Statistically assessing vertical change on a sandy beach from permanent laser scanning time series

Mieke Kuschnerus , Roderik Lindenbergh , Sander Vos , Ramon Hanssen
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Abstract

In the view of climate change, understanding and managing effects on coastal areas and adjacent cities is essential. Permanent Laser Scanning (PLS) is a successful technique to not only observe notably sandy coasts incidentally or once every year, but (nearly) continuously over extended periods of time. The collected point cloud observations form a 4D point cloud data set representing the evolution of the coast provide the opportunity to assess change processes at high level of detail. For an exemplary location in Noordwijk, The Netherlands, three years of hourly point clouds were acquired on a 1 km long section of a typical Dutch urban sandy beach. Often, the so-called level of detection is used to assess point cloud differences from two epochs. To explicitly incorporate the temporal dimension of the height estimates from the point cloud data set, we revisit statistical testing theory. We apply multiple hypothesis testing on elevation time series in order to identify different coastal processes, like aeolian sand transport or bulldozer works. We then estimate the minimal detectable bias for different alternative hypotheses, to quantify the minimal elevation change that can be estimated from the PLS observations over a certain period of time. Additionally, we analyse potential error sources and influences on the elevation estimations and provide orders of magnitudes and possible ways to deal with them. Finally we conclude that elevation time series from a long term PLS data set are a suitable input to identify aeolian sand transport with the help of multiple hypothesis testing. In our example case, slopes of 0.032 m/day and sudden changes of 0.031 m can be identified with statistical power of 80% and with 95% significance in 24-h time series on the upper beach. In the intertidal area the presented method allows to classify daily elevation time series over one month according to the dominating model (sudden change or linear trend) in either eroding or accreting behaviour.

Abstract Image

根据永久激光扫描时间序列对沙滩的垂直变化进行统计评估
鉴于气候变化,了解和管理对沿海地区和邻近城市的影响至关重要。永久性激光扫描(PLS)是一种成功的技术,不仅可以偶然或每年一次观测显著的沙质海岸,还可以(几乎)长时间连续观测。收集到的点云观测数据形成了代表海岸演变过程的 4D 点云数据集,为评估变化过程的细节提供了机会。在荷兰 Noordwijk 的一个示例地点,对典型的荷兰城市沙滩上 1 公里长的部分采集了三年的每小时点云。所谓的检测水平通常用于评估两个时间点的点云差异。为了明确纳入点云数据集高度估算的时间维度,我们重新审视了统计检验理论。我们对海拔高度时间序列进行多重假设检验,以识别不同的沿岸过程,如风沙运移或推土机工程。然后,我们估算不同替代假设的最小可检测偏差,以量化在一定时期内可从 PLS 观测中估算出的最小海拔变化。此外,我们还分析了海拔估算的潜在误差来源和影响因素,并提供了误差大小顺序和可能的处理方法。最后,我们得出结论,来自长期 PLS 数据集的海拔时间序列是借助多重假设检验识别风沙迁移的合适输入。在我们的示例中,在上部海滩的 24 小时时间序列中,每天 0.032 米的斜率和 0.031 米的突变可以被识别,统计功率为 80%,显著性为 95%。在潮间带地区,所提出的方法可以根据侵蚀或增生行为的主要模式(突变或线性趋势)对一个月内的日海拔时间序列进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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