Objectives

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

  • Access and manipulate cell values

  • Generate new rasters using mathematical functions

  • Summarize rasters using global functions

  • Generate new rasters describing the spatial context of individual cells

Revisiting Projections

• project changes the entire coordinate reference system

library(terra)
a <- rast(ncols=40, nrows=40, xmin=-110, xmax=-90, ymin=40, ymax=60, 
          crs="+proj=longlat +datum=WGS84")
values(a) <- 1:ncell(a)
newcrs="+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +datum=WGS84"
b <- rast(ncols=94, nrows=124, xmin=-944881, xmax=935118, ymin=4664377, ymax=7144377, crs=newcrs)
w <- project(a, b)

origin(a)

[1] 0 0

origin(w)

[1] -4881.5  4377.0

ext(a)

SpatExtent : -110, -90, 40, 60 (xmin, xmax, ymin, ymax)

ext(w)

SpatExtent : -944881, 935118, 4664377, 7144377 (xmin, xmax, ymin, ymax)

res(a)

[1] 0.5 0.5

res(w)

[1] 19999.99 20000.00

Revisiting Projections

a <- rast(ncols=40, nrows=40, xmin=-110, xmax=-90, ymin=40, ymax=60, 
          crs="+proj=longlat +datum=WGS84")
values(a) <- 1:ncell(a)

b <- rast(ncols=94, nrows=124, xmin=-111, xmax=-80, ymin=45, ymax=65)
w <- resample(a, b)

origin(a)

[1] 0 0

origin(b)

[1] 0.1382979 0.0000000

origin(w)

[1] 0.1382979 0.0000000

res(a)

[1] 0.5 0.5

res(b)

[1] 0.3297872 0.1612903

res(w)

[1] 0.3297872 0.1612903

Revisiting Projections

• if origin and extent are the same consider using aggregate, disagg, extend or crop

Accessing Cell Values

• We can extract or change cell values using []

a <- rast(ncols=10, nrows=10, xmin=-110, xmax=-100, ymin=40, ymax=50, 
          crs="+proj=longlat +datum=WGS84")
values(a) <- 1
b1 <- a
b2 <- a
b1[5,5] <- 10
b2[1, 1:10 ] <- runif(10,4,10)

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

Cell-wise operations

r <- rast(ncols=10, nrows=10)
values(r) <- 1:ncell(r)
f <- function(i) (i+1) * 2 * i + sqrt(i)
s <- app(r, f)

Cell-wise Operations

s <- rast(system.file("ex/logo.tif", package="terra")) + 1  
ss <- s[[2:1]]

fvi <- function(x, y){ (x - y ) / (x + y) } 
x <- lapp(ss, fun=fvi)

Global Methods

• Provide summaries of 1 or more layers

• Use zonal to extract values from one layer based on categorical layer

r <- rast(ncols=10, nrows=10)
values(r) <- 1:ncell(r)
z <- rast(r)
values(z) <- rep(c(1:2, NA, 3:4), each=20)
names(z) <- "zone"
a <-  zonal(r, z, "sum", na.rm=TRUE)
b <- zonal(r, z, "sum", na.rm=TRUE, as.raster=TRUE)
plot(b)

Context-specific Functions

• distance and relatives are based on relationships between cells

• focal provides moving windows for smoothing data

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

• shade calculates hillshade based on terrain

Using focal

• focal requires a window (w) or weights matrix

• na.policy determines how to deal with NAs in the smoother

• fillvalue and expand tell terra what to do at the edges

r <- rast(ncols=10, nrows=10, ext(0, 10, 0, 10))
values(r) <- 1:ncell(r)

f <- focal(r, w=3, fun=function(x, ...) quantile(x, c(.25, .5, .75), ...), na.rm=TRUE) 
plot(f)

Using focal