Work With Multi-Band Rasters

Overview

Teaching: 15 min
Exercises: 5 min

Questions

• How can I visualize individual and multiple bands in a raster object?

Objectives

• Identify a single vs. a multi-band raster file.

• Import multi-band rasters into R using the raster package.

• Plot multi-band color image rasters in R using the ggplot package.

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

We introduced multi-band raster data in an earlier lesson. This episode explores how to import and plot a multi-band raster in R.

Getting Started with Multi-Band Data in R

In this episode, the multi-band data that we are working with is imagery collected using the NEON Airborne Observation Platform high resolution camera over the NEON Harvard Forest field site. Each RGB image is a 3-band raster. The same steps would apply to working with a multi-spectral image with 4 or more bands - like Landsat imagery.

If we read a RasterStack object into R using the raster() function, it only reads in the first band.

RGB_band1_HARV <- raster("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif")

We need to convert this data to a data frame in order to plot it with ggplot.

RGB_band1_HARV_df  <- as.data.frame(RGB_band1_HARV, xy = TRUE)

ggplot() +
  geom_raster(data = RGB_band1_HARV_df,
              aes(x = x, y = y, alpha = HARV_RGB_Ortho)) + 
  coord_quickmap()

Challenge

View the attributes of this band. What are its dimensions, CRS, resolution, min and max values, and band number?

Solution

RGB_band1_HARV

class      : RasterLayer 
band       : 1  (of  3  bands)
dimensions : 2317, 3073, 7120141  (nrow, ncol, ncell)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
source     : HARV_RGB_Ortho.tif 
names      : HARV_RGB_Ortho 
values     : 0, 255  (min, max)

Notice that when we look at the attributes of this band, we see: band: 1 (of 3 bands)

This is R telling us that this particular raster object has more bands (3) associated with it.

Data Tip

The number of bands associated with a raster object can also be determined using the nbands() function: syntax is nbands(RGB_band1_HARV).

Image Raster Data Values

As we saw in the previous exercise, this raster contains values between 0 and 255. These values represent degrees of brightness associated with the image band. In the case of a RGB image (red, green and blue), band 1 is the red band. When we plot the red band, larger numbers (towards 255) represent pixels with more red in them (a strong red reflection). Smaller numbers (towards 0) represent pixels with less red in them (less red was reflected). To plot an RGB image, we mix red + green + blue values into one single color to create a full color image - similar to the color image a digital camera creates.

Import A Specific Band

We can use the raster() function to import specific bands in our raster object by specifying which band we want with band = N (N represents the band number we want to work with). To import the green band, we would use band = 2.

RGB_band2_HARV <-  raster("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif", band = 2)

We can convert this data to a data frame and plot the same way we plotted the red band:

RGB_band2_HARV_df <- as.data.frame(RGB_band2_HARV, xy = TRUE)

ggplot() +
  geom_raster(data = RGB_band2_HARV_df,
              aes(x = x, y = y, alpha = HARV_RGB_Ortho)) + 
  coord_equal()

Challenge: Making Sense of Single Band Images

Compare the plots of band 1 (red) and band 2 (green). Is the forested area darker or lighter in band 2 (the green band) compared to band 1 (the red band)?

Solution

We’d expect a brighter value for the forest in band 2 (green) than in band 1 (red) because the leaves on trees of most often appear “green” - healthy leaves reflect MORE green light than red light.

Raster Stacks in R

Next, we will work with all three image bands (red, green and blue) as an R RasterStack object. We will then plot a 3-band composite, or full color, image.

To bring in all bands of a multi-band raster, we use thestack() function.

RGB_stack_HARV <- stack("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif")

Let’s preview the attributes of our stack object:

RGB_stack_HARV

class      : RasterStack 
dimensions : 2317, 3073, 7120141, 3  (nrow, ncol, ncell, nlayers)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
names      : HARV_RGB_Ortho.1, HARV_RGB_Ortho.2, HARV_RGB_Ortho.3 
min values :                0,                0,                0 
max values :              255,              255,              255 

We can view the attributes of each band in the stack in a single output:

