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In landscapemetrics: Landscape Metrics for Categorical Map Patterns

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(landscapemetrics)
library(raster)
library(dplyr)

Design

All functions in landscapemetrics start with lsm_ (for landscapemetrics). 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. ennfor the euclidean nearest-neighbor distance):

# general structure
lsm_"level"_"metric"

# Patch level
## lsm_p_"metric"
lsm_p_enn()

# Class level
## lsm_c_"metric"
lsm_c_enn()

# Landscape level
## lsm_p_"metric"
lsm_l_enn()

All functions return an identical structured tibble:

| layer | level | class | id | metric | value | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | | 1 | patch | 1 | 1 | landscape metric | x | | 1 | class | 1 | NA | landscape metric | x | | 1 | landscape | NA | NA | landscape metric | x |

Checking your landscape

Before using landscapemetrics and calculating landscape metrics in general, it makes sense to check your landscape. If your landscape has some properties that restrict the calculation or interpretation of landscape metrics, that should be detected with check_landscape:

# import raster
# for local file: raster("pathtoyourraster/raster.asc")
# ... or any other raster file type, geotiff, ...

# Check your landscape
check_landscape(landscapemetrics::landscape) # because CRS is unkown, not clear

check_landscape(landscapemetrics::podlasie_ccilc) # wrong units

check_landscape(landscapemetrics::augusta_nlcd) # everything is ok

The requirements to calculate meaningful landscape metrics are:

The distance units of your projection are meter, as the package converts units internally and returns results in either meters, square meters or hectares. For more information see the help file of each function.
Your raster encodes landscape classes as integers (1, 2, 3, 4, ..., n).
Landscape metrics describe categorical landscapes, that means that your landscape needs to be classified (we throw a warning if you have more than 30 classes to make sure you work with a classified landscape).

Using `landscapemetrics`

If you are sure that your landscape is suitable for the calculation of landscape metrics, landscapemetrics makes this quite easy:

# import raster
# for local file: raster("pathtoyourraster/raster.asc")
# ... or any other raster file type, geotiff, ...

# Calculate e.g. perimeter of all patches
lsm_p_perim(landscape)

Using `landscapemetrics` in a tidy workflow

Every function in landscapemetrics accept data as its first argument, which makes piping a natural workflow. A possible use case is that you would load your spatial data, calculate some landscape metrics and then use the resulting tibble in further analyses.

# all patch IDs of class 2 with an ENN > 2.5
subsample_patches <- landscape %>% 
    lsm_p_enn() %>%
    dplyr::filter(class == 2 & value > 2.5) %>%
    dplyr::pull(id)

# show results
subsample_patches

Use multiple metric functions

To list all available metrics, just use the list_lsm() function. Here, you can specify e.g. a level or type of metrics.

# list all available metrics
list_lsm()

# list only aggregation metrics at landscape level and just return function name
list_lsm(level = "landscape", 
         type = "aggregation metric", 
         simplify = TRUE)

# you can also combine arguments and only return the function names
list_lsm(level = c("patch", "landscape"), 
         type = "core area metric", 
         simplify = TRUE)

As the result of every function always returns a tibble, combining the metrics that were selected for your research question is straightforward:

# bind results from different metric functions
patch_metrics <- dplyr::bind_rows(
  lsm_p_cai(landscape),
  lsm_p_circle(landscape),
  lsm_p_enn(landscape)
)

# look at the results
patch_metrics

All metrics are abbreviated in the result tibble. Therefore, we provide a tibble containing the full metric names, as well as the class of each metric (lsm_abbreviations_names). Using e.g. the left_join() function of the dplyr package one could join a result tibble and the abbrevations tibble.

# bind results from different metric functions
patch_metrics <- dplyr::bind_rows(
  lsm_p_cai(landscape),
  lsm_p_circle(landscape),
  lsm_p_enn(landscape)
  )
# look at the results
patch_metrics_full_names <- dplyr::left_join(x = patch_metrics,
                                             y = lsm_abbreviations_names, 
                                             by = "metric")
patch_metrics_full_names

Additionally, we provide a wrapper where the desired metrics can be specified as a vector of strings. Because all metrics regardless of the level return an identical tibble, different levels can be mixed. It is also possible to calculate all available metrics at a certain level using e.g. level = "patch". Additionally, similar to list_lsm() you can also specify e.g. a certain group of metrics. Of course, you can also include the full names and information of all metrics using full_name = TRUE.

# calculate certain metrics
calculate_lsm(landscape, 
              what = c("lsm_c_pland", "lsm_l_ta", "lsm_l_te"))

# calculate all aggregation metrics on patch and landscape level
calculate_lsm(landscape, 
              type = "aggregation metric", 
              level = c("patch", "landscape"))

# show full information of all metrics
calculate_lsm(landscape, 
              what = c("lsm_c_pland", "lsm_l_ta", "lsm_l_te"),
              full_name = TRUE)