vectormetrics is an R package for calculating landscape and shape
metrics for vector layers. Its aim is to provide a set of metrics that
can characterize landscape patterns and properties of the shapes defined
as polygons and multipolygons. Whole package is based on Simple Feature
geometry standard provided by sf package. Every function can be used
in a tidy, piped workflow, as it always takes the data as the first
argument and returns a tibble.
You can download most recent development version of the package from GitHub with:
remotes::install_github("r-spatialecology/vectormetrics")All functions in vectormetrics start with vm_ (for vector metrics).
The second part of the name specifies the level (patch - p, class -
c or landscape - l). The last part of the function name is the
abbreviation of the corresponding metric (e.g. enn for the euclidean
nearest-neighbor distance and rect for the rectangularity). Some
landscape and class level functions have also a suffix at the end, that
specifies the aggregation method (e.g. mean, sd).
# Patch level
## vm_p_"metric"
vm_p_area()
vm_p_square()
# Class level
## vm_c_"metric"[_"aggregation"]
vm_c_np()
vm_c_shape_sd()
# Landscape level
## vm_l_"metric"[_"aggregation"]
vm_l_lpi()
vm_l_square_mn()Some examples of calculating metrics on all levels and with different class and patch columns.
library(vectormetrics)
library(sf)
data("vector_landscape")
plot(vector_landscape)
## Shape index
vm_p_shape(vector_landscape, class_col = "class")
#> MULTIPOLYGON geometry provided. You may want to cast it to separate polygons with 'get_patches()'.
#> This message is displayed once per session.
#> # A tibble: 3 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 patch 1 1 shape 5.06
#> 2 patch 2 2 shape 4.76
#> 3 patch 3 3 shape 4.80
## Number of patches
vm_c_np(vector_landscape, class_col = "class")
#> # A tibble: 3 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <int>
#> 1 class 1 <NA> np 1
#> 2 class 2 <NA> np 1
#> 3 class 3 <NA> np 1
## Largest patch index
vm_l_lpi(vector_landscape)
#> # A tibble: 1 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 landscape <NA> <NA> lpi 49.7
## Mean squareness
vm_l_square_mn(vector_landscape)
#> # A tibble: 1 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 landscape <NA> <NA> sq_mn 0.232For now there are two utility functions available in the package. First
one is get_patches() which breaks multipolygon geometries into
polygons. There are two types of neighborhood relations available: 4
(edge) and 8 (vertex). This function enables users to create set of
geometries from aggregated shapes and analyze each shape’s properties
separately.
vector_patches = get_patches(vector_landscape, class_col = "class", direction = 4)
vector_patches
#> Simple feature collection with 40 features and 2 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: 0 xmax: 30 ymax: 30
#> CRS: NA
#> First 10 features:
#> class patch geometry
#> 1 1 1 POLYGON ((1 2, 0 2, 0 3, 0 ...
#> 2 1 2 POLYGON ((14 5, 15 5, 16 5,...
#> 3 1 3 POLYGON ((12 18, 12 19, 12 ...
#> 4 1 4 POLYGON ((10 19, 10 18, 9 1...
#> 5 1 5 POLYGON ((5 20, 6 20, 6 19,...
#> 6 1 6 POLYGON ((6 21, 7 21, 7 20,...
#> 7 1 7 POLYGON ((3 16, 4 16, 4 15,...
#> 8 1 8 POLYGON ((2 24, 2 23, 2 22,...
#> 9 1 9 POLYGON ((10 20, 11 20, 11 ...
#> 10 1 10 POLYGON ((13 24, 14 24, 14 ...
vector_patches |>
dplyr::mutate(patch = as.factor(patch)) |>
plot()
## Shape index
vm_p_shape(vector_patches, class_col = "class", patch_col = "patch")
#> # A tibble: 40 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 patch 1 1 shape 1.66
#> 2 patch 1 2 shape 1.37
#> 3 patch 1 3 shape 1.51
#> 4 patch 1 4 shape 1.41
#> 5 patch 1 5 shape 1.13
#> 6 patch 1 6 shape 1.13
#> 7 patch 1 7 shape 1.13
#> 8 patch 1 8 shape 1.15
#> 9 patch 1 9 shape 1.13
#> 10 patch 1 10 shape 1.13
#> # ℹ 30 more rows
## Number of patches
vm_c_np(vector_patches, class_col = "class")
#> # A tibble: 3 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <int>
#> 1 class 1 <NA> np 19
#> 2 class 2 <NA> np 14
#> 3 class 3 <NA> np 7
## Mean squareness
vm_l_square_mn(vector_patches)
#> # A tibble: 1 × 5
#> level class id metric value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 landscape <NA> <NA> sq_mn 0.845Another utility function is get_axes() which calculates the length of
the major and minor axes of the shape. It is used to calculate the
elongation metric in vm_p_elong() but since length of axes might be
useful information itself, get_axes() was exported as a separate
function.
get_axes(vector_patches, class_col = "class")
#> # A tibble: 40 × 6
#> level class id metric major minor
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 patch 1 1 main_axes 10.8 5.28
#> 2 patch 1 2 main_axes 9.64 7.84
#> 3 patch 1 3 main_axes 8.38 5.16
#> 4 patch 1 4 main_axes 3.6 2.22
#> 5 patch 1 5 main_axes 1.42 1.42
#> 6 patch 1 6 main_axes 1.42 1.42
#> 7 patch 1 7 main_axes 1.42 1.42
#> 8 patch 1 8 main_axes 4.24 2.82
#> 9 patch 1 9 main_axes 1.42 1.42
#> 10 patch 1 10 main_axes 1.42 1.42
#> # ℹ 30 more rowsThis is an experimental version of the package, so any feedback and contributions in the form of pull requests are welcome.
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