Remote Sensing
Remote Sensing is art, science, and technology of obtaining information about the environment through the recording, analysis, and interpretation of data acquired through non-contact sensors. In this case, using imagery to gather information about the earth and earth surface objects. The information below describes Landsat 8 Band Information, R functions needed to gather information from imagery, and useful hints for visualizing Landsat products in R.
LandSat 8 Band Information
Natural Color
Bands: 4, 3, 2
“This band combination is as close to”true color" as you can get with a Landsat OLI image. One unfortunate drawback with this band combination is that these bands tend to be susceptible to atmospheric interference, so they sometimes appear hazy." - Harris Geospatial
Color Infrared (Vegetation)
Bands: 5, 4, 3
“Note how vegetation really pops in red, with healthier vegetation being more vibrant. It’s also easier to tell different types of vegetation apart than it is with a natural color image. This is a very commonly used band combination in remote sensing when looking at vegetation, crops and wetlands.” - Harris Geospatial
False Color (Urban)
Bands: 7, 6, 4
“Because this band combination makes use of both of the SWIR bands aboard Landsat 8, the image is much more crisp than band combinations that make use of bands in shorter wavelengths, which are more susceptible to haze.” - Harris Geospatial
Agriculture
Bands 6, 5, 2
“This band combination is useful for the monitoring of agricultural crops, which appear as a vibrant green. Bare earth appears as a magenta color and non-crop vegetation appears as more subdued shades of green.” - Harris Geospatial
Atmospheric Penetration
Bands: 7, 5, 3
“This band combination is useful for the monitoring of agricultural crops, which appear as a vibrant green. Bare earth appears as a magenta color and non-crop vegetation appears as more subdued shades of green.” - Harris Geospatial
CODE:
# Packages:
install.packages("raster")
library(raster)
# Working Directory:
getwd() # Will tell you your current working directory
setwd("C:/Users/Josh Carrell/Documents/R-Programming-Portfolio/RPubs/Data/RS/DC") # Where you want to pull your data from.
# Loading in Raster Data (Only using bands 2-7)
b2 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band2.tif")
b3 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band3.tif")
b4 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band4.tif")
b5 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band5.tif")
b6 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band6.tif")
b7 <- raster("LC08_L1TP_015033_20190812_20190812_01_RT_sr_band7.tif")
# Compiling Band Combinations (Only using 1 as an example.)
Natural_Color <- stack(b4, b3, b2)
# Plotting Landsat Image (Only using Natural Color as an example)
## Use the following code for plotting with axes and a title
plotRGB(true, axes=TRUE, stretch="hist", main = "Landsat Natural Color: Bands 4, 3, 2")
## Change the axes= to FALSE to remove axes and title
plotRGB(true, axes=FALSE, stretch="hist", main = "Landsat Natural Color: Bands 4, 3, 2")
## All of the above satellite images were with axes=FALSE