RGB_stack_HARV@layers

[[1]]
class      : RasterLayer 
band       : 1  (of  3  bands)
dimensions : 2317, 3073, 7120141  (nrow, ncol, ncell)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
source     : HARV_RGB_Ortho.tif 
names      : HARV_RGB_Ortho.1 
values     : 0, 255  (min, max)


[[2]]
class      : RasterLayer 
band       : 2  (of  3  bands)
dimensions : 2317, 3073, 7120141  (nrow, ncol, ncell)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
source     : HARV_RGB_Ortho.tif 
names      : HARV_RGB_Ortho.2 
values     : 0, 255  (min, max)


[[3]]
class      : RasterLayer 
band       : 3  (of  3  bands)
dimensions : 2317, 3073, 7120141  (nrow, ncol, ncell)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
source     : HARV_RGB_Ortho.tif 
names      : HARV_RGB_Ortho.3 
values     : 0, 255  (min, max)

If we have hundreds of bands, we can specify which band we’d like to view attributes for using an index value:

RGB_stack_HARV[[2]]

class      : RasterLayer 
band       : 2  (of  3  bands)
dimensions : 2317, 3073, 7120141  (nrow, ncol, ncell)
resolution : 0.25, 0.25  (x, y)
extent     : 731998.5, 732766.8, 4712956, 4713536  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
source     : HARV_RGB_Ortho.tif 
names      : HARV_RGB_Ortho.2 
values     : 0, 255  (min, max)

We can also use the ggplot functions to plot the data in any layer of our RasterStack object. Remember, we need to convert to a data frame first.

RGB_stack_HARV_df  <- as.data.frame(RGB_stack_HARV, xy = TRUE)

Each band in our RasterStack gets its own column in the data frame. Thus we have:

str(RGB_stack_HARV_df)

'data.frame':	7120141 obs. of  5 variables:
 $ x               : num  731999 731999 731999 731999 732000 ...
 $ y               : num  4713535 4713535 4713535 4713535 4713535 ...
 $ HARV_RGB_Ortho.1: num  0 2 6 0 16 0 0 6 1 5 ...
 $ HARV_RGB_Ortho.2: num  1 0 9 0 5 0 4 2 1 0 ...
 $ HARV_RGB_Ortho.3: num  0 10 1 0 17 0 4 0 0 7 ...

Let’s create a histogram of the first band:

ggplot() +
  geom_histogram(data = RGB_stack_HARV_df, aes(HARV_RGB_Ortho.1))

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

And a raster plot of the second band:

ggplot() +
  geom_raster(data = RGB_stack_HARV_df,
              aes(x = x, y = y, alpha = HARV_RGB_Ortho.2)) + 
  coord_quickmap()

We can access any individual band in the same way.

Create A Three Band Image

To render a final three band, colored image in R, we use the plotRGB() function.

This function allows us to:

1. Identify what bands we want to render in the red, green and blue regions. The plotRGB() function defaults to a 1=red, 2=green, and 3=blue band order. However, you can define what bands you’d like to plot manually. Manual definition of bands is useful if you have, for example a near-infrared band and want to create a color infrared image.
2. Adjust the stretch of the image to increase or decrease contrast.

Let’s plot our 3-band image. Note that we can use the plotRGB() function directly with our RasterStack object (we don’t need a dataframe as this function isn’t part of the ggplot2 package).

plotRGB(RGB_stack_HARV,
        r = 1, g = 2, b = 3)

The image above looks pretty good. We can explore whether applying a stretch to the image might improve clarity and contrast using stretch="lin" or stretch="hist".

When the range of pixel brightness values is closer to 0, a darker image is rendered by default. We can stretch the values to extend to the full 0-255 range of potential values to increase the visual contrast of the image.

When the range of pixel brightness values is closer to 255, a lighter image is rendered by default. We can stretch the values to extend to the full 0-255 range of potential values to increase the visual contrast of the image.

plotRGB(RGB_stack_HARV,
        r = 1, g = 2, b = 3,
        scale = 800,
        stretch = "lin")

plotRGB(RGB_stack_HARV,
        r = 1, g = 2, b = 3,
        scale = 800,
        stretch = "hist")

In this case, the stretch doesn’t enhance the contrast our image significantly given the distribution of reflectance (or brightness) values is distributed well between 0 and 255.

