Improving coral monitoring by reducing variability and bias in cover estimates from seabed images
Improving coral monitoring by reducing variability and bias in cover estimates from seabed images
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通过减少海底图像覆盖估计的变异性和偏差来改善珊瑚监测
DOI：
10.1016/j.pocean.2024.103214
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发表时间：
2024
影响因子：
4.1
通讯作者：
Curtis E
中科院分区：
文献类型：
--
作者：
Curtis E
海底生物覆盖率是评估许多脆弱海洋生态系统状况的既定指标。在从海底图像得出覆盖估计数时，由于用于提取生态数据的注释方法固有的可变性和偏差，在获取生物真实分布方面产生了不确定性。我们研究了两种常见的注释方法估计生物覆盖率的变异性和偏差，以及大小选择性在这种变异性中的作用。11个注释者估计稀疏冷水珊瑚覆盖在相同的96个图像与基于网格和手动分割注释方法。与分割相比，基于网格的方法中注释者之间的标准差大三倍，并且注释者基于网格的估计往往高估珊瑚覆盖。大小选择性偏向手动分割;分割的菌落的最小大小在注释者之间变化五倍。两种建模技术（基于理查德的选择曲线和高斯过程）被用来估算注释者识别的菌落太小而无法分割的区域。通过在分割估计中插补小珊瑚尺寸，注释者之间的变异系数降低了约10%，方法偏差（与参考数据集相比）降低了23%。因此，对于稀疏的低覆盖生物体，建议手动分割图像，以最大限度地减少注释者的可变性和偏差。在使用数据驱动的建模技术对图像中的小生物体进行注释时，通过解决大小选择性偏差，可进一步减少覆盖估计的不确定性。
Seabed cover of organisms is an established metric for assessing the status of many vulnerable marine ecosystems. When deriving cover estimates from seafloor imagery, a source of uncertainty in capturing the true distribution of organisms is introduced by the inherent variability and bias of the annotation method used to extract ecological data. We investigated variability and bias in two common annotation methods for estimating organism cover, and the role of size selectivity in this variability. Eleven annotators estimated sparse cold-water coral cover in the same 96 images with both grid-based and manual segmentation annotation methods. The standard deviation between annotators was three times greater in the grid-based method compared to segmentation, and grid-based estimates from annotators tended to overestimate coral cover. Size selectivity biased the manual segmentation; the minimum size of colonies segmented varied between annotators fivefold. Two modelling techniques (based on Richard’s selection curves and Gaussian processes) were used to impute areas where annotators identified colonies too small for segmentation. By imputing small coral sizes in segmentation estimates, the coefficient of variation between annotators was reduced by approximately 10%, and method bias (compared to a reference dataset) was reduced by up to 23%. Therefore, for sparse, low cover organisms, manual segmentation of images is recommended to minimise annotator variability and bias. Uncertainty in cover estimates may be further reduced by addressing size selectivity bias when annotating small organisms in images using a data-driven modelling technique.
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