Read Single Band From Raster in Matlab
Piece of work With Multi-Band Rasters
Overview
Teaching: twoscore min
Exercises: 20 minQuestions
How tin can I visualize private and multiple bands in a raster object?
Objectives
Identify a single vs. a multi-ring raster file.
Import multi-band rasters into R using the
rasterbundle.Plot multi-band colour image rasters in R using the
ggplotpackage.
Things You'll Demand To Complete This Episode
See the lesson homepage for detailed information almost the software, data, and other prerequisites you volition 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-ring raster in R.
Getting Started with Multi-Ring Data in R
In this episode, the multi-band information 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 prototype is a 3-band raster. The same steps would apply to working with a multi-spectral prototype with iv or more bands - similar Landsat imagery.
If we read a RasterStack object into R using the raster() function, information technology 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 social club 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 = ten , y = y , blastoff = HARV_RGB_Ortho )) + coord_quickmap () 
Claiming
View the attributes of this ring. What are its dimensions, CRS, resolution, min and max values, and ring number?
Solution
grade : RasterLayer band : 1 (of 3 bands) dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell) resolution : 0.25, 0.25 (10, y) extent : 731998.5, 732766.eight, 4712956, 4713536 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=18 +datum=WGS84 +units=k +no_defs source : HARV_RGB_Ortho.tif names : HARV_RGB_Ortho values : 0, 255 (min, max)Notice that when we await at the attributes of this band, nosotros see:
band: 1 (of 3 bands)This is R telling us that this item 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 isnbands(RGB_band1_HARV).
Paradigm Raster Data Values
Equally 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, greenish and blue), band 1 is the ruby band. When we plot the red ring, larger numbers (towards 255) stand for pixels with more red in them (a potent carmine reflection). Smaller numbers (towards 0) represent pixels with less red in them (less cherry-red was reflected). To plot an RGB image, we mix red + green + blue values into one single color to create a full color image - like 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 nosotros want with ring = Northward (North represents the band number we want to piece of work with). To import the light-green band, we would use ring = 2.
RGB_band2_HARV <- raster ( "information/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif" , band = 2 ) Nosotros can convert this data to a data frame and plot the same style nosotros plotted the red band:
RGB_band2_HARV_df <- every bit.data.frame ( RGB_band2_HARV , xy = Truthful ) ggplot () + geom_raster ( data = RGB_band2_HARV_df , aes ( ten = x , y = y , alpha = HARV_RGB_Ortho )) + coord_equal () 
Challenge: Making Sense of Single Band Images
Compare the plots of ring 1 (red) and ring 2 (green). Is the forested area darker or lighter in band 2 (the light-green band) compared to ring 1 (the cherry-red band)?
Solution
We'd expect a brighter value for the forest in ring two (green) than in band 1 (red) because the leaves on trees of most oftentimes appear "greenish" - good for you leaves reverberate MORE green light than red low-cal.
Raster Stacks in R
Next, we will work with all three image bands (red, green and blue) equally an R RasterStack object. Nosotros will then plot a 3-band composite, or full colour, image.
To bring in all bands of a multi-band raster, nosotros use thestack() office.
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:
form : RasterStack dimensions : 2317, 3073, 7120141, 3 (nrow, ncol, ncell, nlayers) resolution : 0.25, 0.25 (x, y) extent : 731998.five, 732766.8, 4712956, 4713536 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=18 +datum=WGS84 +units=thousand +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 tin can view the attributes of each ring in the stack in a single output:
[[1]] class : RasterLayer band : 1 (of three bands) dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell) resolution : 0.25, 0.25 (10, y) extent : 731998.5, 732766.8, 4712956, 4713536 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=18 +datum=WGS84 +units=1000 +no_defs source : HARV_RGB_Ortho.tif names : HARV_RGB_Ortho.1 values : 0, 255 (min, max) [[two]] class : RasterLayer band : ii (of 3 bands) dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell) resolution : 0.25, 0.25 (x, y) extent : 731998.5, 732766.viii, 4712956, 4713536 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=xviii +datum=WGS84 +units=k +no_defs source : HARV_RGB_Ortho.tif names : HARV_RGB_Ortho.2 values : 0, 255 (min, max) [[three]] form : RasterLayer band : iii (of iii bands) dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell) resolution : 0.25, 0.25 (x, y) extent : 731998.five, 732766.viii, 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 nosotros take hundreds of bands, nosotros can specify which band nosotros'd like to view attributes for using an index value:
course : RasterLayer band : 2 (of iii bands) dimensions : 2317, 3073, 7120141 (nrow, ncol, ncell) resolution : 0.25, 0.25 (x, y) extent : 731998.5, 732766.eight, 4712956, 4713536 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=xviii +datum=WGS84 +units=chiliad +no_defs source : HARV_RGB_Ortho.tif names : HARV_RGB_Ortho.2 values : 0, 255 (min, max) We tin can also use the ggplot functions to plot the data in any layer of our RasterStack object. Recall, we need to convert to a data frame offset.
RGB_stack_HARV_df <- as.data.frame ( RGB_stack_HARV , xy = True ) Each band in our RasterStack gets its own cavalcade in the data frame. Thus we have:
'data.frame': 7120141 obs. of 5 variables: $ ten : num 731999 731999 731999 731999 732000 ... $ y : num 4713535 4713535 4713535 4713535 4713535 ... $ HARV_RGB_Ortho.ane: num 0 2 6 0 sixteen 0 0 6 one 5 ... $ HARV_RGB_Ortho.2: num 1 0 nine 0 5 0 4 ii i 0 ... $ HARV_RGB_Ortho.3: num 0 10 1 0 17 0 four 0 0 7 ... Allow'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 = thirty`. Choice meliorate 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.ii )) + coord_quickmap () 
Nosotros tin can admission whatsoever individual band in the same mode.
Create A Three Band Paradigm
To render a concluding iii band, colored paradigm in R, we use the plotRGB() function.
This part allows u.s. to:
- Identify what bands we want to render in the red, dark-green and blueish regions. The
plotRGB()role defaults to a 1=red, two=dark-green, and three=blue band order. Notwithstanding, you can ascertain what bands you'd like to plot manually. Manual definition of bands is useful if you accept, for example a nigh-infrared band and want to create a color infrared image. - Adjust the
stretchof the paradigm to increase or decrease dissimilarity.
Let'south plot our 3-band image. Note that nosotros can apply the plotRGB() part directly with our RasterStack object (we don't need a dataframe every bit this role isn't part of the ggplot2 bundle).
plotRGB ( RGB_stack_HARV , r = 1 , one thousand = ii , b = iii ) 
The image above looks pretty good. We can explore whether applying a stretch to the image might ameliorate 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 tin can stretch the values to extend to the full 0-255 range of potential values to increase the visual contrast of the prototype.
When the range of pixel brightness values is closer to 255, a lighter epitome is rendered past 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 = i , g = two , b = 3 , scale = 800 , stretch = "lin" ) 
plotRGB ( RGB_stack_HARV , r = 1 , g = two , b = 3 , scale = 800 , stretch = "hist" ) 
In this example, the stretch doesn't raise the contrast our image significantly given the distribution of reflectance (or brightness) values is distributed well between 0 and 255.
Claiming - NoData Values
Let's explore what happens with NoData values when working with RasterStack objects and using the
plotRGB()role. We will utilize theHARV_Ortho_wNA.tifGeoTIFF file in theNEON-DS-Airborne-Remote-Sensing/HARVRGB_Imagery/directory.
- View the files attributes. Are there
NoDatavalues assigned for this file?- If so, what is the
NoDataValue?- How many bands does it have?
- Load the multi-band raster file into R.
- Plot the object as a true colour image.
- What happened to the black edges in the data?
