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Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique

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Title: Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique
Authors: Tahara, Satoru Browse this author
Sudo, Kenji Browse this author
Yamakita, Takehisa Browse this author →KAKEN DB
Nakaoka, Masahiro Browse this author →KAKEN DB
Keywords: Remote sensing
Spatial mapping
Species identification
Deep neural network
Zostera marina
Zostera japonica
Accuracy assessment
Issue Date: 17-Oct-2022
Publisher: PeerJ
Journal Title: Peerj
Volume: 10
Start Page: e14017
Publisher DOI: 10.7717/peerj.14017
Abstract: Background: Seagrass beds are essential habitats in coastal ecosystems, providing valuable ecosystem services, but are threatened by various climate change and human activities. Seagrass monitoring by remote sensing have been conducted over past decades using satellite and aerial images, which have low resolution to analyze changes in the composition of different seagrass species in the meadows. Recently, unmanned aerial vehicles (UAVs) have allowed us to obtain much higher resolution images, which is promising in observing fine-scale changes in seagrass species composition. Furthermore, image processing techniques based on deep learning can be applied to the discrimination of seagrass species that were difficult based only on color variation. In this study, we conducted mapping of a multispecific seagrass bed in Saroma-ko Lagoon, Hokkaido, Japan, and compared the accuracy of the three discrimination methods of seagrass bed areas and species composition, i.e., pixel-based classification, object-based classification, and the application of deep neural network.Methods: We set five benthic classes, two seagrass species (Zostera marina and Z. japonica), brown and green macroalgae, and no vegetation for creating a benthic cover map. High-resolution images by UAV photography enabled us to produce a map at fine scales (<1 cm resolution).Results: The application of a deep neural network successfully classified the two seagrass species. The accuracy of seagrass bed classification was the highest (82%) when the deep neural network was applied.Conclusion: Our results highlighted that a combination of UAV mapping and deep learning could help monitor the spatial extent of seagrass beds and classify their species composition at very fine scales.
Type: article
Appears in Collections:北方生物圏フィールド科学センター (Field Science Center for Northern Biosphere) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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