Systematic analysis of rocky shore platform morphology at large spatial scale using LiDAR-derived digital elevation models
Systematic analysis of rocky shore platform morphology at large spatial scale using LiDAR-derived digital elevation models
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使用 LiDAR 衍生的数字高程模型对大空间尺度的岩石海岸平台形态进行系统分析
DOI：
10.1016/j.geomorph.2017.03.011
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发表时间：
2017
期刊：
影响因子：
3.9
通讯作者：
Matsumoto H
中科院分区：
文献类型：
--
作者：
Matsumoto H
现有的许多关于岩石海岸平台的研究描述了从仔细选择的野外地点获得的结果，或者在相对较少的选定地点之间进行比较。在这里，我们描述了一种方法，系统地分析岩石海岸形态在一个大面积使用激光雷达衍生的数字高程模型。该方法被应用到700公里的海岸线在英格兰西南部的一个地区，有相当大的变化，在波浪气候和岩性设置，和一个大的沿岸变化的潮差。在海岸周围每隔50米自动提取跨岸剖面图，海岸航道观测站海岸分类提供了相关信息。开发了自动删除非平台配置文件的工具。其余612个海岸平台剖面，然后进行自动形态分析，并在三个可能的环境控制：波高，平均春季潮差和岩石强度的相关性分析。正如预期的那样，相当大的分散存在于相关性分析，因为只有非常粗略的估计岩石强度和波高，而可变性的因素，如这些可以在当地最重要的控制海岸线形态。有鉴于此，这是有点令人惊讶的是，总体一致性之间发现以前发表的研究结果和系统的，自动分析的激光雷达数据：平台梯度增加岩石强度和潮差增加，但减少波高增加;平台宽度增加波高和潮差增加，但减少岩石强度增加。以前的研究仅用潮差预测海岸平台坡度。激光雷达数据的多元回归分析证实，潮差是最强的预测，但一个新的多因素的经验模型，考虑潮差，波高，岩石强度产生更好的预测海岸平台梯度（预测的均方根误差减少了5%）。这项研究的关键发现是，大规模的半自动形态分析有可能揭示占主导地位的过程控制，在面对小规模的局部变异。
Much of the existing research on rocky shore platforms describes results from carefully selected field sites, or comparisons between a relatively small number of selected sites. Here we describe a method to systematically analyse rocky shore morphology over a large area using LiDAR-derived digital elevation models. The method was applied to 700 km of coastline in southwest England; a region where there is considerable variation in wave climate and lithological settings, and a large alongshore variation in tidal range. Across-shore profiles were automatically extracted at 50 m intervals around the coast where information was available from the Coastal Channel Observatory coastal classification. Routines were developed to automatically remove non-platform profiles. The remaining 612 shore platform profiles were then subject to automated morphometric analyses, and correlation analysis in respect to three possible environmental controls: wave height, mean spring tidal range and rock strength. As expected, considerable scatter exists in the correlation analysis because only very coarse estimates of rock strength and wave height were applied, whereas variability in factors such as these can locally be the most important control on shoreline morphology. In view of this, it is somewhat surprising that overall consistency was found between previous published findings and the results from the systematic, automated analysis of LiDAR data: platform gradient increases as rock strength and tidal range increase, but decreases as wave height increases; platform width increases as wave height and tidal range increase, but decreases as rock strength increases. Previous studies have predicted shore platform gradient using tidal range alone. A multi-regression analysis of LiDAR data confirms that tidal range is the strongest predictor, but a new multi-factor empirical model considering tidal range, wave height, and rock strength yields better predictions of shore platform gradient (root mean square error of predictions reduced by 5%). The key finding of this study is that large-scale semi-automated morphometric analyses have the potential to reveal dominant process controls in the face of small-scale local variability.
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