High resolution topographic data has become widely available over the preceding decades and increasingly detailed digital elevation models are aiding in nearly every type of natural-resource related research. Digital terrain models (DTMs), which depict the ground surface topography devoid of vegetation or man-made structures, are particularly helpful in stream-related research. Historically, coarse resolution topographic data (e.g., several meters to tens of meters pixel size) did not afford evaluation of meter scale roughness elements exposed above the water surface within stream channels. The purpose of this study is to demonstrate how the integration of water-classified lidar returns in submeter resolution DTM-development may capture stream corridor topography and be useful for further stream-related research. Four reaches of streams draining the southeastern Blue Ridge Escarpment in southern North Carolina (USA) are assessed for reach positioning, length, and gradient. These parameters are chosen because they are foundational to many other forms of stream analysis (e.g., stream power, normalized channel steepness, chi, and others). Water-assigned lidar returns are included in 0.5- pixel size DTMs and compared to both a 0.5-m DTM generated without use of water returns (i.e., bare-earth) and a pre-processed, hydro-flattened 0.9-m bare-earth DTM. In steep bedrock channels, bare-earth only DTMs result in channels 12–23% shorter than water return integrated DTMs. Observations of stream positioning on DTMs that include water returns and comparisons to orthophotographs suggest a more consistent stream center line in relation to boulders and exposed bedrock within stream channels. Small streams do not benefit from the modified analysis methods because water-classified returns are not present in these channels. Nor do low gradient alluvial channels benefit because these streams tend to lack exposed bedrock or large roughness elements that might divert stream flows. Because so many geomorphic parameters are largely dependent on channel length, these findings have far-reaching implications in ongoing stream-related research. The methods presented here do not require new data collection or technology, but offer simple modifications to processing of existing data and should be considered on other high quality lidar datasets.

在过去的几十年中，高分辨率地形数据已经广泛可用，并且越来越详细的数字高程模型正在帮助几乎每种类型的自然资源相关研究。数字地形模型（DTM）描述了没有植被或人造结构的地表地形，在与河流有关的研究中特别有用。历史上，粗糙分辨率的地形数据（例如，几米到几十米的像素大小）无法评估流道内水面上方暴露的米级粗糙度元素。这项研究的目的是演示如何将水分类的激光雷达回波与亚米级分辨率DTM的发展相结合，可以捕获河流走廊的地形，并有助于进一步开展与河流有关的研究。评估了北卡罗莱纳州南部（美国）东南蓝脊陡坡排水的四段溪流的位置，长度和坡度。选择这些参数是因为它们是许多其他形式的流分析（例如，流功率，归一化通道陡度，chi等）的基础。分配给水的激光雷达返回包含在0.5像素大小的DTM中，并且与不使用回水（即，裸土）生成的0.5-m DTM和经过预处理，水压平的0.9-m裸地球进行了比较DTM。在陡峭的基岩通道中，仅裸露的DTM导致通道比回水集成DTM短12–23％。在DTM上观察到的河流位置（包括回水）和与正射照片的比较表明，与河流通道内的巨石和裸露的基岩有关的河流中心线更加一致。小流量无法从修改后的分析方法中受益，因为在这些渠道中不存在按水分类的收益。低坡度冲积河道也无济于事，因为这些溪流往往缺乏裸露的基岩或可能使溪流转向的大粗糙度元素。由于这么多地貌参数很大程度上取决于通道长度，因此这些发现对正在进行的与流有关的研究具有深远的影响。此处介绍的方法不需要新的数据收集或技术，