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Processing Landsat 8 Tiles into NDVI, EVI, and SATV via Python

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Landsat 8 Scene Processing

Developing RGB, NDVI, EVI, and SATV

Landsat 8 Info

Landsat 8 was launched Febuary 11, 2013 to fill the data gap left by Landsat4/5(RIP) and the partially functioning Landsat 7. Landsat 8 provides 11 bands ranging in spectrums from visible light, near infrared, and to thermal energy.

Band # Purpose Resolution
1 Coastal/Aerosol: Deep Blues\Violets 30m
2 Visible Light: Blue 30m
3 Visible Light: Green 30m
4 Visible Light: Red 30m
5 Near Infrared NIR: Vegetation 30m
6 Shortwave Infrared SWIR: Soils\Geology 30m
7 Shortwave Infrared SWIR: Soils\Geology 30m
8 Panchromatic: RGB Together 15m
9 High Reflectivity: Cirrus Clouds 30m
10 Thermal Infrared TIRS 1: Heat 100m
11 Thermal Infrared TIRS 2: Heat 100m

Sources:

Mapbox: Putting Landsat 8 Bands to Work USGS: Landsat Data Product USGS: Landsat Band Desigination NASA: Landsat DCM

Downloading the Scene Data

  1. USGS: Earth Explorer
  2. USGS: LandsatLook
  3. USGS: GLOVIS

After the scene has been downloaded, you will have a compressed file ranging from 750mb to a 1gb. To uncompress the file:

$ tar -zxvf LC80370372013169LGN00.tar.gz

The uncompressed bundle produces 13 files: 11 Tiff files for each Band, a BQA Tiff for scene's quality assesment, and a *MTL.txt file with metadata. The MTL.txt will be used to complete the calculations in processing a scene correctly.

Running the Script

From the terminal, change to the script's directory and use the python interpreter to run the script. Designate the full path of the Landsat's MTL.txt file.

$ python landsat_proc.py home/landsat/LC80370372013169LGN00_MTL.txt

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Processing Landsat 8 Tiles into NDVI, EVI, and SATV via Python

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