Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----load_libraries_hidden, eval=TRUE, echo=FALSE, message=FALSE, results='hide'----
library(landscapemetrics)
library(terra)
library(dplyr)
# internal data needs to be read
landscape <- terra::rast(landscapemetrics::landscape)
augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd)
podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc)
## -----------------------------------------------------------------------------
# import raster
# for local file: rast("pathtoyourraster/raster.asc")
# ... or any other raster file type, geotiff, ...
# Check your landscape
check_landscape(landscape) # because CRS is unknown, not clear
check_landscape(podlasie_ccilc) # wrong units
check_landscape(augusta_nlcd) # everything is ok
## ----message=FALSE------------------------------------------------------------
# import raster
# for local file: rast("pathtoyourraster/raster.asc")
# ... or any other raster file type, geotiff, ...
# Calculate e.g. perimeter of all patches
lsm_p_perim(landscape)
## ----message=FALSE------------------------------------------------------------
# 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
## -----------------------------------------------------------------------------
# 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)
## ----message=FALSE------------------------------------------------------------
# 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
## ----message=FALSE------------------------------------------------------------
# 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
## ----message=FALSE------------------------------------------------------------
# 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)
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