Institute of Life and Environmental Sciences, University of Iceland, Reykjavík, Iceland.
PLoS One. 2023 Aug 29;18(8):e0290736. doi: 10.1371/journal.pone.0290736. eCollection 2023.
Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawning habitat in shallow water areas can be distinguished by their visible reflection which makes the use of standard unmanned aerial vehicles (UAV) a viable option for their mapping. Here, we aimed to develop a standardised approach to detect salmonid spawning habitat that is easy and low-cost. We used a semi-automated approach by applying supervised classification techniques to UAV derived RGB imagery from two contrasting lakes in Iceland. For both lakes six endmember classes were obtained with high accuracies. Most importantly, producer's and user's accuracy for classifying spawning redds was >90% after applying post-classification improvements for both study areas. What we are proposing here is an entirely new approach for monitoring spawning habitats which will address some the major shortcomings of the widely used redd count method e.g. collecting and analysing large amounts of data cost and time efficiently, limiting observer bias, and allowing for precise quantification over different temporal and spatial scales.
鲑鱼在胚胎发育阶段特别脆弱，但对其产卵场的监测很少，并且通常依赖于对其巢（红场）的手动计数。然而，这种方法容易出现采样误差，导致红场计数过高或过低。浅水区域的鲑鱼产卵栖息地可以通过其可见的反射来区分，这使得使用标准的无人机 (UAV) 对其进行测绘成为一种可行的选择。在这里，我们旨在开发一种简单且低成本的标准化方法来检测鲑鱼产卵栖息地。我们使用半自动化方法，对来自冰岛两个对比湖泊的无人机衍生的 RGB 图像应用监督分类技术。对于两个湖泊，都获得了具有高精度的六个端元类。最重要的是，在对两个研究区域都应用后分类改进后，对产卵红场进行分类的生产者和使用者的准确性>90%。我们在这里提出的是一种监测产卵栖息地的全新方法，该方法将解决广泛使用的红场计数方法的一些主要缺点，例如高效地收集和分析大量数据，限制观察者偏见，并允许在不同的时间和空间尺度上进行精确量化。