Challenge - NoData Values

Let’s explore what happens with NoData values when working with RasterStack objects and using the plotRGB() function. We will use the HARV_Ortho_wNA.tif GeoTIFF file in the NEON-DS-Airborne-Remote-Sensing/HARVRGB_Imagery/ directory.

1. View the files attributes. Are there NoData values assigned for this file?
2. If so, what is the NoData Value?
3. How many bands does it have?
4. Load the multi-band raster file into R.
5. Plot the object as a true color image.
6. What happened to the black edges in the data?
7. What does this tell us about the difference in the data structure between HARV_Ortho_wNA.tif and HARV_RGB_Ortho.tif (R object RGB_stack). How can you check?

Answers

1) First we use the GDALinfo() function to view the data attributes.

GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_Ortho_wNA.tif")

rows        2317 
columns     3073 
bands       3 
lower left origin.x        731998.5 
lower left origin.y        4712956 
res.x       0.25 
res.y       0.25 
ysign       -1 
oblique.x   0 
oblique.y   0 
driver      GTiff 
projection  +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
file        data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_Ortho_wNA.tif 
apparent band summary:
   GDType hasNoDataValue NoDataValue blockSize1 blockSize2
1 Float64           TRUE       -9999          1       3073
2 Float64           TRUE       -9999          1       3073
3 Float64           TRUE       -9999          1       3073
apparent band statistics:
  Bmin Bmax     Bmean      Bsd
1    0  255 107.83651 30.01918
2    0  255 130.09595 32.00168
3    0  255  95.75979 16.57704
Metadata:
AREA_OR_POINT=Area 

2) From the output above, we see that there are NoData values and they are assigned the value of -9999.

3) The data has three bands.

4) To read in the file, we will use the stack() function:

HARV_NA <- stack("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_Ortho_wNA.tif")

5) We can plot the data with the plotRGB() function:

plotRGB(HARV_NA,
        r = 1, g = 2, b = 3)

6) The black edges are not plotted. 7) Both data sets have NoData values, however, in the RGB_stack the NoData value is not defined in the tiff tags, thus R renders them as black as the reflectance values are 0. The black edges in the other file are defined as -9999 and R renders them as NA.

GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif")

rows        2317 
columns     3073 
bands       3 
lower left origin.x        731998.5 
lower left origin.y        4712956 
res.x       0.25 
res.y       0.25 
ysign       -1 
oblique.x   0 
oblique.y   0 
driver      GTiff 
projection  +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs 
file        data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif 
apparent band summary:
   GDType hasNoDataValue NoDataValue blockSize1 blockSize2
1 Float64           TRUE   -1.7e+308          1       3073
2 Float64           TRUE   -1.7e+308          1       3073
3 Float64           TRUE   -1.7e+308          1       3073
apparent band statistics:
  Bmin Bmax Bmean Bsd
1    0  255   NaN NaN
2    0  255   NaN NaN
3    0  255   NaN NaN
Metadata:
AREA_OR_POINT=Area 

Data Tip

We can create a RasterStack from several, individual single-band GeoTIFFs too. We will do this in a later episode, Raster Time Series Data in R.

RasterStack vs RasterBrick in R

The R RasterStack and RasterBrick object types can both store multiple bands. However, how they store each band is different. The bands in a RasterStack are stored as links to raster data that is located somewhere on our computer. A RasterBrick contains all of the objects stored within the actual R object. In most cases, we can work with a RasterBrick in the same way we might work with a RasterStack. However a RasterBrick is often more efficient and faster to process - which is important when working with larger files.

More Resources

You can read the help for the brick() function by typing ?brick.

We can turn a RasterStack into a RasterBrick in R by using brick(StackName). Let’s use the object.size() function to compare RasterStack and RasterBrick objects. First we will check the size of our RasterStack object:

object.size(RGB_stack_HARV)

50360 bytes

Now we will create a RasterBrick object from our RasterStack data and view its size:

RGB_brick_HARV <- brick(RGB_stack_HARV)

object.size(RGB_brick_HARV)

170898672 bytes

Notice that in the RasterBrick, all of the bands are stored within the actual object. Thus, the RasterBrick object size is much larger than the RasterStack object.

You use the plotRGB() function to plot a RasterBrick too:

plotRGB(RGB_brick_HARV)

Challenge: What Functions Can Be Used on an R Object of a particular class?

We can view various functions (or methods) available to use on an R object with methods(class=class(objectNameHere)). Use this to figure out:

1. What methods can be used on the RGB_stack_HARV object?
2. What methods can be used on a single band within RGB_stack_HARV?
3. Why do you think there is a difference?

Answers

1) We can see a list of all of the methods available for our RasterStack object:

methods(class=class(RGB_stack_HARV))

  [1] !                     !=                    [                    
  [4] [[                    [[<-                  [<-                  
  [7] %in%                  ==                    $                    
 [10] $<-                   addLayer              adjacent             
 [13] aggregate             all.equal             animate              
 [16] approxNA              area                  Arith                
 [19] as.array              as.character          as.data.frame        
 [22] as.integer            as.list               as.logical           
 [25] as.matrix             as.vector             atan2                
 [28] bbox                  boxplot               brick                
 [31] calc                  cellFromRowCol        cellFromRowColCombine
 [34] cellFromXY            cellStats             clamp                
 [37] click                 coerce                colFromCell          
 [40] colFromX              colSums               Compare              
 [43] coordinates           corLocal              couldBeLonLat        
 [46] cover                 crop                  crosstab             
 [49] crs<-                 cut                   cv                   
 [52] density               dim                   dim<-                
 [55] disaggregate          dropLayer             extend               
 [58] extent                extract               flip                 
 [61] freq                  getValues             getValuesBlock       
 [64] getValuesFocal        hasValues             head                 
 [67] hist                  image                 init                 
 [70] inMemory              interpolate           intersect            
 [73] is.factor             is.finite             is.infinite          
 [76] is.na                 is.nan                isLonLat             
 [79] KML                   labels                length               
 [82] levels                levels<-              log                  
 [85] Logic                 mask                  match                
 [88] Math                  Math2                 maxValue             
 [91] mean                  merge                 metadata             
 [94] minValue              modal                 mosaic               
 [97] names                 names<-               ncell                
[100] ncol                  ncol<-                nlayers              
[103] nrow                  nrow<-                origin               
[106] origin<-              overlay               pairs                
[109] persp                 plot                  plotRGB              
[112] predict               print                 proj4string          
[115] proj4string<-         quantile              raster               
[118] rasterize             ratify                readAll              
[121] readStart             readStop              reclassify           
[124] rectify               res                   res<-                
[127] resample              rotate                rowColFromCell       
[130] rowFromCell           rowFromY              rowSums              
[133] sampleRandom          sampleRegular         scale                
[136] select                setMinMax             setValues            
[139] shift                 show                  spplot               
[142] stack                 stackSelect           stretch              
[145] subs                  subset                Summary              
[148] summary               t                     tail                 
[151] text                  trim                  unique               
[154] unstack               values                values<-             
[157] weighted.mean         which.max             which.min            
[160] whiches.max           whiches.min           wkt                  
[163] writeRaster           xFromCell             xFromCol             
[166] xmax                  xmax<-                xmin                 
[169] xmin<-                xres                  xyFromCell           
[172] yFromCell             yFromRow              ymax                 
[175] ymax<-                ymin                  ymin<-               
[178] yres                  zonal                 zoom                 
see '?methods' for accessing help and source code

2) And compare that with the methods available for a single band:

methods(class=class(RGB_stack_HARV[1]))

Warning in .S3methods(generic.function, class, envir): 'class' is of length > 1;
only the first element will be used

 [1] [             [<-           anyDuplicated as_tibble     as.data.frame
 [6] as.raster     bind          boxplot       brick         cellFromXY   
[11] coerce        coordinates   determinant   distance      duplicated   
[16] edit          extent        extract       head          initialize   
[21] isSymmetric   Math          Math2         Ops           raster       
[26] rasterize     relist        subset        summary       surfaceArea  
[31] tail          trim          unique        values<-      weighted.mean
[36] writeValues  
see '?methods' for accessing help and source code

3) There are far more things one could or want to ask of a full stack than of a single band.

Key Points

• A single raster file can contain multiple bands or layers.

• Use the stack() function to load all bands in a multi-layer raster file into R.

• Individual bands within a stack can be accessed, analyzed, and visualized using the same functions as single bands.