@article{scheip_wegmann_2022, title={Insights on the growth and mobility of debris flows from repeat high-resolution lidar}, volume={3}, ISSN={["1612-5118"]}, DOI={10.1007/s10346-022-01862-2}, journal={LANDSLIDES}, author={Scheip, Corey and Wegmann, Karl}, year={2022}, month={Mar} }
 @article{scheip_wegmann_2021, title={HazMapper: a global open-source natural hazard mapping application in Google Earth Engine}, volume={21}, ISSN={["1684-9981"]}, DOI={10.5194/nhess-21-1495-2021}, abstractNote={Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.
                    }, number={5}, journal={NATURAL HAZARDS AND EARTH SYSTEM SCIENCES}, author={Scheip, Corey M. and Wegmann, Karl W.}, year={2021}, month={May}, pages={1495–1511} }
 @article{scheip_2021, title={Integrating water-classified returns in DTM generation to increase accuracy of stream delineations and geomorphic analyses}, volume={385}, ISSN={["1872-695X"]}, DOI={10.1016/j.geomorph.2021.107722}, abstractNote={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- 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.}, journal={GEOMORPHOLOGY}, author={Scheip, Corey M.}, year={2021}, month={Jul} }