Integrating water-classified returns in DTM generation to increase accuracy of stream delineations and geomorphic analyses

Abstract

Abstract 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-meter pixel size DTMs and compared to both a 0.5-meter DTM generated without use of water returns (i.e., bare-earth) and a pre-processed, hydro-flattened 0.9-meter 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.