CGEI

The goal of CGEI is to …

Installation

1. Install R

   • (Recommended) Install RStudio

2. Install terra

3. Install CGEI:

   • Install remotes

     install.packages("remotes")

   • Install CGEI

     CGEI is still in very active development. Therefore, the package is also not on CRAN yet. You can install the latest version of CGEI from GitHub with:
     remotes::install_git("https://github.com/STBrinkmann/CGEI")

Examples

Data

For the first two examples we will use a Digital Elevation Model (DEM), a binary Greenspace Mask based on a land cover classification and a Digital Surface Model (DSM). The DSM is generated from LiDAR data collected in 2013. A detailed explanation on how the DSM has been generated using R is provided in this tutorial. To reduce the size of the R package, the sample data has been uploaded to a separate GitHub repository and needs to be downloaded first.

# Download DEM
DEM_tmp <- tempfile(fileext = ".tif")
download.file(url = "https://github.com/STBrinkmann/data/raw/main/GVI_Data/GVI_DEM.tif",
              destfile = DEM_tmp, mode="wb")

# Download DSM
DSM_tmp <- tempfile(fileext = ".tif")
download.file(url = "https://github.com/STBrinkmann/data/raw/main/GVI_Data/GVI_DSM.tif",
              destfile = DSM_tmp, mode="wb")

# Download GreenSpace
GS_tmp <- tempfile(fileext = ".tif")
download.file(url = "https://github.com/STBrinkmann/data/raw/main/GVI_Data/GVI_GreenSpace.tif",
              destfile = GS_tmp, mode="wb")

# Download hoechstetter
hoechstetter_tmp <- tempfile(fileext = ".tif")
download.file(url = "https://github.com/STBrinkmann/data/raw/main/GVI_Data/hoechstetter.tif",
              destfile = hoechstetter_tmp, mode="wb")

# Load libraries. if one is not installed, use the "install.packages()" function
library(terra)
library(sf)
library(sfheaders)
library(CGEI)

# Load raster objects
GreenSpace <- rast(GS_tmp)
DEM <- rast(DEM_tmp)
DSM <- rast(DSM_tmp)
hoechstetter <- rast(hoechstetter_tmp)

Viewshed Greenness Visibility Index (VGVI)

The VGVI expresses the proportion of visible greenness to the total visible area based on a viewshed. The estimated VGVI values range between 0 and 1, where 0 = no green cells are visible, and 1 = all of the visible cells are green.

Based on a viewshed and a binary greenspace raster, all visible points are classified as visible green and visible no-green. All values are summarized using a decay function, to account for the reducing visual prominence of an object in space with increasing distance from the observer. Currently two options are supported, a logistic and an exponential function.

$$\begin{align*} f(x) = \cfrac{1}{1 + e^{b(x-m)}} && \text{(logistic)}\ f(x) = \cfrac{1}{1 + (bx^{m})} && \text{(exponential)} \end{align*} $$

The full algorithm has been described in Brinkmann et al. (2022).

The visualizeWeights function helps to adjust spatial weight parameters m and b used in the vgvi and vgvi_from_sf functions.

Example output of the visualizeWeight function to compare and parameterize the decay weights of a logistic (left) and an exponential (right) function.

1. Single Point

Calculate the viewshed for a 200 meters radius around the observers position at 1.7 meters height (eye level).

# Generate single observer point
observer <- st_sf(sf_point(c(492243.3, 5454231.4)), crs = st_crs(26910))
vs <- viewshed_list(observer = observer, dsm_rast = DSM, dtm_rast = DEM, 
                    max_distance = 200, observer_height = 1.7)

Left: Digital Surface Model (DSM); Right: Viewshed, where green = visible and yellow = no-visible area.

The VGVI can now be calculated using the vgvi function.

vgvi_sf <- vgvi(observer = observer, 
                dsm_rast = DSM, dtm_rast = DEM, greenspace_rast = GreenSpace, 
                max_distance = 200, observer_height = 1.7)
vgvi_sf$VGVI
#> [1] 0.6134167

The output of ~0.61 indicates, that ~61% of the visible area, within a 200 meters radius, is covered by greenspace.

2. Road Network

We also provide sample data of a SF LINESTRING feature, representing a road network. This feature represents roads and paths, that can be reached by walking within 5 minutes from our observer location.

# Download line feature and load as sf object
isodistance <- read_sf("https://github.com/STBrinkmann/data/raw/main/GVI_Data/isoline.gpkg")

Compute the VGVI along the line feature. As the resolution of our DSM is 5 meters, points along the line feature will be generated every 5 meters, too.

vgvi_sf <- vgvi(observer = isodistance, 
                dsm_rast = DSM, dtm_rast = DEM, greenspace_rast = GreenSpace, 
                max_distance = 200, observer_height = 1.7, spacing = 2,
                m = 1, b = 3, mode = "exponential", cores = 12)

The plot below shows the VGVI along the line feature. Yellow has a high VGVI, while blue has a low VGVI. The VGVI is highest in parks and lowest in urban center in the east.