- Derive elevation of water bodies for hydro-flattening
r.hydro.flatten [-k] input=name water_elevation=name water_elevation_stddev=name percentile=float [min_size=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Keep intermediate results
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- Force launching GUI dialog
- input=name [required]
- Raster map of binned lidar point elevation
- water_elevation=name [required]
- Raster map of derived water elevation
- Represents single elevation value for each water body
- water_elevation_stddev=name [required]
- Raster map of derived water elevation standard deviation
- percentile=float [required]
- Percentile of elevation to determine water level
- Default: 5
- Minimum size of areas in map units
The tool derives single elevation value for water bodies
based on lidar data. These values are used for hydro-flattening a digital elevation model.
raster is expected to represent ground surface created by binning lidar data
(e.g., using r.in.pdal
) with averaged ground elevation.
Small gaps in the input are expected. Large gaps are interpreted as water bodies.
The minimum size of a water body can be set with min_size
option in map units.
The output water_elevation is a raster map of water bodies where each water body
has a single value representing the water level elevation derived from the lidar data
at the edge of a water body.
Since the elevation varies along the edge, option percentile is used to determine
a single value. The variation along the edge can be examined with the water_elevation_stddev
output representing the standard deviation of the lidar elevation values
along the water body's edge. Higher deviation suggests problematic areas
that need to be further inspected.
To keep the intermediate results for inspection, use flag -k.
While this tool was designed for water bodies, it can be used for other purposes,
e.g., for filling a gap in digital elevation models caused by excluding buildings.
This tool does not interpolate gaps in data, rather it derives a single value
for each gap. The result can be used to fill gaps and the tool can be run on large areas.
For actual gap interpolation, which is typically more computationally intensive,
We will download a lidar tile with r.in.usgs
to bin the elevation points at 1 meter resolution, and derive elevation levels for lakes
with minimum size of 4000 m^2.
# select study area and resolution
g.region n=213300 s=211900 w=653900 e=655300 res=1
# download lidar tile into /tmp
r.in.usgs product=lidar output_directory=/tmp title_filter=Phase2 -d
# bin point elevation using ground and road points with reprojection
r.in.pdal input=/tmp/USGS_LPC_NC_Phase2_2014_LA_37_20164902_.laz output=ground -w class_filter=2,13
# convert elevation from feet to meters
r.mapcalc "ground_m = ground * 0.304800609601219"
# derive elevation of water bodies and standard deviation
r.hydro.flatten input=ground_m water_elevation=water_elevation water_elevation_stddev=water_elevation_stddev percentile=10 misize=4000
Figure: Input binned elevation representing ground with gaps (left),
input overlayed with elevation values estimated for gaps and highlighted with an outline (middle),
input overlayed with standard deviation of the elevation along the edge of the gaps (right).
Method based on workflow presented
at NC GIS Conference 2021 by Doug Newcomb.
Anna Petrasova, NCSU GeoForAll Lab
r.hydro.flatten source code
Latest change: Wednesday Apr 19 15:17:46 2023 in commit: 81b199aadad0788a1c2d0cd0286c816871815ddb
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