Tahara, et al 2022. Species Level Mapping of a Seagrass Bed Using an Unmanned Aerial Vehicle and Deep Learning Technique

Tahara, Satoru, Kenji Sudo, Takehisa Yamakita, and Masahiro Nakaoka. 2022. “Species Level Mapping of a Seagrass Bed Using an Unmanned Aerial Vehicle and Deep Learning Technique.” PeerJ 10:e14017. doi: 10.7717/peerj.14017.


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.


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).


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.


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.

Figure 1: Methodology workflow of this study.

Parallelograms, rectangles and arrows represent input/output data, data processes and data flows, respectively.