Operations on Raster Data I

HES 505 Fall 2024: Session 12

Carolyn Koehn

Objectives

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

    • Align rasters for spatial processing

    • Adjust the resolution of raster data

    • Combine (or reduce) rasters to match the extent of your analysis

Aligning rasters for spatial processing

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
library(sf)
library(terra)
library(spDataLarge)
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
x <- rast(pr1)
# Set the cell size
res(x) <- 200000
pr3 <- terra::project(r, x)
plot(pr3)

Aligning Data: 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")

Adjusting resolution

Downscaling and Upscaling

  • Aligning data for later analysis

  • Remembering scale

  • Thinking about support

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()
r
class       : SpatRaster 
dimensions  : 180, 360, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) 
values(r) <- 1:ncell(r)
plot(r)

ra <- aggregate(r, 20)
ra
class       : SpatRaster 
dimensions  : 9, 18, 1  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) 
source(s)   : memory
name        :   lyr.1 
min value   :  3430.5 
max value   : 61370.5 
plot(ra)

Changing resolutions: disagg

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

rd <- disagg(r, 20)
rd
class       : SpatRaster 
dimensions  : 3600, 7200, 1  (nrow, ncol, nlyr)
resolution  : 0.05, 0.05  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) 
source      : spat_14dc31c8319c_5340.tif 
name        : lyr.1 
min value   :     1 
max value   : 64800 
plot(rd)

Modifying the Extent

Dealing with Different Extents

  • Raster extents often larger than our analysis

  • Reducing memory and computational resources

  • Making attractive maps

Using terra::crop()

  • Coordinate Reference System must be the same for both objects

  • Crop is based on the (converted) SpatExtent of the 2nd object

  • snap describes how y will be aligned to the raster

  • Returns all data within the extent

Using terra::crop()

library(sf)
library(terra)
library(spDataLarge)
srtm = rast(system.file("raster/srtm.tif", package = "spDataLarge"))
zion = read_sf(system.file("vector/zion.gpkg", package = "spDataLarge"))
zion = st_transform(zion, crs(srtm))

crs(srtm) == crs(zion)
[1] TRUE
srtm.crop <- crop(x=srtm, y=zion, snap="near")

Using mask()

  • Often want to get rid of all values outside of vector

  • Can set mask=TRUE in crop() (y must be SpatVector)

  • Or use mask()

srtm.crop.msk <- crop(x=srtm, y=vect(zion), snap="near", mask=TRUE)
plot(srtm.crop.msk)

srtm.msk <- mask(srtm.crop, vect(zion))
plot(srtm.msk)

Using mask()

  • Allows more control over what the mask does

  • Can set maskvalues and updatevalues to change the resulting raster

  • Can also use inverse to mask out the vector

srtm.msk <- mask(srtm.crop, vect(zion), updatevalue=-1000)
plot(srtm.msk)

srtm.msk <- mask(srtm.crop, vect(zion), inverse=TRUE, updatevalue=0)
plot(srtm.msk)

Extending boundaries

  • Vector slightly larger than raster

  • Especially when using buffered datasets

  • Can use extend

  • Not exact; depends on snap()

zion.buff <-  zion %>% 
  st_buffer(., 10000)
srtm.ext <- extend(srtm, vect(zion.buff))
ext(srtm.ext)
SpatExtent : -113.343749879444, -112.74541654615, 37.0479167631968, 37.5979167631601 (xmin, xmax, ymin, ymax)
ext(vect(zion.buff))
SpatExtent : -113.343652923976, -112.745006809213, 37.0477357596604, 37.5977812137969 (xmin, xmax, ymin, ymax)

Practice

mosaic is a funciton that combines adjacent rasters, but they need to have the same origin and resolution. Let’s practice preparing some rasters for a mosaic.

  1. Load wildfire hazard data from the rasterexample folder for OR and ID: Copy of CRPS_OR.tif and Copy of CRPS_ID.tif.

  2. These rasters have a fine resolution that will make our calculations slow. Transform them to have a resolution of 900 m.

  3. Do the rasters have the same CRS, origin, resolution, and extent? Check this with ==.

  4. Use new functions from this lecture to align the properties mentioned in #3 (plot often to check your work). Why not use project?

  5. mosaic the two rasters together.