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Deep learning for coastal resource conservation: automating detection of shellfish reefs

Publication ,  Journal Article
Ridge, JT; Gray, PC; Windle, AE; Johnston, DW
Published in: Remote Sensing in Ecology and Conservation
December 1, 2020

It is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate-driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely sensed data to capture complex spatial habitat patterns. To help managers and researchers avoid inefficient traditional mapping efforts, we developed a deep learning tool (OysterNet) that uses unoccupied aircraft systems (UAS) imagery to automatically detect and delineate oyster reefs, an ecosystem that has proven problematic to monitor remotely. OysterNet is a convolutional neural network (CNN) that assesses intertidal oyster reef extent, yielding a difference in total area between manual and automated delineations of just 8%, attributable in part to OysterNet's ability to detect oysters overlooked during manual demarcation. Further training of OysterNet could enable assessments of oyster reef heights and densities, and incorporation of more coastal habitat types. Future iterations will be applied to high-resolution satellite data for effective management at larger scales.

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Published In

Remote Sensing in Ecology and Conservation

DOI

EISSN

2056-3485

Publication Date

December 1, 2020

Volume

6

Issue

4

Start / End Page

431 / 440

Related Subject Headings

  • 4104 Environmental management
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
 

Citation

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Ridge, J. T., Gray, P. C., Windle, A. E., & Johnston, D. W. (2020). Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sensing in Ecology and Conservation, 6(4), 431–440. https://doi.org/10.1002/rse2.134
Ridge, J. T., P. C. Gray, A. E. Windle, and D. W. Johnston. “Deep learning for coastal resource conservation: automating detection of shellfish reefs.” Remote Sensing in Ecology and Conservation 6, no. 4 (December 1, 2020): 431–40. https://doi.org/10.1002/rse2.134.
Ridge JT, Gray PC, Windle AE, Johnston DW. Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sensing in Ecology and Conservation. 2020 Dec 1;6(4):431–40.
Ridge, J. T., et al. “Deep learning for coastal resource conservation: automating detection of shellfish reefs.” Remote Sensing in Ecology and Conservation, vol. 6, no. 4, Dec. 2020, pp. 431–40. Scopus, doi:10.1002/rse2.134.
Ridge JT, Gray PC, Windle AE, Johnston DW. Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sensing in Ecology and Conservation. 2020 Dec 1;6(4):431–440.

Published In

Remote Sensing in Ecology and Conservation

DOI

EISSN

2056-3485

Publication Date

December 1, 2020

Volume

6

Issue

4

Start / End Page

431 / 440

Related Subject Headings

  • 4104 Environmental management
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management