- What does this tell usa near the departure in the data structure between
HARV_Ortho_wNA.tifandHARV_RGB_Ortho.tif(R objectRGB_stack). How can you bank check?Answers
ane) Commencement we use the
GDALinfo()part to view the information attributes.GDALinfo ( "information/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_Ortho_wNA.tif" )rows 2317 columns 3073 bands three lower left origin.ten 731998.v 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=eighteen +datum=WGS84 +units=grand +no_defs file information/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 two Float64 Truthful -9999 1 3073 3 Float64 Truthful -9999 ane 3073 apparent band statistics: Bmin Bmax Bmean Bsd 1 0 255 107.83651 xxx.01918 2 0 255 130.09595 32.00168 3 0 255 95.75979 xvi.57704 Metadata: AREA_OR_POINT=Areaii) From the output above, we see that there are
NoDatavalues 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()office:HARV_NA <- stack ( "data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_Ortho_wNA.tif" )v) We can plot the data with the
plotRGB()part:plotRGB ( HARV_NA , r = 1 , g = 2 , b = 3 )
6) The black edges are not plotted. seven) Both information sets take
NoDatavalues, however, in the RGB_stack the NoData value is not defined in the tiff tags, thus R renders them every bit 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 iii lower left origin.ten 731998.5 lower left origin.y 4712956 res.ten 0.25 res.y 0.25 ysign -one oblique.x 0 oblique.y 0 driver GTiff projection +proj=utm +zone=xviii +datum=WGS84 +units=m +no_defs file data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif apparent ring summary: GDType hasNoDataValue NoDataValue blockSize1 blockSize2 ane Float64 True -1.7e+308 1 3073 ii 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, private single-band GeoTIFFs besides. We will do this in a subsequently episode, Raster Time Series Data in R.
RasterStack vs RasterBrick in R
The R RasterStack and RasterBrick object types tin can both shop multiple bands. Still, how they store each band is different. The bands in a RasterStack are stored as links to raster information that is located somewhere on our computer. A RasterBrick contains all of the objects stored within the actual R object. In most cases, nosotros can work with a RasterBrick in the aforementioned 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 Resource
You can read the help for the
brick()function by typing?brick.
We can plow a RasterStack into a RasterBrick in R by using brick(StackName). Let'southward use the object.size() function to compare RasterStack and RasterBrick objects. First nosotros will check the size of our RasterStack object:
object.size ( RGB_stack_HARV ) 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 ) Notice that in the RasterBrick, all of the bands are stored within the bodily object. Thus, the RasterBrick object size is much larger than the RasterStack object.
You utilise the plotRGB() part to plot a RasterBrick too:
Challenge: What Functions Can Be Used on an R Object of a particular course?
We can view diverse functions (or methods) available to use on an R object with
methods(class=class(objectNameHere)). Utilise this to figure out:
- What methods can be used on the
RGB_stack_HARVobject?- What methods tin can be used on a unmarried band within
RGB_stack_HARV?- Why do you think there is a difference?
Answers
1) Nosotros can encounter a listing of all of the methods available for our RasterStack object:
methods ( grade = class ( RGB_stack_HARV ))[1] ! != [ [four] [[ [[<- [<- [7] %in% == $ [10] $<- addLayer adjacent [13] aggregate all.equal breathing [16] approxNA area Arith [19] as.array as.character as.data.frame [22] every bit.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] comprehend crop crosstab [49] crs<- cut cv [52] density dim dim<- [55] disaggregate dropLayer extend [58] extent excerpt flip [61] freq getValues getValuesBlock [64] getValuesFocal hasValues head [67] hist epitome 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] hateful 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 calibration [136] select setMinMax setValues [139] shift prove spplot [142] stack stackSelect stretch [145] subs subset Summary [148] summary t tail [151] text trim unique [154] unstack values values<- [157] weighted.hateful 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 codetwo) And compare that with the methods available for a unmarried ring:
methods ( class = class ( RGB_stack_HARV [ one ]))Warning in .S3methods(generic.function, class, envir): 'course' is of length > 1; only the first element volition be used[1] [ [<- anyDuplicated as_tibble as.data.frame [6] as.raster demark boxplot brick cellFromXY [11] coerce coordinates determinant distance duplicated [16] edit extent excerpt 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 aid and source code3) There are far more things one could or desire to ask of a total stack than of a single band.
Key Points
A single raster file can incorporate 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 tin can be accessed, analyzed, and visualized using the same functions as single bands.
Source: https://datacarpentry.org/r-raster-vector-geospatial/05-raster-multi-band-in-r/
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