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

PeerJ

article

Satoru Tahara¹  Kenji Sudo²  Takehisa Yamakita⁴  Masahiro Nakaoka² 

[1] Graduate School of Environmental Science, Hokkaido University;Akkeshi Marine Station, Field Science Center for Northern Biosphere, Hokkaido University;Japan Fisheries Research and Education Agency, Fisheries Technology Institute;Marine Biodiversity and Environmental Assessment Research Center ,(BioEnv), Research Institute for Global Change ,(RIGC), Japan Agency for Marine Earth Science and Technology

关键词: Remote sensing;    Spatial mapping;    Species identification;    Drone;    Deep neural network;    Zostera marina;    Zostera japonica;    Accuracy assessment;    Hokkaido;    Japan;   

DOI  :  10.7717/peerj.14017

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY