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Intro to Raster Data in Python


Teaching: 40 min
Exercises: 20 min
  • What is a raster dataset?

  • How do I work with and plot raster data in Python?

  • How can I handle missing or bad data values for a raster?

  • Describe the fundamental attributes of a raster dataset.

  • Explore raster attributes and metadata using Python.

  • Read rasters into Python using the rasterio package.

  • Plot a raster file in Python using the earthpy package.

  • Describe the difference between single- and multi-band rasters.

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

In this episode, we will introduce the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in Python. We will discuss some of the core metadata elements that we need to understand to work with rasters, including Coordinate Reference Systems, no data values, and resolution. We will also explore missing and bad data values as stored in a raster and how Python handles these elements.

We will use four packages in this episode to work with raster data - rasterio for reading and writing rasters, scipy for descriptive statistics, numpy for masking raster data and earthpy.plot for plotting. We will also use matplotlib to improve the aesthetics of our plot. Make sure that you have these packages installed and imported.

import rasterio
import scipy.stats
import numpy
import earthpy.plot
import matplotlib.pyplot as plt

A brief introduction to the datasets can be found on the Geospatial workshop homepage.

For more detailed information about the datasets, check out the Geospatial workshop data page.

View Raster File Attributes

We will be working with a series of GeoTIFF files in this lesson. The GeoTIFF format contains a set of embedded tags with metadata about the raster data. We can use the function to get metadata about our raster data before we read the whole file into Python. This let’s you get a quick look at the shape and attributes of your data.

surface_model_HARV ="data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
{'driver': 'GTiff',
 'dtype': 'float64',
 'nodata': -9999.0,
 'width': 1697,
 'height': 1367,
 'count': 1,
 'crs': CRS.from_epsg(32618),
 'transform': Affine(1.0, 0.0, 731453.0,
        0.0, -1.0, 4713838.0)}

The first call to opens the file and returns an object that we store in a variable, surface_model_HARV. This object has an attribute, .meta, which contains the metadata for the file we opened.

This metadata is stored in the form of a python dict. The driver shows that we read in a GeoTIFF file, where the values are encoded as floating point numbers ('dtype': 'float64'), and the nodata value encoded as -9999.0. 'width': 1697 represents the 1697 columns in the raster and 'height': 1367 represents the number of rows. 'count': 1 represents the number of bands in the dataset, indicating that we’re working with a single-band raster. The Coordinate Reference System, or crs, is reported by EPSG code as the 32618 in CRS.from_epsg(32618). The transform represents the conversion between pixel coordinates and spatial coordinates.

A python dict stores key, value pairs. If we wanted to access the width of the file we opened, we can use the key 'width' to select that value from the dictionary.


The width value in this raster’s meta is of type int, whereas the value for the key nodata is of type float. Other keys, crs and transform, use custom objects defined by the rasterio to represent the coordinate reference system, location, and resolution. We will be exploring this data throughout this episode. By the end of this episode, you will be able to understand and explain the metadata output.

Open a Raster in Python

Now that we’ve previewed the metadata for our GeoTIFF, let’s load this raster dataset into Python as an array and explore its metadata more closely. We can use the .read() function to read the first, and only, band of our raster.

Data Tip - Object names

To improve code readability, file and object names should be used that make it clear what is in the file. The data for this episode were collected from Harvard Forest so we’ll use a naming convention of datatype_HARV_arr. We’ll add the ‘arr’ suffix to indicate this is an array.

surface_model_HARV_arr =
array([[408.76998901, 408.22998047, 406.52999878, ..., 345.05999756,
        345.13998413, 344.97000122],
       [407.04998779, 406.61999512, 404.97998047, ..., 345.20999146,
        344.97000122, 345.13998413],
       [407.05999756, 406.02999878, 403.54998779, ..., 345.07000732,
        345.08999634, 345.17999268],
       [367.91000366, 370.19000244, 370.58999634, ..., 311.38998413,
        310.44998169, 309.38998413],
       [370.75997925, 371.50997925, 363.41000366, ..., 314.70999146,
        309.25      , 312.01998901],
       [369.95999146, 372.6000061 , 372.42999268, ..., 316.38998413,
        309.86999512, 311.20999146]])

The output of .read() is a numpy array, which is abbreviated with ... since the array is too large for the output console.

