Operations With Raster Data I

HES 505 Fall 2022: Session 11

Matt Williamson

Today’s Plan

Image Source: USGS

Objectives

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

    • Evaluate logical conditions with raster data

    • Calculate different measures of raster data

    • Align rasters for spatial processing

Revisitng the Raster Data Model

  • Raster data represent spatially continuous phenomena (NA is possible)

  • Depict the alignment of data on a regular lattice (often a square)

    • Operations mimic those for matrix objects in R

  • Geometry is implicit; the spatial extent and number of rows and columns define the cell size

Predicates and measures in terra

Extending predicates

  • Predicates: evaluate a logical statement asserting that a property is TRUE

  • terra does not follow the same hierarchy as sf so a little trickier

Unary predicates in terra

  • Can tell us qualities of a raster dataset

  • Many similar operations for SpatVector class (note use of .)

predicate asks…
is.lonlat Does the object have a longitude/latitude CRS?
inMemory is the object stored in memory?
is.factor Are there categorical layers?
hasValues Do the cells have values?

Unary predicates in terra

  • global: tests if the raster covers all longitudes (from -180 to 180 degrees) such that the extreme columns are in fact adjacent
r <- rast()
is.lonlat(r)
[1] TRUE
is.lonlat(r, global=TRUE)
[1] TRUE
  • perhaps: If TRUE and the crs is unknown, the method returns TRUE if the coordinates are plausible for longitude/latitude
crs(r) <- ""
is.lonlat(r)
[1] NA
is.lonlat(r, perhaps=TRUE, warn=FALSE)
[1] TRUE
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
is.lonlat(r)
[1] FALSE

Binary predicates in terra

  • Take exactly 2 inputs, return 1 matrix of cell locs where value is TRUE

  • adjacent: identifies cells adajcent to a set of raster cells

Unary measures in terra

  • Slightly more flexible than sf

  • One result for each layer in a stack

measure returns
cellSize area of individual cells
expanse summed area of all cells
values returns all cell values
ncol number of columns
nrow number of rows
ncell number of cells
res resolution
ext minimum and maximum of x and y coords
origin the orgin of a SpatRaster
crs the coordinate reference system
cats categories of a categorical raster

Binary measures in terra

  • Returns a matrix or SpatRaster describing the measure
measure returns
distance shortest distance to non-NA or vector object
gridDistance shortest distance through adjacent grid cells
costDistance Shortest distance considering cell-varying friction
direction azimuth to cells that are not NA

Aligning rasters

Projecting raster data

  • Transformation from lat/long to planar CRS involves some loss of precision
  • New cell values estimated using overlap with original cells
  • Interpolation for continuous data, nearest neighbor for categorical data
  • Equal-area projections are preferred; especially for large areas
r <- rast(xmin=-110, xmax=-90, ymin=40, ymax=60, ncols=40, nrows=40)
values(r) <- 1:ncell(r)
plot(r)

Projecting raster data

  • simple method; alignment not guaranteed
newcrs <- "+proj=robin +datum=WGS84"
pr1 <- terra::project(r, newcrs)
plot(pr1)

  • providing a template to ensure alignment

Changing resolutions

  • aggregate, disaggregate, resample allow changes in cell size

  • aggregate requires a function (e.g., mean() or min()) to determine what to do with the grouped values

  • resample allows changes in cell size and shifting of cell centers (slower)

Changing resolutions: aggregate

r <- rast()
values(r) <- 1:ncell(r)
plot(r)

]

ra <- aggregate(r, 20)
plot(ra)

Changing resolutions: disagg

ra <- aggregate(r, 20)
plot(ra)

rd <- disagg(r, 20)
plot(rd)

Changing Resolutions: resample

r <- rast(nrow=3, ncol=3, xmin=0, xmax=10, ymin=0, ymax=10)
values(r) <- 1:ncell(r)
s <- rast(nrow=25, ncol=30, xmin=1, xmax=11, ymin=-1, ymax=11)
x <- resample(r, s, method="bilinear")

On Weds

  • Transformations of data and coverage
  • Raster math
  • Cell-based functions