Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed
Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed
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DOI：
10.3390/rs12203398
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发表时间：
2020-10-01
期刊：
影响因子：
5
通讯作者：
Bas, Tim Le
中科院分区：
文献类型：
--
作者：
Diesing, Markus;Mitchell, Peter J.;Bas, Tim Le
海底及其物种和栖息地正受到各种人类活动的压力。海洋空间规划和自然保护旨在应对这些威胁，但需要足够详细和准确的海底基质和栖息地分布地图。在过去十年中，底栖生境制图作为一门学科有了显著发展，但仍然存在重大挑战。为了测试当前数据产品和分类方法的适用性，我们基于多波束测深仪测深和反向散射数据的通用数据集进行了一项比较研究，并辅以实地观测。任务是预测英国西南大陆架一个高度异质区域内五种基质类别（粗沉积物、混合沉积物、淤泥、沙子和岩石）的空间分布。采用了五种不同的监督分类方法，并使用一组预留样本估计了它们的准确性。我们发现所有方法的总体准确率约为50%。对于岩石基质，误判和漏判误差是可以接受的，但对于所有沉积物类型误差都很高。我们主要将无论采用何种制图方法都存在的地图精度低归因于所选分类系统的不足，该系统需要将逐渐变化的基质类型纳入一个僵化的方案，现有预测因子的判别能力低，以及相对于实地观测设备定位精度而言该地点的空间复杂性高。通过创建一个将各个输出汇总到一张显示主要基质类别及其相关置信度的集合地图，或者采用一种对沉积物组分空间分布进行建模的定量方法，其中一些问题可能会得到缓解。我们得出结论，需要进一步逐步改进遥感和样本数据的收集、处理和分析，以提高地图精度。为了评估底栖生境制图的进展，我们建议创建基准数据集。
The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets.