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Intro to Raster Data in Python

Overview

Teaching: 40 min
Exercises: 20 min
Questions
  • 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?

Objectives
  • Describe the fundamental attributes of a raster dataset.

  • Explore raster attributes and metadata using Python.

  • Read rasters into Python using the rioxarray package.

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 1 package in this episode to work with raster data - rioxarray, which is based on the popular rasterio package for working with rasters and xarray for working with multi-dimensional arrays. Make sure that you have rioxarray installed and imported.

import rioxarray

Introduce the Data

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

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

Open a Raster and 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 rioxarray.open_rasterio() to read the geotiff file and then inspect this metadata. By calling the variable name in the jupyter notebook we can get a quick look at the shape and attributes of the data.

surface_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
surface_HARV
<xarray.DataArray (band: 1, y: 1367, x: 1697)>
[2319799 values with dtype=float64]
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 4.714e+06 4.714e+06 ... 4.712e+06 4.712e+06
  * x            (x) float64 7.315e+05 7.315e+05 ... 7.331e+05 7.331e+05
    spatial_ref  int64 0
Attributes:
    transform:     (1.0, 0.0, 731453.0, 0.0, -1.0, 4713838.0)
    _FillValue:    -9999.0
    scales:        (1.0,)
    offsets:       (0.0,)
    grid_mapping:  spatial_ref

The first call to rioxarray.open_rasterio() opens the file and returns an object that we store in a variable, surface_HARV.

The output tells us that we are looking at an xarray.DataArray, with 1 band, 1367 columns, and 1697 rows. We can also see the number of pixel values in the DataArray, and the type of those pixel values, which is floating point, or (float64). The DataArray also stores different values for the coordinates of the DataArray. When using rioxarray, the term coordinates refers to spatial coordinates like x and y but also the band coordinate. Each of these sequences of values has its own data type, like float64 for the spatial coordinates and int64 for the band coordinate. The transform represents the conversion between array coordinates (non-spatial) and spatial coordinates.

This DataArray object also has a couple attributes that are accessed like .rio.crs, .rio.nodata, and .rio.bounds(), which contain the metadata for the file we opened. Note that many of the metadata are accessed as attributes without (), but bounds() is a function and needs parentheses.

print(surface_HARV.rio.crs)
print(surface_HARV.rio.nodata)
print(surface_HARV.rio.bounds())
print(surface_HARV.rio.width)
print(surface_HARV.rio.height)
EPSG:32618
-9999.0
(731453.0, 4712471.0, 733150.0, 4713838.0)
1697
1367

The Coordinate Reference System, or surface_HARV.rio.crs, is reported as the string EPSG:32618. The nodata value is encoded as -9999.0 and the bounding box corners of our raster are represented by the output of .bounds() as a tuple (like a list but you can’t edit it). The height and width match what we saw when we printed the DataArray, but by using .rio.width we can access these values if we need them in calculations.

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.

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.

After viewing the attributes of our raster, we can examine the raw value sof the array with .values:

surface_HARV.values
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]]])

This can give us a quick view of the values of our array, but only at the corners. Since our raster is loaded in python as a DataArray type, we can plot this in one line similar to a pandas DataFrame with DataArray.plot().

surface_HARV.plot()

Raster plot with earthpy.plot using the viridis color scale

Nice plot! Notice that rioxarray helpfully allows us to plot this raster with spatial coordinates on the x and y axis (this is not the default in many cases with other functions or libraries).

Plotting Tip

For more aesthetic looking plots, matplotlib allows you to customize the style with plt.style.use. 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.

import matplotlib.pyplot as plt
plt.style.use("ggplot")
surface_HARV.plot()

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 be accessed with .rio.crs. 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.

print(surface_HARV.rio.crs)
EPSG:32618

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)

import earthpy
earthpy.epsg['32618']
'+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs'

Challenge

What units are our data in?

Answers

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

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_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: https://upload.wikimedia.org/wikipedia/commons/8/8d/Utm-zones-USA.svg

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 min and max

print(surface_HARV.min())
print(surface_HARV.max())
<xarray.DataArray ()>
array(305.07000732)
Coordinates:
    spatial_ref  int64 0
<xarray.DataArray ()>
array(416.06997681)
Coordinates:
    spatial_ref  int64 0

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?

import numpy
print(numpy.percentile(surface_HARV, 25))
print(numpy.percentile(surface_HARV, 75))
345.5899963378906
374.2799987792969

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 arguments 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 view the number of bands in a raster by looking at the .shape attribute of the DataArray. The band number comes first when geotiffs are red with the .open_rasterio() function.

rgb_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif")
rgb_HARV.shape
(3, 2317, 3073)

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 rioxarray 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. rioxarray assigns a specific number as missing data to the .rio.nodata attribute when the dataset is read, based on the file’s own metadata. the GeoTiff’s nodata attribute is assigned to the value -1.7e+308and in order to run calculations on this image that ignore these edge values or plot the image without the nodata values being displayed on the color scale, rioxarray masks them out.

rgb_HARV.plot.imshow()

plot of demonstrate-no-data-black

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.

While this plot tells us where we have no data values, the color scale look strange, because our plotting function expects image values to be normalized between a certain range (0-1 or 0-255). By using surface_HARV.plot.imshow with the robust=True argument, we can display values between the 2nd and 98th percentile, providing better color contrast.

rgb_HARV.plot.imshow(robust=True)

plot of demonstrate-no-data-masked

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.

Key Points

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

  • rioxarray 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.

  • rioxarray and xarray are for working with multidimensional arrays like pandas is for working with tabular data with many columns