To visualise this data in Python using earthpy.plot, all we need is our data in a numpy array and some options to control the color of our plot and plot labels.

    title="Digital Surface Model Without Hillshade",
    figsize=(10, 6)

Nice plot! We set the color scale to viridis which is a color-blindness friendly color scale.

Raster plot with earthpy.plot using the viridis color scale

Plotting Tip

For more aesthetic looking plots, matplotlib allows you to customize the style with However, if you want more control of the look of your plot, matplotlib has many more functions to change the position and appearnce of plot elements.

Show plot

Here is the result of using a ggplot like style for our surface model plot."ggplot")
    title="Digital Surface Model Without Hillshade",
    figsize=(10, 6)

plot of chunk unnamed-chunk-5

This map shows the elevation of our study site in Harvard Forest. From the legend, we can see that the maximum elevation is ~400, but we can’t tell whether this is 400 feet or 400 meters because the legend doesn’t show us the units. We can look at the metadata of our object to see what the units are. Much of the metadata that we’re interested in is part of the CRS, and it can also be accessed by calling other attributes besides meta. We introduced the concept of a CRS in an earlier lesson (TODO replace link).

Now we will see how features of the CRS appear in our data file and what meanings they have.

View Raster Coordinate Reference System (CRS) in Python

We can view the CRS string associated with our Python object using thecrs attribute.

You can convert the EPSG code to a PROJ4 string with earthpy.epsg, another python dict which maps epsg codes (keys) to PROJ4 strings (values)

'+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs'


What units are our data in?


+units=m tells us that our data is in meters. We could also get this information from the attribute

Understanding CRS in Proj4 Format

Let’s break down the pieces of proj4 string. The string contains all of the individual CRS elements that Python or another GIS might need. Each element is specified with a + sign, similar to how a .csv file is delimited or broken up by a ,. After each + we see the CRS element being defined. For example projection (proj=) and datum (datum=).

UTM Proj4 String

Our projection string for surface_model_HARV specifies the UTM projection as follows:

'+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs'

Note that the zone is unique to the UTM projection. Not all CRSs will have a zone. Image source: Chrismurf at English Wikipedia, via Wikimedia Commons (CC-BY).

The UTM zones across the continental United States. From:

Calculate Raster Min and Max Values

It is useful to know the minimum or maximum values of a raster dataset. In this case, given we are working with elevation data, these values represent the min/max elevation range at our site.

We can compute these and other descriptive statistics with scipy.stats.describe().

scipy.stats.describe(surface_model_HARV_arr, axis=None)
DescribeResult(nobs=2319799, minmax=(305.07000732421875, 416.0699768066406), mean=359.8531180291444, variance=317.96928806118814, skewness=-0.04227854491703972, kurtosis=-0.7242596053501216)

The information above includes a report of the number of observations, min and max values, mean, and variance. We specified the axis=None argument so that statistics were computed for the whole array, rather than for each row in the array.

You could also get each of these values one by one using numpy. What if we wanted to calculate 25% and 75% quartiles?

print(numpy.percentile(surface_model_HARV_arr, 25))
print(numpy.percentile(surface_model_HARV_arr, 75))

Data Tip - Set min and max values

You may notice that numpy.percentile didn’t require an axis=None argument. This is because axis=None is the default for most numpy functions. It’s always good to check out the docs on a function to see what the default argumetns are, particularly when working with multi-dimensional image data. To do so, we can usehelp(numpy.percentile) or ?numpy.percentile if you are using jupyter notebook or jupyter lab.

We can see that the elevation at our site ranges from 305.0700073m to 416.0699768m.

Raster Bands

The Digital Surface Model that we’ve been working with is a single band raster. This means that there is only one dataset stored in the raster: surface elevation in meters for one time period. However, a raster dataset can contain one or more bands.

Multi-band raster image

We can use the .read() function to load one single band from a single or multi-band raster. We can view the number of bands in a raster by looking at the count key of the meta python dict.


However, raster data can also be multi-band, meaning that one raster file contains data for more than one variable or time period for each cell. By default the .read() function loads all bands in a raster regardless of whether it has one or more bands and places each in a third axis of a numpy array in [bands, rows, columns] order. For example, even if there is only 1 band in a raster, it will be placed in it’s own band axis.

surface_model_HARV_arr_3D =
(1, 1367, 1697)

Earlier, we read our raster in as a 2D array instead

surface_model_HARV_arr_2D =
(1367, 1697)

It’s always a good idea to examine the shape of the raster array you are working with and make sure it’s what you expect. Many functions, especially ones that plot images, expect a raster array to have a particular shape.

