Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning
Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning
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DOI：
10.1029/2012wr012223
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发表时间：
2012-11-14
影响因子：
5.4
通讯作者：
Rychkov, I.
中科院分区：
文献类型：
--
作者：
Brasington, J.;Vericat, D.;Rychkov, I.
最近的技术进步使地形数据的获取发生了革命性的变化，为地球表面的结构和形态提供了新的视角。这些发展对河流科学的实践产生了深远的影响，使河流地形模型的维度、分辨率和精度发生了阶段性的变化。“超尺度”测量方法的出现，包括结构运动摄影测量和地面激光扫描(TLS)，现在提供了获取三维点云数据的机会，这些数据可以捕获范围内的颗粒尺度细节。然而，将这些数据转换成与地貌相关的产品并不是一帆风顺的。与传统的调查方法不同，TLS可以快速、自动、非选择性地获取观测值。这导致了与植被和其他伪影的后向散射相关的相当大的“噪声”。此外，大数据量很难可视化；需要非常高的容量存储；并且不容易纳入地理信息系统和模拟模型。在这篇文章中，我们分析了多尺度地形模型的地貌完整性，这些模型是由苏格兰费希河辫状河的TLS调查绘制的。这些栅格地形模型是使用一种新的、计算高效的地理空间工具包生成的：地形点云分析工具包(TOPCAT)。这会将点云数据智能地抽取到一组2.5维地形模型中，这些模型保留了高频次网格地形的信息，作为局部去趋势高程分布的矩。这些结果量化了传统河流DEM固有的地形概化程度，并说明了如何使用亚网格地形统计来绘制河段尺度上的粒度、颗粒粗糙度和沉积相的空间模式。
Recent advances in technology have revolutionized the acquisition of topographic data, offering new perspectives on the structure and morphology of the Earth's surface. These developments have had a profound impact on the practice of river science, creating a step change in the dimensionality, resolution, and precision of fluvial terrain models. The emergence of "hyperscale" survey methods, including structure from motion photogrammetry and terrestrial laser scanning (TLS), now presents the opportunity to acquire 3-D point cloud data that capture grain-scale detail over reach-scale extents. Translating these data into geomorphologically relevant products is, however, not straightforward. Unlike traditional survey methods, TLS acquires observations rapidly and automatically, but unselectively. This results in considerable "noise" associated with backscatter from vegetation and other artifacts. Moreover, the large data volumes are difficult to visualize; require very high capacity storage; and are not incorporated readily into GIS and simulation models. In this paper we analyze the geomorphological integrity of multiscale terrain models rendered from a TLS survey of the braided River Feshie, Scotland. These raster terrain models are generated using a new, computationally efficient geospatial toolkit: the topographic point cloud analysis toolkit (ToPCAT). This performs an intelligent decimation of point cloud data into a set of 2.5-D terrain models that retain information on the high-frequency subgrid topography, as the moments of the locally detrended elevation distribution. The results quantify the degree of terrain generalization inherent in conventional fluvial DEMs and illustrate how subgrid topographic statistics can be used to map the spatial pattern of particle size, grain roughness, and sedimentary facies at the reach scale.