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Sonar backscatter differentiation of dominant macrohabitat types in a hydrothermal vent field

机译：热液喷口区主要大型栖息地类型的声纳反向散射区分

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Over the past 20 years, sonar remote sensing has opened ways of acquiring new spatial information on seafloor habitat and ecosystem properties. While some researchers are presently working to improve sonar methods so that broad-scale high-definition surveys can be effectively conducted for management purposes, others are trying to use these surveying techniques in more local areas. Because ecosystem management is scale-dependent, there is a need to acquire spatiotemporal knowledge over various scales to bridge the gap between already-acquired point-source data and information available at broader scales. Using a 675-kHz single-pencil-beam sonar mounted on the remotely operated vehicle ROPOS, 2200 m deep on the Juan de Fuca Ridge, East Pacific Rise, five dominant habitat types located in a hydrothermal vent field were identified and characterized by their sonar signatures. The data, collected at different altitudes from I to 10 m above the seafloor, were depth-normalized. We compared three ways of handling the echoes embedded in the backscatters to detect and differentiate the five habitat types; we examined the influence of footprint size on the discrimination capacity of the three methods; and we identified key variables, derived from echoes that characterize each habitat type. The first method used a set of variables describing echo shapes, and the second method used as variables the power intensity values found within the echoes, whereas the last method combined all these variables. Canonical discriminant analysis was used to discriminate among the five habitat types using the three methods. The discriminant models were constructed using 70% of the data while the remaining 30% were used for validation. The results showed that footprints 20-30 cm in diameter included a sufficient amount of spatial variation to make the sonar signatures sensitive to the habitat types, producing on average 82% correct classification. Smaller footprints produced lower percentages of correct classification; instead of the habitat types, the sonar data responded to intrapatch roughness and hardness characteristics. The sonar variables used in this study and the methods for extracting and transforming them are fully described in this paper and available in the public domain.
机译：在过去的20年中，声纳遥感为获取有关海底栖息地和生态系统特性的新空间信息开辟了途径。虽然一些研究人员目前正在努力改善声纳方法，以便可以有效地进行大规模的高清勘测以达到管理目的，但另一些研究人员则试图在更多的地区使用这些勘测技术。由于生态系统管理取决于规模，因此需要获取各种规模的时空知识，以弥合已经获取的点源数据与更广泛的可用信息之间的差距。使用安装在遥控车辆ROPOS上的675 kHz单铅笔束声纳，在东太平洋上升胡安德富卡山脊2200 m深处，确定了位于热液喷口区域的五种主要生境类型，并对其声纳进行了表征。签名。从海平面以上1到10 m的不同高度收集的数据进行了深度归一化。我们比较了三种处理嵌入反向散射的回波的方法，以检测和区分五种栖息地类型。我们研究了足迹尺寸对三种方法的判别能力的影响;我们确定了关键变量，这些变量是从表征每种栖息地类型的回声中得出的。第一种方法使用一组描述回波形状的变量，第二种方法将在回波中找到的功率强度值用作变量，而后一种方法将所有这些变量组合在一起。典型判别分析用于使用三种方法来区分五种栖息地类型。判别模型使用70％的数据构建，而其余30％用于验证。结果表明，直径为20-30 cm的足迹包含足够的空间变化，以使声纳信号对生境类型敏感，平均产生正确分类的82％。较小的脚印产生较低的正确分类百分比;声纳数据不是对生境类型的响应，而是对圈内粗糙度和硬度特性的响应。本研究中使用的声纳变量及其提取和转换方法已在本文中进行了充分描述，并可以在公共领域使用。

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