Reproject Raster Data with Rioxarray
OverviewTeaching: 60 min
Exercises: 20 minQuestions
How do I call a DataArray to print out its metadata information?
How do I work with raster data sets that are in different projections?Objectives
Reproject a raster in Python using rasterio.
Accomplish the same task with rioxarray and xarray.
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.
Sometimes we encounter raster datasets that do not “line up” when plotted or analyzed. Rasters that don’t line up are most often in different Coordinate Reference Systems (CRS), otherwise known as “projections”. This episode explains how to line up rasters in different, known CRSs.
Raster Projection in Python
If you loaded two rasters with different projections in QGIS or ArcGIS, you’d see that they would align since these software reproject “on-the-fly”. But with R or Python, you’ll need to reproject your data yourself in order to plot or use these rasters together in calculations.
For this episode, we will be working with the Harvard Forest Digital Terrain Model (DTM). This differs from the surface model data we’ve been working with so far in that the digital surface model (DSM) includes the tops of trees, while the digital terrain model (DTM) shows the ground level beneath the tree canopy.
Our goal is to get these data into the same projection with the
rioxarray.reproject_match() function so that
we can use both rasters to calculate tree canopy height, also called a Canopy Height Model (CHM).
First, we need to read in the DSM and DTM rasters.
import rioxarray surface_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif") terrain_HARV = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/HARV/DTM/HARV_dtmCrop_WGS84.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 x (x) float64 7.315e+05 7.315e+05 ... 7.331e+05 spatial_ref () int64 0 Attributes: STATISTICS_MAXIMUM: 416.06997680664 STATISTICS_MEAN: 359.85311802914 STATISTICS_MINIMUM: 305.07000732422 STATISTICS_STDDEV: 17.83169335933 _FillValue: -9999.0 scale_factor: 1.0 add_offset: 0.0 grid_mapping: spatial_ref
To read the spatial reference in the output you click on the icon “Show/Hide attributes” on the right side of the
spatial_ref row. You can also print the Well-known Text projection string.
'PROJCS["WGS 84 / UTM zone 18N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-75],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32618"]]'
We can see the datum and projection are UTM zone 18N and WGS 84 respectively. UTM zone 18N is a regional projection with an associated coordinate system to more accurately capture distance, shape and/or area around the Harvard Forest.
'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]'
We see the DTM is in an unprojected geographic coordinate system, using WGS84 as the datum and a coordinate system that spans the entire planet (i.e. latitude and longitude). This means that every location on the planet is defined using the SAME coordinate system and the same units. Geographic coordinate reference systems are best for global analysis but not for capturing distance, shape and/or area on a local scale.
We can use the CRS attribute from one of our datasets to reproject the other dataset so that they are both in the same projection. The only argument that is required is the
reproject_match argument, which takes the CRS of the result of the reprojection.
terrain_HARV_UTM18 = terrain_HARV.rio.reproject_match(surface_HARV) terrain_HARV_UTM18
xarray.DataArray band: 1, y: 1492, x: 1801 array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.], [-9999., -9999., -9999., ..., -9999., -9999., -9999.], [-9999., -9999., -9999., ..., -9999., -9999., -9999.], ..., [-9999., -9999., -9999., ..., -9999., -9999., -9999.], [-9999., -9999., -9999., ..., -9999., -9999., -9999.], [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]]) Coordinates: x (x) float64 7.314e+05 7.314e+05 ... 7.332e+05 y (y) float64 4.714e+06 4.714e+06 ... 4.712e+06 band (band) int64 1 spatial_ref () int64 0 Attributes: scale_factor: 1.0 add_offset: 0.0 grid_mapping: spatial_ref _FillValue: -9999.0
In one line
reproject_match does a lot of helpful things:
- It reprojects
terrain_HARVfrom WGS 84 to UTM Zone 18.
terrain_HARVhas data values and
surface_HARVdoes not, the result
terrain_HARV_UTM18is clipped. Where surface_HARV has data values and
terrain_HARVdoes not, the result
terrain_HARV_UTM18is padded with no data values to match the extent.
- It sets the no data value of
terrain_HARVto the no data value for
There also exists a method called
reproject(), which only reprojects one raster to another projection. If you want more control over how rasters are resampled, clipped, and/or reprojected, you can use the
reproject()method and other
We can also save our DataArray that we created with
rioxarray to a file.
reprojected_path = "data/NEON-DS-Airborne-Remote-Sensing/HARV/DTM/HARV_dtmCrop_UTM18.tif" terrain_HARV_UTM18.rio.to_raster(reprojected_path)
Inspect the metadata for
surface_HARV. Are the projections the same? Are the extents the same? Are the no data values the same? How might projections, extents, and no data values effect calculations we make between arrays?
# view crs for DTM print(terrain_HARV_UTM18.rio.crs) # view crs for DSM print(surface_HARV.rio.crs)
Good, the CRSs are the same. But, before we plot or calculate both of these DataArrays together, we should make sure they have the same nodata value.
# view noddata value for DTM print(terrain_HARV_UTM18.rio.nodata) # view nodata value for DSM print(surface_HARV.rio.nodata)
Furthermore, let’s make sure both of these DataArrays have the same shape (i.e. extent).
# view shape for DTM print(terrain_HARV_UTM18.shape) # view shape for DSM print(surface_HARV.shape)
(1, 1367, 1697) (1, 1367, 1697)
The shapes and projections are the same which means these data cover the same locations. The no data values are also the same. This means we can run calculations on these two DataArrays.
Let’s plot our handiwork so far! We can use the
xarray.DataArray.plot function to show the DTM. But if we run the following code, something doesn’t look right …
Whoops! What did we forget to do to the DTM DataArray before plotting?
Our array has a
-9999.0, which causes the color of our plot to be stretched over too wide a range. We’d like to only display valid values, so before plotting we can filter out the nodata values using the
where()function and the
.rio.nodataattribute of our DataArray.
terrain_HARV_UTM18_valid = terrain_HARV_UTM18.where( terrain_HARV_UTM18 != terrain_HARV_UTM18.rio.nodata) terrain_HARV_UTM18_valid.plot(cmap="viridis")
If we had saved
terrain_HARV_UTM18to a file and then read it in with
masked=Trueargument, the raster’s
nodatavalue would be masked and we would not need to use the
where()function to do the masking before plotting.
There are many ways to improve this plot. Matplotlib offers lots of different functions to change the position and appearance of plot elements. To plot with Matplotlib, you need to import the
pyplotmodule. Something that would really improve our figure is adding a title. This can be done with the
Try importing Matplotlib and adding a title to the figure.
pyplotand adding a title
Here’s how we can use
pyplotfunctions to modify elements in our graph.
import matplotlib.pyplot as plt terrain_HARV_UTM18_valid.plot() plt.title("Harvard Forest Digital Terrain Model")
xarrayhas Matplotlib under the hood, we don’t need to modify our original plotting method.
Customizing plots with Matplotlib
Now that you’ve added a title to your plot, look for other ways to customize your plot with Matplotlib. One possible way to quickly customize a plot is with the
plt.style.use()function. You can check available styles with
Another useful function for the plots we are making is
plt.ticklabel_format(style="plain"). This will ensure that our ticks are not truncated, making our plot nicer.
Try customizing your plot with the functions above or any other
Styles and formatting
Here is the result of using a ggplot like style for our digital terrain plot.
plt.style.use("ggplot") terrain_HARV_UTM18_valid.plot() plt.title("Harvard Forest Digital Terrain Model") plt.ticklabel_format(style="plain")
plt.style.use()comes before and both
plt.ticklabel_formatcome after the
.plot()function. This is because
pyplotwide setting, while the latter two functions apply only to our current figure.
Quick tip: for all following plots in our lesson, use the
Challenge: Reproject, then Plot a Digital Terrain Model
Create 2 maps in a UTM projection of the San Joaquin Experimental Range field site, using the
matplotlib.pyplot(to add a title). Reproject the data as necessary to make sure each map is in the same UTM projection and save the reprojected file with the file name “data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif”.
If we read in these files with the argument
masked=True, then the nodata values will be masked automatically and set to
numpy.nan, or Not a Number. This can make plotting easier since only valid raster values will be shown. However, it’s important to remember that
numpy.nanvalues still take up space in our raster just like
nodatavalues, and thus they still affect the shape of the raster. Rasters need to be the same shape for raster math to work in Python. In the next lesson, we will examine how to prepare rasters of different shapes for calculations.
terrain_SJER = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmCrop.tif", masked=True) surface_SJER = rioxarray.open_rasterio("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif", masked=True) reprojected_surface_model = surface_SJER.rio.reproject(dst_crs=terrain_SJER.rio.crs) plt.figure() reprojected_surface_model.plot() plt.title("SJER Reprojected Surface Model") reprojected_surface_model.rio.to_raster("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop_WGS84.tif") plt.figure() terrain_HARV_SJER.plot() plt.title("SJER Terrain Model")
In order to plot or do calculations with two raster data sets, they must be in the same CRS.
rioxarray and xarray provide simple syntax for accomplishing fundamental geospatial operations.
rioxarray is built on top of rasterio, and you can use rasterio directly to accomplish fundamental geospatial operations.