Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring
Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring
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DOI：
10.3390/rs12101572
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发表时间：
2020-05
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通讯作者：
America Zelada Leon;V. Huvenne;N. Benoist;M. Ferguson;B. Bett;R. Wynn
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作者：
America Zelada Leon;V. Huvenne;N. Benoist;M. Ferguson;B. Bett;R. Wynn
由于众多国家目标的目标是到 2030 年保护多达 30% 的水域，全球海洋保护区的数量和面积正在迅速增加。自动海底分类算法正在兴起，作为生成海底栖息地地图以监测这些区域的更快、更客观的方法。然而，还没有研究系统地比较它们的重复性。在这里，我们的目标是通过比较使用三种自动海底分类算法连续几天收集的声学数据集得出的地图的可重复性来解决这个问题：（1）随机森林（RF），（2）K最近邻（KNN）和（3）K均值（KMEANS）。然后使用最稳健和可重复的方法来评估 2012 年至 2015 年英国凯尔特海大黑格弗拉斯海洋保护区内海底栖息地的变化。我们的结果表明，只有 RF 和 KNN 提供统计上可重复的地图，连续几天之间的一致性为 60.3% 和 47.2%。此外，这项研究表明，在低地势区域，测深导数是非必要的输入参数，而反向散射纹理特征，特别是灰度共生矩阵，在检测不同栖息地时要有效得多。 2012年至2015年间，测试区域的栖息地持续率为48.8%，其中38.2%的区域因栖息地的交换而发生变化。总的来说，这项研究强调了在自动化海底分类方法充分用于海底栖息地监测之前研究其可重复性的重要性。
The number and areal extent of marine protected areas worldwide is rapidly increasing as a result of numerous national targets that aim to see up to 30% of their waters protected by 2030. Automated seabed classification algorithms are arising as faster and objective methods to generate benthic habitat maps to monitor these areas. However, no study has yet systematically compared their repeatability. Here we aim to address that problem by comparing the repeatability of maps derived from acoustic datasets collected on consecutive days using three automated seafloor classification algorithms: (1) Random Forest (RF), (2) K–Nearest Neighbour (KNN) and (3) K means (KMEANS). The most robust and repeatable approach is then used to evaluate the change in seafloor habitats between 2012 and 2015 within the Greater Haig Fras Marine Conservation Zone, Celtic Sea, UK. Our results demonstrate that only RF and KNN provide statistically repeatable maps, with 60.3% and 47.2% agreement between consecutive days. Additionally, this study suggests that in low-relief areas, bathymetric derivatives are non-essential input parameters, while backscatter textural features, in particular Grey Level Co-occurrence Matrices, are substantially more effective in the detection of different habitats. Habitat persistence in the test area between 2012 and 2015 was 48.8%, with swapping of habitats driving the changes in 38.2% of the area. Overall, this study highlights the importance of investigating the repeatability of automated seafloor classification methods before they can be fully used in the monitoring of benthic habitats.