Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments
Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments
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DOI：
10.3390/rs11060704
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Ben Mcilwaine;M. Casado;P. Leinster
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作者：
Ben Mcilwaine;M. Casado;P. Leinster
大型藻华 (MAB) 是一种全球性自然灾害，随着气候变化和农业径流的增加，其发生率可能会增加。人与生物圈可能会给本地物种、养鱼场、核电站和旅游活动带来重大问题。该项目重点研究人与生物圈计划对英国核电站运行的影响。然而，产出和调查结果也与具有类似问题的其他沿海运营商相关。通过为人与生物圈提供预警检测系统，应该可以最大限度地减少破坏性影响，并有可能完全避免它们。目前基于卫星图像的方法无法用于检测不同水深的低密度移动植被。这项工作是提供一个系统的第一步，该系统可以在海洋侵入事件发生前 6-8 小时向沿海运营商发出警告。这种预警系统的一个基本组成部分是有问题的大型藻类物种的光谱反射特性。这对于优化海洋环境中存在问题的大型藻类的检测能力是必要的。我们测量了在发电站附近采样的八种大型藻类的反射特征。基于当前的方法，仅分析低于 900 nm 的波长（相似性百分比为 700 nm (SIMPER)）。然后我们得出了这八个采样物种的一阶导数光谱。使用多方面的单变量和多变量方法来可视化光谱反射率，并且相似性分析 (ANOSIM) 为所有可能的成对比较提供了 85% 的物种水平辨别率。 SIMPER 分析用于检测始终有助于同时区分所有八种大型藻类样本的群体水平（535-570 nm）和物种水平（570-590 nm）的波段。使用固定翼无人机（UAV）确认采样位置，收集的图像用于通过标准摄影测量过程生成单个正交图像。先前发现对群体水平歧视有一致贡献的波段与光合色素沉着有关，而物种水平歧视波段则不具有这种关联。这表明光合色素的光谱多样性不足以成功区分所有八个物种。我们建议未来的工作应该使用上面突出显示的波段来研究基于电荷耦合器件 (CCD) 的传感器。这将有助于开发使用无人机的区域规模预警 MAB 检测系统，并有助于为最佳传感器滤波器选择提供信息。
Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection.