A deep learning model for measuring coral reef halos globally from multispectral satellite imagery

Franceschini, Simone; Meier, Amelia C.; Suan, Aviv; Stokes, Kaci; Roy, Samapriya; Madin, Elizabeth M.P.

doi:10.1016/j.rse.2023.113584

i

A deep learning model for measuring coral reef halos globally from multispectral satellite imagery

2023
By Franceschini, Simone ; Meier, Amelia C. ; Suan, Aviv ; ...

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Journal Title:

Remote Sensing of Environment
Personal Author:

Franceschini, Simone ; Meier, Amelia C. ; Suan, Aviv ; Stokes, Kaci ; Roy, Samapriya ; Madin, Elizabeth M.P.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CoRIS (Coral Reef Information System)
Description:

Reef halos are rings of bare sand that surround coral reef patches. Halo formation is likely to be the indirectly result of interactions between relatively healthy predator and herbivore populations. To reduce the risk of predation, herbivores preferentially graze close to the safety of the reef, potentially affecting the presence and size of the halo. Reef halos are readily visible in remotely sensed imagery, and monitoring their presence and changes in size may therefore offer clues as to how predator and herbivore populations are faring. However, manually identifying and measuring halos is slow and limits the spatial and temporal scope of studies. There are currently no existing tools to automatically identify single reef halos and measure their size to speed up their identification and improve our ability to quantify their variability over space and time. Here we present a set of convolutional neural networks aimed at identifying and measuring reef halos from very high-resolution satellite imagery (i.e., ∼0.6 m spatial resolution). We show that deep learning algorithms can successfully detect and measure reef halos with a high degree of accuracy (F1 = 0.824), thereby enabling faster, more accurate spatio-temporal monitoring of halo size. This tool will aid in the global study of reef halos, and potentially coral reef ecosystem monitoring, by facilitating our discovery of the ecological dynamics underlying reef halo presence and variability.
Keywords:

Computers In Earth Sciences Geology Soil Science Computers In Earth Sciences Computers In Earth Sciences
Source:

Remote Sensing of Environment, 292, 113584
DOI:

https://doi.org/10.1016/j.rse.2023.113584
ISSN:

0034-4257
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
Funding:

Grant no. NA18OAR4170076
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

urn:sha256:6052589b44b1c317375e43fe984a6cd4d0ea3b2a6a05ffb1592b932dbc8a3dd5
Download URL:

https://repository.library.noaa.gov/view/noaa/59097/noaa_59097_DS1.pdf
File Type:

[PDF - 11.36 MB ]

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