Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA
Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA
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DOI：
10.1016/j.catena.2006.10.010
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发表时间：
2007-09
期刊：
影响因子：
6.2
通讯作者：
L. Allan James;Darrell Glen Watson;William F. Hansen
中科院分区：
文献类型：
--
作者：
L. Allan James;Darrell Glen Watson;William F. Hansen
美国东南部的皮埃蒙特地区在世纪早期曾遭受过严重的沟壑侵蚀。在许多地区重新造林所形成的茂密的树冠层现在妨碍了通过航空摄影或其他传统遥感方法来识别或绘制沟壑图。2004年4月，美国林务局的一项机载激光扫描（ALS或LiDAR）测绘使命获得了裸露地球的地形数据。本文测试的能力ALS地形数据，以确定水源渠道和冲沟的两个分支沟系统在森林地区，并提取沟的形态信息。比较使用差分GPS和水准测量测量的参考横截面与现场导线。在沟道网络尺度上，激光雷达数据提供了准确的地图-最好的可用地图-除了狭窄或平行且间隔紧密的小沟道之外，还可以对小沟道进行稳健的检测。在森林冠层下的映射通道位置和网络拓扑连接的错误增加，试图确定较小的功能，如大溪流。激光雷达数据绘制森林景观中的沟壑和渠道的能力应改善渠道网络地图和拓扑模型。在沟河段尺度上，尝试使用激光雷达数据提取沟壑横截面形态信息下的森林冠层是不太成功的，由于系统低估沟深和高估沟顶宽度。有限的形态精度的数据集在这个规模可能是由于低裸地球点密度，沟底阴影，地形不连续性的过滤过程中后处理。本研究中使用的ALS数据不适合用于详细的形态分析或细微变化检测，以监测冲沟或开发沉积物预算。通过改进扫描仪技术或提高过滤和软件能力以区分植被和地面，将飞行定向于冲沟上方并增加点密度，可改进数据收集工作。
The southeastern Piedmont of the USA was severely gullied during the early 20th century. A thick canopy established by reforestation in many areas now inhibits the identification or mapping of gullies by aerial photography or other conventional remote sensing methods. An Airborne Laser-Scanning (ALS or LiDAR) mapping mission flown for the U.S. Forest Service in April, 2004 acquired bare-Earth topographic data. This paper tests the ability of the ALS topographic data to identify headwater channels and gullies for two branching gully systems in forested areas and to extract gully morphologic information. Comparisons are made with field traverses using differential GPS and reference cross sections measured by leveling surveys. At the gully network scale, LiDAR data provide accurate maps – the best available – with robust detection of small gullies except where they are narrow or parallel and closely spaced. Errors in mapping channel location and network topological connectivity under forest canopy increase with attempts to identify smaller features such as large rills. The ability of LiDAR data to map gullies and channels in a forested landscape should improve channel-network maps and topological models. At the gully reach scale, attempts to use LiDAR data to extract gully cross-section morphologic information under forest canopy were less successful due to systematic underestimation of gully depths and overestimation of gully top widths. Limited morphologic accuracy of the data set at this scale may be due to low bare-Earth point densities, shadowing of gully bottoms, and filtering of topographic discontinuities during post-processing. The ALS data used in this study are not suitable for detailed morphometric analysis or subtle change detection to monitor gullies or develop sediment budgets. Data collection may be improved by orienting flights over gullies and with increased point densities through improved scanner technology or better filtering and software capabilities to differentiate between vegetation and ground surfaces.