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...
Saved in:
| Published in: | Environmental modelling & software : with environment data news Vol. 157; p. 105503 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.11.2022
|
| Subjects: | |
| ISSN: | 1364-8152, 1873-6726 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1364-8152 1873-6726 |
| DOI: | 10.1016/j.envsoft.2022.105503 |