Jump to a later episode in this series for information on working with multi-band rasters: Work with Multi-band Rasters in Python.

Dealing with Missing Data

Raster data often has a “no data value” associated with it and for raster datasets read in by rasterio this value is referred to as nodata. This is a value assigned to pixels where data is missing or no data were collected. However, there can be different cases that cause missing data, and it’s common for other values in a raster to represent different cases. The most common example is missing data at the edges of rasters.

By default the shape of a raster is always rectangular. So if we have a dataset that has a shape that isn’t rectangular, some pixels at the edge of the raster will have no data values. This often happens when the data were collected by an sensor which only flew over some part of a defined region.

In the RGB image below, the pixels that are black have no data values. The sensor did not collect data in these areas.

rgb_HARV ="NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif", "r")
rgb_HARV_arr =
    title="RGB Image, NoData Values UnMasked",
    figsize=(10, 6)

plot of chunk demonstrate-no-data-black

rasterio assigns a specific number as missing data to the meta attribute when the dataset is read, based on the file’s own metadata. While the GeoTiff’s nodata attribute is assigned to the value -1.7e+308, it turns out the missing data at the edges are represented by the value 0. In order to run calculations on this image that ignore these edge values or plot he image without the nodata values being displayed on the color scale, we can mask out 0 values in our numpy array.

In the next image, the black edges have been masked using, a function that assigns no data values where a condition is true.

rgb_HARV_masked_arr =, rgb_HARV_arr) #1st argument is the condition, second is the array to mask
    title="RGB Image, NoData Values Masked",
    figsize=(10, 6)

plot of chunk demonstrate-no-data-masked

The difference here shows up as ragged edges on the plot, rather than black spaces where there is no data.

If your raster already has nodata values set correctly but you aren’t sure where they are, you can deliberately plot them in a particular colour. This can be useful when checking a dataset’s coverage. For instance, sometimes data can be missing where a sensor could not ‘see’ its target data, and you may wish to locate that missing data and fill it in. With Python, we can plot a boolean array of True/False values from our masked array’s .mask attribute. Since the mask array represents no data values as True and data values as False, we need to reverse our boolean array so that we can more clearly see where no data values have been masked in each of our color channels.

    rgb_HARV_masked_arr.mask*-1, # mutliplying a boolean array by -1 reverses True and False values
    title="Mask Array",
    figsize=(10, 6)

plot of chunk napink

From this plot we see something interesting, while our no data values were masked along the edges, the color channel’s no data values don’t all line up. The colored pixels at the edges between white black result from there being no data in one or two channels at a given pixel. 0 could conceivably represent a valid value for reflectance (the units of our pixel values) so it’s good to make sure we are masking values at the edges and not valid data values within the image.

Check out the documentation on the masked array module for more details. Regular numpy functions work with masked arrays like they do for regular numpy arrays, but ignore masked no data values.

The value that is conventionally used to take note of missing data (the no data value) varies by the raster data type. For floating-point rasters, the figure -3.4e+38 is a common default, and for integers, -9999 is common. Some disciplines have specific conventions that vary from these common values.

In some cases, other nodata values may be more appropriate. An nodata value should be a) outside the range of valid values, and b) a value that fits the data type in use. For instance, if your data ranges continuously from -20 to 100, 0 is not an acceptable nodata value! Or, for categories that number 1-15, 0 might be fine for nodata, but using -.000003 will force you to save the GeoTIFF on disk as a floating point raster, resulting in a bigger file.


How can we find the assigned nodata value for our dataset when it is read in? How can we assign it to something else?


changed_meta_copy = surface_model_HARV.meta.copy()
changed_meta_copy['nodata'] = -3.4e+38

No data values are encoded as -9999. If we didn’t make a copy of the meta and instead a) opened the file with both read and write permissions and b) changed the original, we would have changed the original file’s no data value even after restarting the python kernel.

Key Points

  • The GeoTIFF file format includes metadata about the raster data.

  • To plot raster data with the earthpy package, we need to read in the image as a numpy array.

  • rasterio stores CRS information as a CRS object that can be converted to an EPSG code or PROJ4 string.

  • The GeoTIFF file may or may not store the correct no data value(s). We can find the correct value(s) in the raster’s external metadata or by plotting the raster.