Objectives

• By the end of today, you should be able to:

  • Use moving windows as a means of smoothing raster data

  • Reclassify data using conditional statements and reclassification tables

  • Use raster math as a means of creating new data based on an existing dataset.

Why use moving windows?

• To create new data that reflects “neighborhood” data

• To smooth out values

• To detect (and fill) holes or edges

• Change the thematic scale of your data (without changing resolution)

What is a moving window?

Implementing Moving Windows in R

• Use the focal function in terra

focal(x, w=3, fun="sum", ..., na.policy="all", fillvalue=NA, expand=FALSE, silent=TRUE, filename="", overwrite=FALSE, wopt=list())

focal for Continuous Rasters

library(tidyverse)
library(terra)
library(spData)
srtm = rast(system.file("raster/srtm.tif", package = "spDataLarge"))
srtm3  <-  focal(x = srtm, w = 3)
srtm9  <-  focal(x = srtm, w = 9)
srtm21  <-  focal(x = srtm, w = 21)

focal for Continuous Rasters

focal for Continuous Rasters

srtmsum  <-  focal(x = srtm, w = 3, fun="sum")
srtmmax  <-  focal(x = srtm, w = 9, fun="mean")
srtmmin  <-  focal(x = srtm, w = 21, fun="min")

focal for Continuous Rasters

focal for Continous Rasters

• can alter the size and shape of window by providing a weights matrix for w

• Can create different custom functions for fun (see the help file)

• na.policy for filling holes or avoiding them

Reclassification

• Create new data based on the presence of a particular class(es) of interest

• Combine classes in a categorical map

• Useful as inputs for overlay analyses

Reclassifying rasters in R

• Using [] and conditionals

srtm = rast(system.file("raster/srtm.tif", package = "spDataLarge"))
srtm.lowelev <- srtm
srtm.lowelev[srtm.lowelev > 2500] <- 1
plot(srtm.lowelev)

Reclassifying rasters in R

• Using [] and conditionals

srtm = rast(system.file("raster/srtm.tif", package = "spDataLarge"))


srtm.na <- srtm
srtm.na[200:300, 200:300] <- NA
srtm.na[is.na(srtm.na)] <- 8000
plot(srtm.na)

Reclassifying Categorical Rasters

• Need a classification matrix
• Use classify

mintemp <- rast("ftp://ftp.hafro.is/pub/data/rasters/Iceland_minbtemp.tif")
cm <- matrix(c(
  -2, 2, 0,
  2, 4, 1,
  4, 10, 2), ncol = 3, byrow = TRUE)

# Create a raster with integers
temp_reclass <- classify(mintemp, cm)
tempcats <- c("cold", "mild", "warm")
levels(temp_reclass) <- tempcats

Reclassifying Categorical Rasters

Raster Math

• Performs cell-wise calculations on 1 (or more) SpatRasters
• Generally works the same as matrix operations
• All layers must be aligned

Raster Math

r <- rast(ncol=5, nrow=5)
values(r) <- 1:ncell(r)
r2 <- r*2
r3 <- t(r)
r4 <- r + r2

Cell-wise operations

• terra has a special set of apply functions

• app, lapp, tapp

• app applies a function to the values of each cell

• lapp applies a function using the layer as the value

• tapp applies the function to a subset of layers

Context-specific Functions

• distance and relatives are based on relationships between cells

• terrain allows calculation of slope, ruggedness, aspect using elevation rasters

• shade calculates hillshade based on terrain