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Integrating Multibeam Backscatter Angular Response Mosaic and Bathymetry Data for Benthic Habitat Mapping

机译:整合多光束后向散射角响应镶嵌和测深数据以进行底栖生境制图

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摘要

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.
机译:多波束回声测深仪(MBES)越来越成为海洋栖息地制图应用程序的首选工具。反过来,栖息地制图研究的迅速扩展导致需要一种自动分类技术,以有效地绘制底栖生境,评估模型输出的可信度以及评估驱动观测模式的变量的重要性。底栖生境的表征过程通常涉及对MBES水深,背向散射镶嵌或角度响应的分析,其中观测数据可提供地面真实情况。但是,在单个分类过程中利用MBES输出的全部范围的研究是有限的。我们提出了一种方法,该方法在使用随机森林(RF)机器学习算法预测底栖生物栖息地分布的分类过程中,将反向散射角响应与MBES测深法,反向散射镶嵌及其衍生物进行了集成。该方法包括一种从反向散射角响应曲线导出统计特征的方法,该曲线特征是根据在反向散射镶嵌图的同质区域内整理的MBES数据创建的。使用RF算法,我们评估每个变量的相对重要性,以优化分类过程并简化所应用的模型。结果表明,在分类过程中包括角度响应特征,将最终生境图的准确性从88.5%提高到93.6%。 RF算法将测深法和角响应平均值确定为两个最重要的预测指标。但是,只有结合了从测深法和反向散射镶嵌获得的其他特征后,才能获得最高的分类率。与反向散射镶嵌特征相比,发现角响应特征对分类过程更为重要。该分析表明,将角响应信息与测深法和后向散射镶嵌及其派生词相结合,构成了研究底栖生境分布的重要改进,这对于有效的海洋空间规划和资源管理是必不可少的。

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