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NAME
r.viewshed.exposure - Visual exposure to defined exposure source.
Computes visual exposure to defined exposure source using weighted parametrised cumulative viewshed analysis.
KEYWORDS
raster,
viewshed,
line of sight,
LOS,
visual exposure,
parallel
SYNOPSIS
r.viewshed.exposure
r.viewshed.exposure --help
r.viewshed.exposure [-cr] input=name output=name [source=name] [sourcecat=value] [sampling_points=name] [weights=name] [observer_elevation=value] [range=value] [function=name] [b1_distance=value] [sample_density=value] [seed=value] [refraction_coeff=value] [memory=memory in MB] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
- -c
- Consider the curvature of the earth (current ellipsoid)
- -r
- Consider the effect of atmospheric refraction
- --overwrite
- Allow output files to overwrite existing files
- --help
- Print usage summary
- --verbose
- Verbose module output
- --quiet
- Quiet module output
- --ui
- Force launching GUI dialog
Parameters:
- input=name [required]
- Name of input digital surface raster map
- Name of input raster map
- output=name [required]
- Name of output raster map of visual exposure
- Name for output raster map
- source=name
- Name of input raster map of exposure source
- Name of input raster map
- sourcecat=value
- Raster values to use as exposure source
- 1-
- Default: *
- sampling_points=name
- Name of input vector map of sampling points
- Or data source for direct OGR access
- weights=name
- Name of input raster map of viewshed weights
- Name of input raster map
- observer_elevation=value
- Observer elevation above the ground
- 0.0-
- Options: 0.0-
- Default: 1.5
- range=value
- Exposure range
- 0.0- , -1 for infinity
- Options: 0.0-
- Default: 100
- function=name
- Viewshed parametrisation function
- Binary, Distance_decay, Fuzzy_viewshed, Visual_magnitude, Solid_angle
- Options: Binary, Distance_decay, Fuzzy_viewshed, Visual_magnitude, Solid_angle
- Default: Distance_decay
- b1_distance=value
- Radius around the observer where clarity is perfect. Used in fuzzy viewshed function.
- Default: 10
- sample_density=value
- Density of sampling points
- 0.0-100.0
- Options: 0.0-100.0
- Default: 25
- seed=value
- Random seed, default [random]
- 0-
- Options: 0-
- refraction_coeff=value
- Refraction coefficient
- 0.0-1.0
- Options: 0.0-1.0
- Default: 0.14286
- memory=memory in MB
- Maximum memory to be used (in MB)
- Cache size for raster rows
- Default: 300
- nprocs=integer
- Number of threads for parallel computing
- Default: 1
r.viewshed.exposure computes visual exposure to given exposure
source(s) using weighted (optional) parametrised (optional) cumulative viewshed.
The processing workflow of the module consists of five steps:
- Random sampling of exposure source raster map with vector points,
- Calculating binary viewshed for each exposure source point,
- Optional parametrisation of the binary viewshed,
- Optional weighting of the (parametrised) viewshed,
- Cumulating the (weighted) (parametrised) viewsheds.
Processing workflow
1. Random sampling of exposure source raster map with vector points
To improve computational efficiency, the exposure source raster map is
randomly sampled with defined density (0-100%; option
sample_density).
In general, lower sampling densities lead to lower
accuracy, higher uncertainty of the result and lower processing time,
while higher sampling densities lead to higher accuracy, lower uncertainty of
the result and longer processing time. Alternatively, it is possible to replace
the exposure source raster map with own vector map of exposure source points
(option
sampling_points).
2. Binary viewshed for each exposure source point
A binary viewshed for each exposure source point is calculated using
r.viewshed module. The height of exposure
source point above the surface is 0m. The height of observer point (exposure
receiver) above the surface is specified by option
observer_elevation.
Viewshed radius (range of visual exposure) is specified by option
max_distance.
3. (optional) Parametrisation of the binary viewshed
The module supports different parametrization functions to better reflect
human visual perspective by accounting for the
variable contribution of the exposure source pixels to visual
exposure depending on their distance, slope and aspect relative to the observer
(option
function).
Four parametrisation functions are implemented:
distance decay function,
fuzzy viewshed function,
visual magnitude function and
solid angle function.
In distance decay function, the contribution of an exposure source pixel
xi to visual exposure at the observer pixel decreases in proportion to
the square of distance between the exposure source pixel and the observer:
D(xi) = A/v2; A is the area of the exposure source
pixel, v is the distance between the exposure source pixel and
the observer.
See Grêt-Regamey et al. (2007) and Chamberlain and Meitner (2013) for more
details.
In fuzzy viewshed function, the contribution of an exposure source pixel
xi to visual exposure at the observer pixel depends on the distance
between the exposure source pixel and the observer and the radius of perfect
clarity.
