PyVF: A python program for extracting vertical features from LiDAR-DEMs

Coastal and riverine flooding is one of the most common environmental hazards that affect billions of people worldwide. A coupled hydrologic and coastal storm surge simulation is required to develop an improved understanding of the individual and collective mechanisms that can cause flooding within...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 157; p. 105503
Main Authors: Gao, Shu, Bilskie, Matthew V., Hagen, Scott C.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2022
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ISSN:1364-8152, 1873-6726
Online Access:Get full text
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Summary:Coastal and riverine flooding is one of the most common environmental hazards that affect billions of people worldwide. A coupled hydrologic and coastal storm surge simulation is required to develop an improved understanding of the individual and collective mechanisms that can cause flooding within watersheds. These simulations are dependent on an accurate digital elevation model (DEM); however, it is a challenge to include numerical model resolution as fine as contemporary DEMs due to the enormous computational cost. Therefore, significant vertical features (VFs) such as roadbeds, levees, railroads, and natural ridges must be identified and considered in developing the model representation of the DEM since the VFs can affect flow propagation. PyVF is an open-source program to extract significant VFs from a high-resolution, bare-earth, LiDAR-derived DEM automatically. This paper introduces the methods and shows the automated extraction of VFs for a coastal, urban, mountain and beach area. •PyVF is a program for identifying vertical features automatically from high-resolution topographic elevation data.•The vertical features are defined by some meaningful parameters.•A variety of landforms (e.g., low-gradient area, urban area, mountain area, beach area) can benefit from the ability of PyVF.
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ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2022.105503