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 |
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
#> Warning: Caution: Coordinate reference system not metric - Units of results
#> based on cellsizes and/or distances may be incorrect.
#> layer crs units class n_classes OK
#> 1 1 NA NA integer 3 ?⃝
check_landscape(landscapemetrics::podlasie_ccilc) # wrong units
#> Warning: Caution: Coordinate reference system not metric - Units of results
#> based on cellsizes and/or distances may be incorrect.
#> 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 ✓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).
landscapemetricsIf 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 × 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 12
#> 3 1 patch 1 3 perim 130
#> 4 1 patch 1 4 perim 4
#> 5 1 patch 1 5 perim 4
#> 6 1 patch 1 6 perim 20
#> 7 1 patch 1 7 perim 8
#> 8 1 patch 1 8 perim 10
#> 9 1 patch 1 9 perim 4
#> 10 1 patch 2 10 perim 38
#> # … with 17 more rowslandscapemetrics in a tidy workflowEvery 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 14 16 17 18 19 20 22 23To 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: 133 × 5
#> metric name type level function_name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 area patch area area and edge metric patch lsm_p_area
#> 2 cai core area index core area metric patch lsm_p_cai
#> 3 circle related circumscribing circle 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 distance aggregation metric patch lsm_p_enn
#> 7 frac fractal dimension index shape metric patch lsm_p_frac
#> 8 gyrate radius of gyration area and edge metric 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 123 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 × 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 0
#> 3 1 patch 1 3 cai 48.0
#> 4 1 patch 1 4 cai 0
#> 5 1 patch 1 5 cai 0
#> 6 1 patch 1 6 cai 14.3
#> 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 rowsAll 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 × 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 area index core… patch lsm_p_cai
#> 2 1 patch 1 2 cai 0 core area index core… patch lsm_p_cai
#> 3 1 patch 1 3 cai 48.0 core area index core… patch lsm_p_cai
#> 4 1 patch 1 4 cai 0 core area index core… patch lsm_p_cai
#> 5 1 patch 1 5 cai 0 core area index core… patch lsm_p_cai
#> 6 1 patch 1 6 cai 14.3 core area index core… patch lsm_p_cai
#> 7 1 patch 1 7 cai 0 core area index core… patch lsm_p_cai
#> 8 1 patch 1 8 cai 0 core area index core… patch lsm_p_cai
#> 9 1 patch 1 9 cai 0 core area index core… patch lsm_p_cai
#> 10 1 patch 2 10 cai 31.4 core area index core… patch lsm_p_cai
#> # … with 71 more rowsAdditionally, 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"))
#> Warning: Please use 'check_landscape()' to ensure the input data is valid.
#> # A tibble: 5 × 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"))
#> Warning: Please use 'check_landscape()' to ensure the input data is valid.
#> # A tibble: 41 × 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 4.03
#> 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)
#> Warning: Please use 'check_landscape()' to ensure the input data is valid.
#> # A tibble: 5 × 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 percentage of landscape area… lsm_c_pland
#> 2 1 class 2 NA pland 26.9 percentage of landscape area… lsm_c_pland
#> 3 1 class 3 NA pland 53.2 percentage of landscape area… lsm_c_pland
#> 4 1 landscape NA NA ta 0.09 total area area… lsm_l_ta
#> 5 1 landscape NA NA te 364 total edge area… lsm_l_te