Parameters - Viewshed exposure and impact

Table 1. Settings for r.viewshed.exposure, default values are used for the other parameters.

Variable	Description	Value range	Value used
input	Digital elevation map, in this study a DSM-DTM raster map is used.		%municipality%_dsmdtm_1m_utmxx.tif
output	Viewshed exposure output raster map		%municipality%_viewshed_exposure_1m_utmxx.tif
source	Exposure source, in this study a tree crown raster map is used (1 = crown coverage, NA = no crown coverage)		%municipality%_tree_crown_mask_1m_utmxx.tif
range	Maximum viewshed radius	>= 0.0	100m
function	Viewshed parametrisation function	None, Binary, Distance decay, Visual magnitude, Solid angle	Distance decay
sample_density	Density of sampling points	0.0 – 100.0	25
seed	Random seed	>= 0	1
memory	Amount of memory to use in MB	>= 1MB	200000
nprocs	Number of cores to parallelise r.viewshed.exposure	>= 1	40
example command	r.viewshed.exposure.py input=dsmdtm_1m_utm32_flt@oslo output=visual_exposure_1m_utm32 source=treecrown_1m_utm32_int@oslo range=100 function=Distance_decay sample_density=25 seed=1 memory=200000 nprocs=40

Table 2. Settings for r.viewshed.impact, default values are used for the other parameters. Note that r.viewshed.impact is ran 3 times. Round 1: open space as weight layer, Round 2: open public space as weight layer and, Round 3: open private space as weight layer.

Variable	Description	Value range	Value used
exposure	Exposure source locations, in this study the tree crown polygons are used.		%municipality%_treecrowns_utmxx.shp
column	Name of attribute column to store visual impact values		v_open, v_public v_private
dsm	Digital elevation map, in this study a DSM-DTM raster map is used.		%municipality%_dsmdtm_1m_utmxx.tif
weight	Input weights raster map		%municipality%_open_space_1m_utmxx.tif %municipality%_public_space_1m_utmxx.tif %municipality%_private_space_1m_utmxx.tif
range	Maximum viewshed radius	>= 0.0	100m
function	Viewshed parametrisation function	None, Binary, Distance decay, Visual magnitude, Solid angle	Distance decay
sample_density	Density of sampling points	0.0 – 100.0	25
seed	Random seed	>= 0	1
memory	Amount of memory to use in MB	>= 1MB	200000
cores_e	Number of cores to parallelise r.viewshed.exposure	>= 1	40
cores_i	Number of cores to parallelise r.viewshed.impact	>= 1	40
example command	r.viewshed.impact.py exposure=treecrowns@impact column=v_public dsm=dsmdtm_1m_utm32_flt@impact weight=public_space_1m_utm32_int@oslo_impact Range=100 seed=1 memory=200000 cores_e=10 cores_i=20

References

• Cimburova, Z. and Blumentrath, S., 2022. Viewshed-based modelling of visual exposure to urban greenery – An efficient GIS tool for practical planning applications. Landscape and Urban Planning, Volume 222,104395. https://doi.org/10.1016/j.landurbplan.2022.104395

• Cimburova, Z., Blumentrath, S., Barton, D.N., 2023. Making trees visible: A GIS method and tool for modelling visibility in the valuation of urban trees. Urban Forestry & Urban Greening 81, 127839. https://doi.org/10.1016/j.ufug.2023.127839