Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone
Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone
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DOI：
10.1016/j.ecss.2017.11.001
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发表时间：
2018-01-05
影响因子：
2.8
通讯作者：
Shutler, Jamie D.
中科院分区：
文献类型：
--
作者：
Duffy, James P.;Pratt, Laura;Shutler, Jamie D.
海草生态系统对环境变化高度敏感。它们也在全球范围内减少，并受到各种人为因素的威胁。现在迫切需要建立强有力的监测方法，以便了解这些敏感的沿海环境中海草丰度和分布的变化。典型的监测方法包括卫星和机载平台遥感、地面生态调查和浮潜/水肺调查。这些技术可能受到时间和空间不一致的影响，或者非常本地化，难以以结构化的方式评估海草草甸。在这里，我们提出了一种新技术，使用轻型（低于7公斤）无人机和消费级相机，在英国威尔士的两个潮间带地点产生非常高的空间分辨率（类似于4毫米像素（-1））马赛克。我们提出了一个完整的数据收集方法，然后选择分类技术来产生每个站点的覆盖估计。我们尝试了三种不同复杂性的分类方法，以调查和说明每种分类方法的不同性能和能力。研究结果表明，无监督分类方法在海草覆盖分类方面优于基于对象的分类方法。我们还发现，研究的两种草甸中，植被越稀疏的草甸分类越准确——与植被越茂密的草甸相比，其观测覆盖度与分类覆盖度之间的均方根偏差（RMSD）更低（9-9.5%）。此外，我们研究了检测其他生物特征的潜力，发现蚓丘可以在较粗的分辨率下检测到，如43毫米像素（-1），而较小的特征，如海草中的蛤壳，需要更细粒度的数据(
Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (similar to 4 mm pixel(-1)) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9-9.5%) compared to a more densely vegetated meadow (RMSD 16-22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel(-1), whereas smaller features such as cockle shells within seagrass require finer grained data (