Imagery Data

Images are Rasters

  • The spatial raster data model represents the world with the continuous grid of cells (a.k.a. pixels)
  • This data model often refers to so-called regular grids, in which each cell has the same, constant size
  • Through its inherent model this data is naturally fits into the wide data structure
Figure 2.1: Cell offset from origin (lower left)
Figure 2.2: Cell values (for example elevation)
Figure 2.3: Color representation of cell value

We will focus on the regular grids only. However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see Chapter 1 of Pebesma and Bivand (2023)).

Types of raster data

  • Raster datasets usually represent continuous phenomena such as elevation, temperature, population density or spectral data.
  • Discrete features such as soil or land-cover classes can also be represented in the raster data model

A simple example: Elevation

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
 [1,]  275  282  373  342  357  326  372  318  400   243   303
 [2,]  230  318  316  351  345  346  359  331  395   225   288
 [3,]  164  337  258  342  363  350  349  320  395   280   321
 [4,]  168  337  261  354  358  364  339  377  368   309   284
 [5,]  202  322  250  380  362  373  327  393  360   379   326
 [6,]   NA  310  270  361  370  363  368  368  385   383   297
 [7,]   NA  277  310  291  375  365  375  355  343   407   220
 [8,]   NA  181  325  264  381  373  389  341  305   395   252
 [9,]   NA   NA  313  264  370  384  392  328  357   376   289
[10,]   NA   NA  298  285  370  380  386  354  349   385   311
[11,]  402   NA  333  293  356  382  376  391  329   352   361

A more complex example: Spectral data

  • Typically, RS imagery consists of more than 1 band
  • In this case, the data is stored in a 3 dimensional array (where band is the 3rd-dimesion)
  • A RS image can contain any number of bands.
  • The most well known type of RS imagery consists of 3 Bands from the red, blue and green spectrum
Figure 2.4: The bands stored internally
Figure 2.5: The RGB Composite

Each band is a 2D matrix

Figure 2.6: Band values
Figure 2.7: RGB True Color Image

Multispectral Datasets

  • Multiband datasets usually capture different parts of the EM spectrum
  • E.g. the Landsat image from the previous example has 6 bands capturing the following wavelengths:
    • Band 1: Blue (0.45 - 0.52 µm)
    • Band 2: Green (0.52 - 0.60 µm)
    • Band 3: Red (0.63 - 0.69 µm)
    • Band 4: Near-Infrared (0.77 - 0.90 µm)
    • Band 5: Short-wave Infrared (1.55 - 1.75 µm)
    • Band 7: Mid-Infrared (2.08 - 2.35 µm)

NirGB Image

Figure 2.8: Band values
Figure 2.9: NIR-GB False Color Image

Representations of multispectral data

  • A true color image is created by using the Red (3), Green (2) and Blue (1) Band and mapping these to RGB
  • A false color image is created by mapping other bands to RGB
Figure 2.10: The R, G and B bands mapped to RGB
Figure 2.11: NIR, G and B bands mapped to RGB

Tasks / Exercises

  1. The following command returns the path to a tif file on your hard drive:

    system.file("ex/elev.tif", package="terra")

    Use this path to import the tif file using rast(), store it as r.

  2. Explore this object:

    • Determine the minimum and maximum eleveation values
    • Make a static map using base plot and tmap
    • Make an interactive map using tmap (tmap_mode("view"))
    • Using tmap, explore different styles and palettes.

  3. The following command returns the path to a tif file on your hard drive:

    system.file("tif/L7_ETMs.tif",package = "stars")

    Use this path to import the tif file using rast(), store it as l7.

  4. Explore this object:

    • Spot the differences to the object r
    • Plot the available layers individually
    • Rename the layers to: c("B", "G", "R", "NIR", "SWIR", "MIR") (see here)

  5. Select the Red Green and Blue bands to create a true color map (plotRGB and tm_rgb)

  1. Select the NIR, Green and Blue bands to create a false color composite