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首页> 外文期刊>Geosciences >New Feature Classes for Acoustic Habitat Mapping—A Multibeam Echosounder Point Cloud Analysis for Mapping Submerged Aquatic Vegetation (SAV)
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New Feature Classes for Acoustic Habitat Mapping—A Multibeam Echosounder Point Cloud Analysis for Mapping Submerged Aquatic Vegetation (SAV)

机译：声栖息地制图的新要素类-淹没水生植物（SAV）测绘的多波束回声点云分析

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A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1–8 m water depth with a spatial resolution in the decimeter scale. The survey area was known to be populated with the seagrass Zostera marina and the bathymetric soundings were highly influenced by this habitat. The depth values often coincide with the canopy of the seagrass. Instead of classifying the data with a digital terrain model and the given derivatives, we derive predictive features from the native point cloud of the MBES soundings in a similar way to terrestrial LiDAR data analysis. We calculated the eigenvalues to derive nine characteristic features, which include linearity, planarity, and sphericity. The features were calculated for each sounding within a cylindrical neighborhood of 0.5 m radius and holding 88 neighboring soundings, on average, during our survey. The occurrence of seagrass was ground-truthed by divers and aerial photography. A data model was constructed and we applied a random forest machine learning supervised classification to predict between the two cases of “seafloor” and “vegetation”. Prediction by linearity, planarity, and sphericity resulted in 88.5% prediction accuracy. After constructing the higher-order eigenvalue derivatives and having the nine features available, the model resulted in 96% prediction accuracy. This study outlines for the first time that valuable feature classes can be derived from MBES point clouds—an approach that could substantially improve bathymetric measurements and habitat mapping.
机译：提出了一种新的多波束回声（MBES）数据分析方法，旨在改善栖息地图，尤其是考虑到水下水生植被（SAV）时。 MBES数据是在1-8 m的水深中以400 kHz的频率获得的，其空间分辨率为分米标度。众所周知，调查区的海草Zostera滨海人口众多，且深海测深法受到该生境的强烈影响。深度值通常与海草的顶盖重合。我们没有使用数字地形模型和给定的导数对数据进行分类，而是通过与地面LiDAR数据分析类似的方式从MBES测深的本机点云中获得了预测特征。我们计算了特征值以得出9个特征，包括线性，平面度和球形度。在我们的调查过程中，针对半径为0.5 m的圆柱邻域内的每个测深计算了特征，并平均容纳88个相邻测深。海草的发生是由潜水员和航拍照片证实的。构建了数据模型，我们应用了随机森林机器学习监督分类来预测“海底”和“植被”这两种情况之间的情况。通过线性，平面度和球形度进行的预测可得出88.5％的预测精度。构建高阶特征值导数并具有九个可用特征后，该模型的预测精度为96％。这项研究首次概述了可以从MBES点云中获得有价值的要素类的方法，该方法可以大大改善测深和生境图。

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