1.1 Compute single viewshed

#Load in DSMtest_dsm <- terra::rast(system.file("test_dsm.tif",                                        package ="viewscape"))#Load in the viewpointtest_viewpoint <- sf::read_sf(system.file("test_viewpoint.shp",                                           package = "viewscape"))#Compute viewshedoutput <- viewscape::compute_viewshed(dsm = test_dsm,                                       viewpoints = test_viewpoint,                                       offset_viewpoint = 6,                                       r = 1600,                                      method = 'plane')

# overlap viewshed on DSMoutput_r <- viewscape::visualize_viewshed(output, outputtype = 'raster')terra::plot(test_dsm, axes=FALSE, box=FALSE, legend = FALSE)terra::plot(output_r, add=TRUE, col = "red", axes=FALSE, box=FALSE, legend = FALSE)terra::plot(test_viewpoint, add = TRUE, col = "blue", axes=FALSE, box=FALSE, legend = FALSE)

1.2 Subset viewshed using by specifying the field of view

sector <- viewscape::fov_mask(output, c(40,160))terra::plot(test_dsm, axes=FALSE, box=FALSE, legend = FALSE)terra::plot(viewscape::visualize_viewshed(sector, outputtype = 'raster'),            axes=FALSE, box=FALSE, legend = FALSE, add = TRUE, col = "red")terra::plot(test_viewpoint, add = TRUE, col = "blue", axes=FALSE, box=FALSE, legend = FALSE)

1.3 Compute visual magnitude

vm <- viewscape::visual_magnitude(output,test_dsm)terra::plot(vm, axes=FALSE, box=FALSE)terra::plot(test_viewpoint, add = TRUE, col = "blue", axes=FALSE, box=FALSE, legend = FALSE)

1.4 Compute the viewshed for multiple viewpoints

#Load in DSMtest_dsm <- terra::rast(system.file("test_dsm.tif",                                        package ="viewscape"))# Load points (.shp file)test_viewpoints <- sf::read_sf(system.file("test_viewpoints.shp",                                            package = "viewscape"))# Compute viewshedsoutput <- viewscape::compute_viewshed(dsm = test_dsm,                                       viewpoints = test_viewpoints,                                       offset_viewpoint = 6,                                       parallel = TRUE,                                       workers = 1)

# Use plot all viewsheds on DSMpar(mfrow=c(3,3))for(i in 1:length(output)) {  each <- output[[i]]  raster_data <- viewscape::visualize_viewshed(each, outputtype="raster")  terra::plot(test_dsm, axes=FALSE, box=FALSE, legend = FALSE)  terra::plot(raster_data, add=TRUE, col = "red", axes=FALSE, box=FALSE, legend = FALSE)}

2.1 Calculate the metrics of viewshed

The function of view depth analysis can calculate two differentmetrics: the furthest distance and standard deviation of distances. Tocalculate view depth, there are two needed objects: the DSM that wasused to get viewshed and result from viewshed analysis.

The function of extent analysis can calculate the total area ofviewshed and needs the DSM that was used to get viewshed and result fromviewshed analysis.

The following function can calculate the area of ground surface andstandard deviation of elevations within a viewshed. The function needs aDSM and a DEM/DTM to calculate the metrics.

#Load in DSMtest_dsm <- terra::rast(system.file("test_dsm.tif",                                        package ="viewscape"))# Load DTMtest_dtm <- terra::rast(system.file("test_dtm.tif",                                        package ="viewscape"))# Load canopy rastertest_canopy <- terra::rast(system.file("test_canopy.tif",                                        package ="viewscape"))# Load building footprints rastertest_building <- terra::rast(system.file("test_building.tif",                                        package ="viewscape"))

# calculate metrics given the viewshed, canopy, and building footprintstest_metrics <- viewscape::calculate_viewmetrics(output[[1]],                                                  test_dsm,                                                  test_dtm,                                                  list(test_canopy, test_building))test_metrics

2.2 Calculate land use/cover diversity

calculate_diversity() calculates the proportion of each type of landuse/ cover within a viewshed to get the Shannon Diversity Index.

# load landuse rastertest_landuse <- terra::rast(system.file("test_landuse.tif",                                        package ="viewscape"))

# the Shannon Diversity Index (SDI)test_diversity <- viewscape::calculate_diversity(output[[1]],                                                 test_landuse,                                                 proportion = TRUE)# SDI and The proportion of each type of land usetest_diversity

2.3 calculate a single feature

calculate_feature is to calculate the proportion of a feature(including trees, buildings, parking, and roads) within the viewshed.This function can be applied to

# load canopy rastertest_canopy <- terra::rast(system.file("test_canopy.tif",                                          package ="viewscape"))# calculate the percentage of canopy coverage  test_canopy_proportion <- viewscape::calculate_feature(viewshed = output[[1]],                                                        feature = test_canopy,                                                       type = 2,                                                       exclude_value=0)test_canopy_proportion