Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos
Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos
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DOI：
10.3390/rs13234880
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Jundong Chen;J. Sasaki
中科院分区：
文献类型：
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作者：
Jundong Chen;J. Sasaki
海草草甸是全球范围内持续减少的蓝碳生态系统之一。频繁测绘对于监测海草草甸了解变化过程至关重要，包括季节变化以及台风和旋风等气象和海洋事件的影响。这种测绘方法也可以加强海草蓝碳战略和管理实践。尽管无人机（UAV）航空摄影已广泛用于此目的，但在测绘精度、效率和对潮下水草甸的适用性方面存在挑战。为了克服这些挑战，本研究开发了一种新的潮下和潮间带海草草甸制图方法。在日本东京湾的Futtsu潮滩上，使用垂直和倾斜的无人机摄影，创造了四季的地面真实海草正射影。通过调整空间分辨率和归一化参数，并考虑季节输入数据集的组合，首次将特征金字塔网络（FPN）应用于海草自动分类。FPN分类结果的总体准确率（OA）为0.957，精密度为0.895，召回率为0.942，f1评分为0.918，IoU为0.848，优于传统的U-Net分类结果。FPN分类结果突出了海草草甸的季节变化，表现出从冬季到夏季的延伸，从夏季到秋季的下降。在2019年10月19号台风发生后，草甸也出现了恢复，这一现象主要发生在2020年夏季之前。
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.