Get Started with landscapemetrics

2019-03-15

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
#> 
#> Caution:
#>  Only metric coordinates make sense for landscapemetrics. You will still get results, but these are not comparable with other studies and the interpretation of metrics that use the cellsize as input becomes error prone.
#>   layer crs units   class n_classes  OK
#> 1     1  NA    NA integer         3 (?)

check_landscape(landscapemetrics::podlasie_ccilc) # wrong units
#> 
#> Caution:
#>  Only metric coordinates make sense for landscapemetrics. You will still get results, but these are not comparable with other studies and the interpretation of metrics that use the cellsize as input becomes error prone.
#>   layer        crs   units   class n_classes OK
#> 1     1 geographic degrees integer        14  x

check_landscape(landscapemetrics::augusta_nlcd) # everything is ok
#>   layer       crs units   class n_classes OK
#> 1     1 projected     m integer        15  v

The requirements to calculate meaningful landscape metrics are:

  1. 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.

  2. Your raster encodes landscape classes as integers (1, 2, 3, 4, …, n).

  3. 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)
#> # A tibble: 27 x 6
#>    layer level class    id metric value
#>    <int> <chr> <int> <int> <chr>  <dbl>
#>  1     1 patch     1     1 perim      4
#>  2     1 patch     1     2 perim    130
#>  3     1 patch     1     3 perim     12
#>  4     1 patch     1     4 perim     20
#>  5     1 patch     1     5 perim      4
#>  6     1 patch     1     6 perim     10
#>  7     1 patch     1     7 perim      4
#>  8     1 patch     1     8 perim      4
#>  9     1 patch     1     9 perim      8
#> 10     1 patch     2    10 perim     38
#> # ... with 17 more rows

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.

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()
#> # A tibble: 132 x 5
#>    metric name                         type             level function_name
#>    <chr>  <chr>                        <chr>            <chr> <chr>        
#>  1 area   patch area                   area and edge m~ patch lsm_p_area   
#>  2 cai    core area index              core area metric patch lsm_p_cai    
#>  3 circle related circumscribing circ~ shape metric     patch lsm_p_circle 
#>  4 contig contiguity index             shape metric     patch lsm_p_contig 
#>  5 core   core area                    core area metric patch lsm_p_core   
#>  6 enn    euclidean nearest neighbor ~ aggregation met~ patch lsm_p_enn    
#>  7 frac   fractal dimension index      shape metric     patch lsm_p_frac   
#>  8 gyrate radius of gyration           area and edge m~ patch lsm_p_gyrate 
#>  9 ncore  number of core areas         core area metric patch lsm_p_ncore  
#> 10 para   perimeter-area ratio         shape metric     patch lsm_p_para   
#> # ... with 122 more rows

# list only aggregation metrics at landscape level and just return function name
list_lsm(level = "landscape", 
         type = "aggregation metric", 
         simplify = TRUE)
#>  [1] "lsm_l_ai"       "lsm_l_cohesion" "lsm_l_contag"   "lsm_l_division"
#>  [5] "lsm_l_enn_cv"   "lsm_l_enn_mn"   "lsm_l_enn_sd"   "lsm_l_iji"     
#>  [9] "lsm_l_lsi"      "lsm_l_mesh"     "lsm_l_np"       "lsm_l_pd"      
#> [13] "lsm_l_pladj"    "lsm_l_split"

# you can also combine arguments and only return the function names
list_lsm(level = c("patch", "landscape"), 
         type = "core area metric", 
         simplify = TRUE)
#>  [1] "lsm_p_cai"      "lsm_p_core"     "lsm_p_ncore"    "lsm_l_cai_cv"  
#>  [5] "lsm_l_cai_mn"   "lsm_l_cai_sd"   "lsm_l_core_cv"  "lsm_l_core_mn" 
#>  [9] "lsm_l_core_sd"  "lsm_l_dcad"     "lsm_l_dcore_cv" "lsm_l_dcore_mn"
#> [13] "lsm_l_dcore_sd" "lsm_l_ndca"     "lsm_l_tca"

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

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.

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"))
#> # A tibble: 5 x 6
#>   layer level     class    id metric  value
#>   <int> <chr>     <int> <int> <chr>   <dbl>
#> 1     1 class         1    NA pland   19.9 
#> 2     1 class         2    NA pland   26.9 
#> 3     1 class         3    NA pland   53.2 
#> 4     1 landscape    NA    NA ta       0.09
#> 5     1 landscape    NA    NA te     364

# calculate all aggregation metrics on patch and landscape level
calculate_lsm(landscape, 
              type = "aggregation metric", 
              level = c("patch", "landscape"))
#> # A tibble: 41 x 6
#>    layer level     class    id metric     value
#>    <int> <chr>     <int> <int> <chr>      <dbl>
#>  1     1 landscape    NA    NA ai       81.1   
#>  2     1 landscape    NA    NA cohesion 95.4   
#>  3     1 landscape    NA    NA contag   25.2   
#>  4     1 landscape    NA    NA division  0.696 
#>  5     1 landscape    NA    NA enn_cv   39.8   
#>  6     1 landscape    NA    NA enn_mn    3.18  
#>  7     1 landscape    NA    NA enn_sd    1.27  
#>  8     1 landscape    NA    NA iji      87.8   
#>  9     1 landscape    NA    NA lsi       3.97  
#> 10     1 landscape    NA    NA mesh      0.0273
#> # ... with 31 more rows

# show full information of all metrics
calculate_lsm(landscape, 
              what = c("lsm_c_pland", "lsm_l_ta", "lsm_l_te"),
              full_name = TRUE)
#> # A tibble: 5 x 9
#>   layer level   class    id metric  value name      type      function_name
#>   <int> <chr>   <int> <int> <chr>   <dbl> <chr>     <chr>     <chr>        
#> 1     1 class       1    NA pland   19.9  percenta~ area and~ lsm_c_pland  
#> 2     1 class       2    NA pland   26.9  percenta~ area and~ lsm_c_pland  
#> 3     1 class       3    NA pland   53.2  percenta~ area and~ lsm_c_pland  
#> 4     1 landsc~    NA    NA ta       0.09 total ar~ area and~ lsm_l_ta     
#> 5     1 landsc~    NA    NA te     364    total ed~ area and~ lsm_l_te