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Multiscale and Hierarchical Classification for Benthic Habitat Mapping

机译：底栖栖息地制图的多尺度和分级分类

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Developing quantitative and objective approaches to integrate multibeam echosounder (MBES) data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for benthic habitat mapping. The scale of predictors within predictive models directly influences habitat distribution maps, therefore matching the scale of predictors to the scale of environmental drivers is key to improving model accuracy. This study uses a multi-scalar and hierarchical classification approach to improve the accuracy of benthic habitat maps. We used a 700-km 2 region surrounding Cape Otway in Southeast Australia with full MBES data coverage to conduct this study. Additionally, over 180 linear kilometers of towed video data collected in this area were classified using a hierarchical classification approach. Using a machine learning approach, Random Forests, we combined MBES bathymetry, backscatter, towed video and wave exposure to model the distribution of biotic classes at three hierarchical levels. Confusion matrix results indicated that greater numbers of classes within the hierarchy led to lower model accuracy. Broader scale predictors were generally favored across all three hierarchical levels. This study demonstrates the benefits of testing predictor scales across multiple hierarchies for benthic habitat characterization.
机译：开发定量和客观的方法以将多波束回声测深（MBES）数据与地面观测资料集成在一起以进行预测建模，这对于确保可重复性和为底栖生境制图提供置信度至关重要。预测模型中预测因子的规模直接影响栖息地分布图，因此，将预测因子的规模与环境驱动因素的规模相匹配是提高模型准确性的关键。这项研究使用多标量和分层分类方法来提高底栖生境图的准确性。我们使用了澳大利亚东南部Cape Otway周围700 km 2的区域，并具有完整的MBES数据覆盖范围来进行这项研究。此外，使用分层分类方法对在该地区收集的超过180线性公里的拖曳视频数据进行了分类。通过使用机器学习方法，Random Forests，我们结合了MBES测深法，反向散射，拖曳视频和波浪暴露来模拟生物等级在三个层次上的分布。混淆矩阵结果表明，层次结构中类的数量过多导致模型准确性降低。通常，在所有三个层次级别中都使用范围更广的预测指标。这项研究证明了在多个层次上测试预测因子规模对底栖生境特征的好处。

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