Workshop: Propagating DEM Uncertainty to Stream Extraction using GRASS
Year: 2025
Type: conference-paper
Abstract: GRASS is an open-source geospatial processing engine. With over 400 tools available in the core distribution and an additional 400+ tools available as extensions, GRASS has broad applicability in the Earth Sciences and geomorphometry in particular. In this workshop, we will give an introduction to GRASS and demonstrate some of the geomorphometry tools available in GRASS. Specifically, we will show how to compute stream extraction uncertainty using a workflow adapted from Hengl (2007) [1] and Hengl (2010) [2]. We will begin by downloading publicly available LiDAR data of the Perugia area using GRASS data fetching tools. Then, we will use R’s kriging functions (gstat) to create 100 iterations of a DEM. After exploring some of the stream extraction and flow routing methods available in GRASS, we will extract streams from each of the 100 DEMs to compute stream uncertainty. The workshop will be conducted in a Jupyter Notebook hosted in Google Colab. By the end of the workshop, participants will have hands on experience with: Creating GRASS projects and importing datasets Adjusting the computational extent and resolution Creating DEMs from point data using a variety of methods implemented in GRASS and using a stochastic kriging approach in R Using the R interface for GRASS and R packages with GRASS data Computing Stream Uncertainty Developing publication-quality figures with grass.jupyter
Authors: White, Corey, Haedrich, Caitlin
Institution: North Carolina State University
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930674653
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