Plot Histograms of Pixel Values from Multi-band Imagery with EarthPy

Learn how to quickly plot distributions of pixel values in Python using Earthpy. This examples shows you how to create histogram plots for each raster band in a multi-band image such as Landsat 8 data.

Plot Raster Data Histograms Using EarthPy


This example shows you how to create histogram plots of pixel values for each raster band of a multi-band image using the ep.hist() function from the earthpy.spatial module.

In this example, you will learn how to plot histograms from multi-band imagery such as Landsat 8. Multi-band images store data as individual raster bands that contain reflectance values for various sections of the electromagnetic spectrum. For example, the second band of Landsat 8 provides surface reflectance within the blue wavelength of the electromagnetic spectrum, while the third and fourth bands of Landsat 8 provides surface reflectance within the green and red wavelengths, respectively. Histogram plots provide a quick way to review the distribution of pixel values for a specific band, which can be helpful to identify potential data quality issues or simply learn more about the surface area that was captured.

To begin, you will create a stack of these individuals bands from Landsat 8 data and then use the ep.hist() function to plot the histograms for each band in the stack.

Import Packages

In order to use the ep.hist() function from EarthPy, 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 ="vignette-landsat")

# Set working directory
os.chdir(os.path.join(, "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(os.path.join("data", "vignette-landsat", "*band*.tif"))
array_stack, meta_data = es.stack(landsat_path, nodata=-9999)

Plot All Histograms in a Stack With Custom Titles and Colors

You can create histograms for each band with unique colors and titles by first creating a list of colors and titles that will be provided to the ep.hist() function. The list for titles must contain the same number of strings as there are bands in the stack. You can also modify the colors for each image with a list of Matplotlib colors. If one color is provided in the list, every band will inherit that color. Otherwise, the list must contain the same number of strings as there are bands in the stack.

# Create the list of color names for each band
colors_list = [

# Create the list of titles for each band. The titles and colors listed
# in this example reflect the order and wavelengths of the Landsat 8 bands
titles = ["Ultra Blue", "Blue", "Green", "Red", "NIR", "SWIR 1", "SWIR 2"]

# Plot the histograms with the color and title lists you just created
# sphinx_gallery_thumbnail_number = 1
ep.hist(array_stack, colors=colors_list, title=titles)
Ultra Blue, Blue, Green, Red, NIR, SWIR 1, SWIR 2

Customize Bin Size and Arrangement of Histograms

You can customize the number of bins each histogram plot uses to group the data it is plotting. The default number is 20. This can be adjusted to match the data you are trying to display. Additionally, you can change the arrangement of the image overall by modifying the number of columns used to plot the data.

# Plot each histogram with 50 bins, arranged across three columns
ep.hist(array_stack, bins=50, cols=3)
plot hist functionality

Total running time of the script: ( 0 minutes 2.913 seconds)

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