Week 4: Discussion Section

Practice raster manipulations

Author

Allie Caughmam & Alessandra Vidal Meza

Published

November 25, 2025

Source Materials

The following materials are modified from Geocomputation with R by Robin Lovelace.

1. Get Started

  • Create a version-controlled R Project
  • Create a Quarto document
Warning

If you don’t have spDataLarge installed, make sure to run:
install.packages('spDataLarge', repos='https://nowosad.github.io/drat/', type='source')

Let’s load all necessary packages:

library(tidyverse)
library(spData)
library(spDataLarge)
library(sf)
library(stars)
library(terra)

You will be working with the following datasets:

  • Digital elevation model (DEM) of Mt. Mongón, Perú, obtained from spDataLarge
  • Landsat image of Zion National Park, obtained from spDataLarge
  • SRTM elevation of Zion National Park, obtained from spDataLarge
dem <- terra::rast(system.file("raster/dem.tif", package = "spDataLarge"))
landsat <- terra::rast(system.file("raster/landsat.tif", package = "spDataLarge"))
srtm <- terra::rast(system.file("raster/srtm.tif", package = "spDataLarge"))

2. Explore elevation at Mt. Mongón, Perú

  • Make a boxplot and histogram of elevation at Mt. Mongón, Perú
hist(dem,
     main = "Digital Elevation Model Distribution",
     xlab = "Value")

boxplot(dem,
        main = "Digital Elevation Model Distribution",
        ylab = "Value")

  • Reclassify dem and compute the mean for the three classes:
    • Low, where elevation is less than 300
    • Medium
    • High, where elevation is greater than 500
# Define a reclassification matrix
rcl <- matrix(c(-Inf, 300, 0, # values -Inf to 300 = 0
                300, 500, 1,  # values 300 to 500 = 1
                500, Inf, 2), # values 500 to Inf = 2
              ncol = 3, byrow = TRUE)

# Apply the matrix to reclassify the raster, making all cells 0 or 1 or 2
dem_rcl <- terra::classify(dem, rcl = rcl)

# Assign labels to the numerical categories
levels(dem_rcl) <- tibble::tibble(id = 0:2, 
                                  cats = c("low", "medium", "high"))

# Calculate mean elevation for each category using original DEM values
elevation_mean <- terra::zonal(dem, dem_rcl, fun = "mean")
elevation_mean
    cats      dem
1    low 274.3910
2 medium 392.0486
3   high 765.2197

3. Explore NDVI and NDWI at Zion National Park

Landsat 8 bands 2-5 correspond to bands 1-4 for this raster. Bands are as follows:

Band Color
1 blue 30 meter
2 green 30 meter
3 red 30 meter
4 near-infrared 30 meter
Apply a scale factor and offset for all grid cells

Landsat Level-2 products are written as scaled integers to allow us to convert the data from floating point to integer for delivery. In most cases these are written to a 16-bit integer, which saves disk space and provides faster download times. Each floating point pixel has an offset applied and then multiplied by a gain to bring the value into the 16-bit integer (or unsigned integer) range. These values are referred to as scaled integers. To allow the user to get the data back to its original floating point value, a scale factor and offset are provided for each band.

Science product Scale factor Offset
Surface reflectance 0.0000275 -0.2

First, correct the scale across all grid cells and then apply the following functions at each grid cell, in the image:

\[\text{NDWI} = \frac{\text{(green - NIR)}}{\text{(green + NIR)}}\] \[\text{NDVI} = \frac{\text{(NIR - red)}}{\text{(NIR + red)}}\]

  • Calculate the Normalized Difference Vegetation Index (NDVI) at Zion National Park
  • Calculate the Normalized Difference Water Index (NDWI) at Zion National Park
  • Find a correlation between NDVI and NDWI at Zion National Park
    • Hint: Explore the terra package to find a function that can help achieve this!
scale_factor <- 0.0000275
offset <- 0.2

scale_function <- function(x) {
  x * scale_factor + offset
}

landsat_scaled <- terra::app(landsat, fun = scale_function)
ndwi_fun <- function(green, nir){
    (green - nir)/(green + nir)
}

ndvi_fun <- function(nir, red){
  (nir - red)/(nir + red)
}
ndwi_rast <- terra::lapp(landsat_scaled[[c(2, 4)]],
                         fun = ndwi_fun)

ndvi_rast <- terra::lapp(landsat_scaled[[c(4, 3)]],
                         fun = ndvi_fun)
plot(ndwi_rast,
     main = "Zion National Park NDWI")

plot(ndvi_rast,
     main = "Zion National Park NDVI")

combine <- c(ndvi_rast, ndwi_rast) # Stack rasters

plot(combine, main = c("NDVI", "NDWI")) # Plot

# Calculate correlation between raster layers 
terra::layerCor(combine, fun = cor)
           [,1]       [,2]
[1,]  1.0000000 -0.9062336
[2,] -0.9062336  1.0000000

4. Change resolution of elevation at Zion National Park

  • Use all the methods available to change the resolution of elevation at Zion National Park to 0.01 by 0.01 degrees
    • Note: The srtm raster has a resolution of 0.00083 by 0.00083 degrees
plot(srtm)

rast_template <- terra::rast(terra::ext(srtm), res = 0.01) # Create empty template
srtm_resampl1 <- terra::resample(srtm, y = rast_template, method = "bilinear")
srtm_resampl2 <- terra::resample(srtm, y = rast_template, method = "near")
srtm_resampl3 <- terra::resample(srtm, y = rast_template, method = "cubic")
srtm_resampl4 <- terra::resample(srtm, y = rast_template, method = "cubicspline")
srtm_resampl5 <- terra::resample(srtm, y = rast_template, method = "lanczos")
srtm_resampl_all <- c(srtm_resampl1, srtm_resampl2, srtm_resampl3, srtm_resampl4, srtm_resampl5)
labs <- c("Bilinear", "Near", "Cubic", "Cubic Spline", "Lanczos")

plot(srtm_resampl_all, main = labs)