See Fisher (1994) and Ogburn (2006) for more details.
In visual magnitude function, the contribution of an exposure source pixel
xi to visual exposure at the observer pixel depends on the pixel's slope,
aspect and distance relative to the observer.
See Chamberlain and Meitner (2013) for more details.
In solid angle function, the contribution of an exposure source pixel
xi to visual exposure at the observer pixel is calculated as a solid
angle, i.e. the area (in sterradians) of the observer's eye retina covered by
the exposure source pixel.
See Domingo-Santos et al. (2011) for more details.
4. (optional) Weighting of the (parametrised) viewshed
Weighting of the individual (parametrised) viewsheds enables modelling
variable intensities of the exposure sources. The individual viewsheds are
multiplied by values extracted from the weights raster map
(option
weights) at the exposure source points.
5. Cumulating the (weighted) (parametrised) viewsheds
After each iteration, the partial viewsheds are cumulated (added),
resulting in a raster of (weighted) (parametrised) cumulative viewshed.
This raster represents visual exposure to the exposure source.
Options
memory specifies the amount of memory allocated for
viewshed computation. Option
nprocs specifies the number of cores used in
parallel processing. In parallel processing, the computation of individual
viewsheds is randomly distributed across the specified cores.
Computation of visual exposure to major roads in South-West Wake county,
North Carolina. Input data are a terrain model and a raster map of major roads
from NC dataset. Viewshed parametrisation function is set to none (example 1)
and solid angle (example 2). Sampling density is set to 50%,
exposure range to 2km.
# set computation region to terrain model
g.region raster=elevation@PERMANENT
# calculate visual exposure
# no viewshed parametrisation function (binary viewshed)
r.viewshed.exposure input=elevation@PERMANENT
output=exposure_roadsmajor_b
source=roadsmajor@PERMANENT
observer_elevation=1.50
max_distance=2000
sample_density=50 memory=5000 nprocs=25
# calculate visual exposure
# solid anfle viewshed parametrisation function
r.viewshed.exposure input=elevation@PERMANENT
output=exposure_roadsmajor_s
source=roadsmajor@PERMANENT
observer_elevation=1.50
max_distance=2000
function=solid_angle
sample_density=50 memory=5000 nprocs=25
# scale solid angle values for visualisation purposes
# (see Domingo-Santos et al., 2011)
r.mapcalc expression=exposure_roadsmajor_s_rescaled =
if(exposure_roadsmajor_s@user1>=0.2*3.1416,1,1/
(-1* log(exposure_roadsmajor_s@user1 /(2*3.1416))))
Example of r.viewshed.exposure (1)
Example of r.viewshed.exposure (2)
- Implement variable exposure source height.
- Implement possibility to switch between absolute and relative values
of visual exposure (now absolute).
- Cimburova, Z., Blumentrath, S., 2022.
Viewshed-based modelling of visual exposure to urban greenery -
an efficient GIS tool for practical applications.
Landscape and Urban Planning 222, 104395.
https://doi.org/10.1016/j.landurbplan.2022.104395
- Chamberlain, B.C., Meitner, M.J., 2013.
A route-based visibility analysis for landscape management.
Landscape and Urban Planning 111, 13-24.
https://doi.org/10.1016/j.landurbplan.2012.12.004
- Domingo-Santos, J.M., de Villarán, R.F., Rapp-Arrarás, Í., de Provens,
E.C.-P., 2011.
The visual exposure in forest and rural landscapes:
An algorithm and a GIS tool.
Landscape and Urban Planning 101, 52-58.
https://doi.org/10.1016/j.landurbplan.2010.11.018
- Fisher, P., 1994.
Probable and fuzzy models of the viewshed operation,
in: Worboys, M.F. (Ed.), Innovations in GIS.
Taylor & Francis, London, pp. 161-176.
- Grêt-Regamey, A., Bishop, I.D., Bebi, P., 2007.
Predicting the scenic beauty value of mapped landscape changes
in a mountainous region through the use of GIS.
Environment and Planning B: Planning and Design 34, 50-67.
https://doi.org/10.1068/b32051
- Ogburn, D.E., 2006.
Assessing the level of visibility of cultural objects in past landscapes.
Journal of Archaeological Science 33, 405-413.
https://doi.org/10.1016/j.jas.2005.08.005
r.viewshed,
r.viewshed.cva,
r.survey
Zofie Cimburova,
NINA
Stefan Blumentrath,
NINA
SOURCE CODE
Available at:
r.viewshed.exposure source code
(history)
Latest change: Tuesday Mar 26 20:41:36 2024 in commit: 3b09b1d78f6e96ffebacac6e36f0afd91ad0c091
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GRASS Development Team,
GRASS GIS 8.3.3dev Reference Manual