Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Abstract

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

Facilitation cascades across space: Monitoring estuarine foundation species from satellites to the microscope

“…For example, satellite images (in addition to low accuracy) are often restricted by cloud cover, high tides, and low temporal resolution. On the other hand, drone operation can be hampered by strong winds, rain, higher operational costs, relatively short flight durations, privacy concerns, and airspace restrictions (e.g., around airports, marine mammals, reserves) (Chen & Sasaki, 2021; Nababan et al, 2021). Nevertheless, using drones as a monitoring tool is flexible because the flying altitude (and hence spatial extents and grain) is under direct user control, and it allows for easy sampling of difficult‐to‐reach areas like across channels and deep soft mud.…”

Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets

“…To minimize the potential for misinterpretation, which is occasionally encountered in the conventional manual annotation of multiple LULC classes, we initially applied the ISODATA approach to the QGIS acquired on 12 February 2023, which was not used for model training [25,35]. Based on 224 Field GPS points and very high-resolution Google Earth and Planet Scope Dove images, the ISODATA output image was manually corrected using a raster editor tool in QGIS (QGIS Development Team (2009); Open-Source Geospatial Foundation Project, http://qgis.osgeo.org, accessed on 10 June 2023).…”

“…Numerous studies have demonstrated the superior performance of deep learning models over traditional ML methods in remote sensing applications [22][23][24]. Among state-of-the-art models, the U-Net segmentation model and artificial neural networks (ANNs) have consistently demonstrated superior performance in remote sensing image analysis [25][26][27]. However, limited studies have applied the U-Net and ANN models using different spatial resolution of satellite images for mangrove ecosystem [28][29][30].…”

Quantifying the Intra-Habitat Variation of Seagrass Beds with Unoccupied Aerial Vehicles (UAVs)

“…UAVs have been used to map seagrass and proximal habitats using a variety of techniques underpinned by differing statistical methods. For example, Ventura et al, 2016 [35], and Chen and Sasaki, 2021 [36], used a pixel-based approach whereby each individual photographic pixel is assigned a value or classification representing a habitat type. Alternatively, Ventura et al, 2016 [35], Ventura et al, 2018 [37], Duffy et al, 2018 [33], Nahirnick et al, 2018 [38], Ellis et al, 2020 [39], and Papakonstantinou et al, 2020 [40], used an object-based image analysis (OBIA) approach in which pixels are spatially grouped together in segments based on the similarity of their properties and classified.…”