ground control points. Planet conducts atmospheric correction with the 6SV2.1 radiative transfer model which accounts for atmospheric absorption and scattering, with aerosol optical depth, water, vapor and ozone inputs from MODIS near-real-time data (MOD09CMA and MOD09CMG). All calculations were done in R. Satellite images cover a time interval from March 2019 to June 2021. The satellite images can not be shared due to file size issues and data-sharing policies, below are the Skysat unique identification numbers for each satellite image used in the project:</p>
<ul>
<li>20201107_025354_ssc9_u0001</li>
<li>20190510_152255_ssc4_u0003</li>
<li>20190625_182659_ssc7_u0001</li>
<li>20190309_183114_ssc11_u0001</li>
<li>20190531_182931_ssc6_u0001</li>
<li>20200919_182915_ssc7_u0002</li>
<li>20190626_182402_ssc8_u0003</li>
<li>20201129_191646_ssc8_u0001</li>
<li>20201022_191415_ssc6_u0001</li>
<li>20201022_191415_ssc6_u0002</li>
<li>20200626_192104_ssc6_u0001</li>
<li>20200626_192104_ssc6_u0002</li>
<li>20200627_190037_ssc7_u0001</li>
<li>20200627_191435_ssc8_u0001</li>
<li>20190413_081243_ssc13d3_0004</li>
<li>20190413_081243_ssc13d3_0005</li>
<li>20190413_081243_ssc13d3_0006</li>
<li>20200202_110140_ssc7_u0001</li>
<li>20210627_034355_ssc19_u0001</li>
<li>20210127_154738_ssc12_u0001</li>
</ul>
<p>&nbsp;</p>
<p>All non-overlapping halos in the AOIs were labeled, resulting in 4,127 manually annotated halos. Halos were labeled using ArcGIS software (ver. 2.9.1), allowing the geo-referenced information for all objects to be retained. To avoid biases due to a single user's perception of halos size, five users were trained to the same labeling procedure – zooming into each halo at a 1:600 scale and tracing light contours with a mouse – and labeled the same AOIs. The dataset generated from the imagery annotation was divided into training and test sets (~70% and ~30%, respectively). The training set was used for model implementation and optimization, while the test set was used for comparing model-predicted vs. manually annotated halos. In addition, we selected independent areas (i.e., AOIs) where no halos were used for the training process) for the test set to estimate the model generalization properties better.</p>
<p>Simone Franceschini and Amelia C. Meier downloaded the satellite images used for this project. Halos data were labeled by Simone Franceschini, Amelia C. Meier, Aviv Suan, Kaci Stokes, and Elizabeth M.P. Madin. Simone Franceschini developed the model and estimated performance metrics.</p>
<p>&nbsp;</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact