# 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. `enn`for 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.

    # 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
    #>  [1] 10 11 12 13 14 15 16 17 19 21 23

### 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:

    # 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
    #> # A tibble: 81 x 6
    #>    layer level class    id metric value
    #>    <int> <chr> <int> <int> <chr>  <dbl>
    #>  1     1 patch     1     1 cai      0
    #>  2     1 patch     1     2 cai     48.0
    #>  3     1 patch     1     3 cai      0
    #>  4     1 patch     1     4 cai     14.3
    #>  5     1 patch     1     5 cai      0
    #>  6     1 patch     1     6 cai      0
    #>  7     1 patch     1     7 cai      0
    #>  8     1 patch     1     8 cai      0
    #>  9     1 patch     1     9 cai      0
    #> 10     1 patch     2    10 cai     31.4
    #> # ... with 71 more rows

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
    #> # A tibble: 81 x 10
    #>    layer level.x class    id metric value name  type  level.y function_name
    #>    <int> <chr>   <int> <int> <chr>  <dbl> <chr> <chr> <chr>   <chr>
    #>  1     1 patch       1     1 cai      0   core~ core~ patch   lsm_p_cai
    #>  2     1 patch       1     2 cai     48.0 core~ core~ patch   lsm_p_cai
    #>  3     1 patch       1     3 cai      0   core~ core~ patch   lsm_p_cai
    #>  4     1 patch       1     4 cai     14.3 core~ core~ patch   lsm_p_cai
    #>  5     1 patch       1     5 cai      0   core~ core~ patch   lsm_p_cai
    #>  6     1 patch       1     6 cai      0   core~ core~ patch   lsm_p_cai
    #>  7     1 patch       1     7 cai      0   core~ core~ patch   lsm_p_cai
    #>  8     1 patch       1     8 cai      0   core~ core~ patch   lsm_p_cai
    #>  9     1 patch       1     9 cai      0   core~ core~ patch   lsm_p_cai
    #> 10     1 patch       2    10 cai     31.4 core~ core~ patch   lsm_p_cai
    #> # ... with 71 more rows

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