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Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data intensive methods such as kriging performing better than 3 more scalable methods involving postprocessing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, −0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.
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This content will become publicly available on April 25, 2026
Propagating DEM Uncertainty to Stream Extraction using GRASS
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
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- Award ID(s):
- 2303651
- PAR ID:
- 10609809
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- geomorphometry streams conditional Gaussian simulation GRASS DEM
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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