The goal of viewscape is to provide accessible landscape spatial analysis based on the viewshed within the R environment. The package computes viewsheds from digital surface models, derives visual magnitude and a rich set of viewscape configuration metrics, generates panoramic views, and maps intervisibility networks between viewpoints.
# Install the package from CRAN
install.packages("viewscape")
# Install the development version from GitHub
devtools::install_github("billbillbilly/viewscape", dependencies = TRUE)
# Load the package
library(viewscape)| Function | Description |
|---|---|
compute_viewshed() |
Compute a viewshed from a DSM and one or more viewpoints. Supports single and multi-viewpoint analysis, parallel processing, and raster output. Observer height (offset_viewpoint), target height (offset_height), and radius (r) are all specified in metres and converted automatically to the DSM’s CRS unit. |
visual_magnitude() |
Calculate visual magnitude — the gradient of visibility across the viewshed — following Chamberlain & Meitner (2013). |
calculate_viewmetrics() |
Compute a set of configuration metrics (extent, depth, relief, skyline, patch statistics) for a viewshed. |
calculate_diversity() |
Compute the Shannon Diversity Index of land cover within a viewshed. |
calculate_feature() |
Compute the proportion of a specific land-use or land-cover class visible in a viewshed. |
sector_mask() |
Subset a viewshed to a field-of-view sector defined by a bearing and angular width. |
intervis_network() |
Compute pairwise intervisibility among a set of viewpoints and return results as an adjacency matrix or an sf network of lines. |
pano_view() |
Generate a cylindrical or equirectangular panoramic view from a DSM. Supports satellite RGB colour (via greenSD), semantic land-cover layers, and customisable ray-marching parameters. |
visualize_viewshed() |
Visualize a viewshed object with various plotting options. |
# Load data
test_dsm <- terra::rast(system.file("test_dsm.tif", package = "viewscape"))
test_dtm <- terra::rast(system.file("test_dtm.tif", package = "viewscape"))
test_viewpoint <- sf::read_sf(system.file("test_viewpoint.shp", package = "viewscape"))
test_canopy <- terra::rast(system.file("test_canopy.tif", package = "viewscape"))
test_building <- terra::rast(system.file("test_building.tif", package = "viewscape"))
test_landuse <- terra::rast(system.file("test_landuse.tif", package = "viewscape"))
# Compute viewshed (observer 1.7 m above surface, 1000 m radius — both in metres)
vs <- viewscape::compute_viewshed(dsm = test_dsm,
viewpoints = test_viewpoint,
offset_viewpoint = 1.7,
r = 1000)
# Visual magnitude
vm <- viewscape::visual_magnitude(vs, test_dsm)
# Configuration metrics
metrics <- viewscape::calculate_viewmetrics(vs, test_dsm, test_dtm,
list(test_canopy, test_building))
# Shannon Diversity Index
diversity <- viewscape::calculate_diversity(test_landuse, vs, proportion = TRUE)
# Panoramic view (cylindrical, no internet needed)
pano <- viewscape::pano_view(test_dsm, test_viewpoint, h = 1.7,
method = "cylindrical", plot = TRUE)
# Intervisibility network (returns adjacency matrix by default)
# mat <- viewscape::intervis_network(test_viewpoint, test_dsm)calculate_viewmetrics() returns the following metrics:
- Number of patches (Nump): visible fragmentation — total visible patches within the viewscape.
- Mean shape index (MSI): visible patchiness based on average perimeter-to-area ratio for all viewscape patches.
- Edge density (ED): visible complexity based on the length of patch edges per unit area.
- Patch size (PS): total average size of a patch over the entire viewscape area.
- Patch density (PD): visible landscape granularity.
- Extent: total visible area (visible cells × resolution²).
- Depth: furthest visible distance from the viewpoint.
- Vdepth: standard deviation of distances to visible points.
- Horizontal: total visible terrestrial (ground-level) area.
- Relief: standard deviation of visible ground surface elevations.
- SVF (Sky View Factor): proportion of the visible sky hemisphere at the viewpoint, computed by casting rays in 36 azimuth directions and calculating SVF = mean(cos²(max obstruction angle)). Ranges from 0 (fully enclosed) to 1 (open sky). Method follows Oke (1981) as implemented in the shadow package (Dorman et al. 2019).
- Skyline: standard deviation of DSM heights for cells with visible canopy or buildings.
This package may take a long time to run if using spatially large or high-resolution digital elevation models.
The package does not support multi-core processing on Windows.
compute_viewshed(parallel = TRUE) will automatically fall back to a
single worker on Windows.
Franklin, W. R., & Ray, C. (1994). Higher isn’t necessarily better: Visibility algorithms and experiments. Advances in GIS Research, 2, 751–770.
Wang, J., Robinson, G. J., & White, K. (2000). Generating viewsheds without using sightlines. Photogrammetric Engineering and Remote Sensing, 66(1), 87–90.
Chamberlain, B. C., & Meitner, M. J. (2013). A route-based visibility analysis for landscape management. Landscape and Urban Planning, 111, 13–24.
Tabrizian, P., Baran, P. K., Van Berkel, D., Mitasova, H., & Meentemeyer, R. (2020). Modeling restorative potential of urban environments by coupling viewscape analysis of lidar data with experiments in immersive virtual environments. Landscape and Urban Planning, 195, 103704.
Oke, T. R. (1981). Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. Journal of Climatology, 1(3), 237–254.
Dorman, M., Vulkan, A., Erell, E., & Kloog, I. (2019). shadow: Geometric Shadow Calculations. The R Journal, 11(1), 287–309. https://github.com/michaeldorman/shadow
Lu, X., Li, Z., Cui, Z., Oswald, M. R., Pollefeys, M., & Qin, R. (2020). Geometry-aware satellite-to-ground image synthesis for urban areas. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 859-867).
This package may take a long time to run with spatially large or high-resolution elevation models.
If you discover a bug not already in the reported issues, please open a new issue with a reproducible example.