Nothing
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(landscapetools)
## ----fig.retina=2, message=FALSE, warning=FALSE--------------------------
# Plot continous landscapes
show_landscape(gradient_landscape)
# Plot continous landscapes
show_landscape(classified_landscape, discrete = TRUE)
# RasterStack/RasterBrick
show_landscape(raster::stack(gradient_landscape, random_landscape), discrete = TRUE)
# Plot a list of raster (list names become facet text)
show_landscape(list("Gradient landscape" = gradient_landscape,
"Random landscape" = random_landscape))
# Plot multiple raster with unique scales
show_landscape(raster::stack(gradient_landscape, random_landscape, classified_landscape), unique_scales = TRUE)
## ----fig.retina=2--------------------------------------------------------
# Binarize the landscape into habitat and matrix
binarized_raster <- util_binarize(fractal_landscape, breaks = 0.31415)
show_landscape(binarized_raster)
# You can also provide a vector with thresholds and get a RasterStack with multiple binarized maps
binarized_raster <- util_binarize(fractal_landscape, breaks = c(0.25, 0.5, 0.7))
show_landscape(binarized_raster)
## ----fig.retina=2--------------------------------------------------------
# Mode 1: Classify landscape into 3 classes based on the Fisher-Jenks algorithm:
mode_1 <- util_classify(fractal_landscape, n = 3)
# Mode 2: Classify landscapes into landscape with exact proportions:
mode_2 <- util_classify(fractal_landscape, weighting = c(0.5, 0.25, 0.25))
# Mode 3: Classify landscapes based on a real dataset (which we first create here)
# and the distribution of values in this real dataset
mode_3 <- util_classify(gradient_landscape, n = 3)
## Mode 3a: ... now we just have to provide the "real landscape" (mode_3)
mode_3a <- util_classify(fractal_landscape, real_land = mode_3)
## Mode 3b: ... and we can also say that certain values are not important for our classification:
mode_3b <- util_classify(fractal_landscape, real_land = mode_3, mask_val = 1)
landscapes <- list(
'Mode 1' = mode_1,
'Mode 2' = mode_2,
'Mode 3' = mode_3,
'Mode 3a' = mode_3a,
'Mode 3b' = mode_3b
)
show_landscape(landscapes, unique_scales = TRUE, nrow = 1)
# ... you can also name the classes:
classified_raster <- util_classify(fractal_landscape,
n = 3,
level_names = c("Land Use 1",
"Land Use 2",
"Land Use 3"))
show_landscape(classified_raster, discrete = TRUE)
## ----fig.retina=2--------------------------------------------------------
library(raster)
landscape <- raster(matrix(1:100, 10, 10))
summary(landscape)
scaled_landscape <- util_rescale(landscape)
summary(scaled_landscape)
## ----fig.retina=2--------------------------------------------------------
# Merge all maps into one
merg <- util_merge(fractal_landscape, c(gradient_landscape, random_landscape), scalingfactor = 1)
# Plot an overview
merge_vis <- list(
"1) Primary" = fractal_landscape,
"2) Secondary 1" = gradient_landscape,
"3) Secondary 2" = random_landscape,
"4) Result" = merg
)
show_landscape(merge_vis)
## ----eval=FALSE----------------------------------------------------------
# util_rescale(fractal_landscape, "fractal.asc")
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