Plot Bands of Satellite Imagery with EarthPy

Learn how to use the EarthPy plot_bands() function to quickly plot raster bands for an image. Plot_bands() can be used to both plot many bands with one command with custom titles and legends OR to plot a single raster layer with (or without) a legend.

Plot Raster Data Layers Using EarthPy

Note

The examples below will show you how to use the plot_bands() function to plot individual raster layers in images using python. To plot rgb data, read help documentation related to ep.plot_rgb().

In this vignette, you will use Landsat 8 data. To begin, you will create a stack of bands using Landsat 8 data. You will then plot the raster layers.

Import Packages

In order to use the plot_bands() function with Landsat 8 data, the following packages need to be imported.

import os
from glob import glob
import matplotlib.pyplot as plt
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

Import Example Data

To get started, make sure your directory is set. Then, create a stack from all of the Landsat .tif files (one per band).

# Get data for example
data = et.data.get_data("vignette-landsat")

# Set working directory
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the Landsat 8 bands
# This creates a numpy array with each "layer" representing a single band
# You can use the nodata= parameter to mask nodata values
landsat_path = glob(
    "data/vignette-landsat/LC08_L1TP_034032_20160621_20170221_01_T1_sr_band*_crop.tif"
)
landsat_path.sort()
array_stack, meta_data = es.stack(landsat_path, nodata=-9999)

Out:

Downloading from https://ndownloader.figshare.com/files/15197339
Extracted output to /home/docs/earth-analytics/data/vignette-landsat/.

Plot All Bands in a Stack

When you give ep.plot_bands() a three dimensional numpy array, it will plot all layers in the numpy array. You can create unique titles for each image by providing a list of titles using the title= parameter. The list must contain the same number of strings as there are bands in the stack.

titles = ["Ultra Blue", "Blue", "Green", "Red", "NIR", "SWIR 1", "SWIR 2"]
# sphinx_gallery_thumbnail_number = 5
ep.plot_bands(array_stack, title=titles)
plt.show()
../_images/sphx_glr_plot_bands_functionality_001.png

Plot One Band in a Stack

If you give ep.plot_bands() a one dimensional numpy array, it will only plot that single band. You can turn off the colorbar using the cbar parameter (cbar=False).

ep.plot_bands(array_stack[4], cbar=False)
plt.show()
../_images/sphx_glr_plot_bands_functionality_002.png

Turn Off Scaling

ep.plot_bands() scales the imagery to a 0-255 scale by default. This range of values makes it easier for matplotlib to plot the data. To turn off scaling, set the scale parameter to False. Below you plot NDVI with scaling turned off in order for the proper range of values (-1 to 1) to be displayed. You can use the cmap= parameter to adjust the colormap for the plot

NDVI = es.normalized_diff(array_stack[4], array_stack[3])
ep.plot_bands(NDVI, scale=False, cmap="RdYlGn")
plt.show()
../_images/sphx_glr_plot_bands_functionality_003.png

Adjust the Number of Columns for a Multi Band Plot

The number of columns used while plotting multiple bands can be changed in order to change the arrangement of the images overall.

ep.plot_bands(array_stack, cols=2)
plt.show()
../_images/sphx_glr_plot_bands_functionality_004.png

Total running time of the script: ( 0 minutes 10.543 seconds)

Gallery generated by Sphinx-Gallery