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Xarray's Rasterio Backend Tutorial (#365)
* add rasterio backend * use tutorial * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * pr suggestions --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎_toc.yml‎

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- file: advanced/backends/1.Backend_without_Lazy_Loading.ipynb
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- file: advanced/backends/2.Backend_with_Lazy_Loading.ipynb
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- file: advanced/backends/rasterio_backend.ipynb
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- file: advanced/accessors/accessors.md
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- file: advanced/accessors/01_accessor_examples.ipynb

‎advanced/backends/rasterio_backend.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Xarray's Rasterio backend\n",
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"\n",
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"In this lesson, we will learn how to use xarray's rasterio backend engine to open GeoTIFF rasters. By the end of the lesson, we will be able to:\n",
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"\n",
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":::{admonition} Learning Goals\n",
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"- Learn about the GeoTIFF format\n",
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"- Lean about xarray's \"rasterio\" backend and the \"rioxarray\" accessor\n",
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"- Learn how to read and plot GeoTIFF files with xarray\n",
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"- Explore how to perform reprojection operations on rasters\n",
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":::\n",
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"\n",
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"## What are GeoTIFFs?\n",
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"\n",
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"The TIFF (Tagged Image File Format) format is a metadata rich image format for raster data. GeoTIFF (Geographic Tagged Image File Format) files are TIFF files that use georeferencing information (such as map projection and coordinate systems) as metadata. A GeoTIFF file with a single band contains 2D raster data for a single characteristic (i.e., variable) and maps to a geographic region. A GeoTIFF file can have multiple bands that all map to the same geographic region.\n",
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"\n",
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"![Raster_Image](https://docs.qgis.org/3.44/en/_images/raster_dataset.png)\n",
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"\n",
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"## Rasterio and Rioxarray Backends\n",
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"\n",
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"[Rasterio](https://rasterio.readthedocs.io/en/stable/intro.html) is a geospatial raster library that expresses GDAL ([Geospatial Data Abstraction Library](http://gdal.org)) data model with a Python API and CLI. [Rioxarray](https://corteva.github.io/rioxarray/stable/readme.html) is a wrapper around the rasterio library, that also extends the xarray api with the *rio* accessor. When you open a GeoTIFF file with the \"rasterio\" engine it returns the data as an xarray object with access to methods from the *rio* accessor.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1",
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"metadata": {},
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"source": [
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"## Reading a GeoTIFF\n",
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"\n",
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"Xarray's \"rasterio\" backend supports reading GeoTIFFs.\n",
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"\n",
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"Lets read a GeoTIFF file as an `xr.Dataset` by selecting `engine='rasterio'`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import xarray as xr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"rds = xr.tutorial.open_dataset(\"RGB.byte.tif\", engine=\"rasterio\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4",
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"metadata": {},
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"source": [
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":::{note} We can also read GeoTIFFs with `rioxarray.open_rasterio`.\n",
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"\n",
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"The following code snippet opens a GeoTIFF and returns a `DataArray` object\n",
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"```python\n",
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"import rioxarray\n",
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"rioxarray.open_rasterio(\"RGB.byte.tif\")\n",
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"```\n",
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"\n",
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":::"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5",
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"metadata": {},
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"source": [
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"## Raster bands\n",
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"\n",
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"GeoTIFFS can have multiple bands each representing a range or band in the electromagnetic spectrum. Arrays stored within bands in xarray are data variables and `DataArray`'s objects in the the Xarray Data Model."
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]
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},
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{
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"cell_type": "markdown",
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"id": "6",
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"metadata": {},
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"source": [
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"Lets get the `DataArray` object (or variable) from our dataset. The variable name is \"band_data\"."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda = rds[\"band_data\"]\n",
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"rda"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8",
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"metadata": {},
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"source": [
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"Lets try getting the total number of bands for this GeoTIFF. Since `rioxarray` extends xarray with the *rio* accessor we can this accessor be able use many of the builtin `rasterio` methods."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda.rio.count"
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]
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},
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{
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"cell_type": "markdown",
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"id": "10",
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"metadata": {},
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"source": [
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"### Selection by bands\n",
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"We can also select by bands. Since there are 3 bands in this dataset we can return raster array for the first band of this `xr.DataArray` by with indexing\n",
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"\n",
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"Lets get the first band of this dataset and try plotting it"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "11",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda[0].plot(cmap=\"pink\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12",
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"metadata": {},
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"source": [
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"## Bounds\n",
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"With `.rio.bounds()` we can get the spatial bounding box of our `DataArray`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "13",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda.rio.bounds()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14",
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"metadata": {},
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"source": [
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"## Transformation\n",
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"\n",
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"With `.rio.transform()` we can get the affine transformation matrix that maps pixel locations in (col, row) coordinates to (x, y) spatial positions.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "15",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda.rio.transform()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16",
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"metadata": {},
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"source": [
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"### Coordinate Reference System (CRS)\n",
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"We `rio.crs` we can get the CRS of our raster and reproject our raster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "17",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda.rio.crs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18",
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"metadata": {},
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"source": [
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"### Reprojection\n",
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"We can also reproject our raster from one CRS to another.\n",
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"\n",
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"Lets reproject our raster from \"EPSG:6326\" to \"EPSG:32612\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "19",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda_reproj = rda.rio.reproject(\"EPSG:32612\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20",
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"metadata": {},
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"source": [
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":::{note}\n",
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"We have to update our CRS system to the new projection with `rio.write_crs`. We set `inplace=True` to write the CRS to the existing dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21",
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"metadata": {},
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"outputs": [],
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"source": [
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"rda_reproj.rio.write_crs(\"EPSG:32612\", inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "22",
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"metadata": {},
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"source": [
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"## Exercise"
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]
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},
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{
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"cell_type": "markdown",
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"id": "23",
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"metadata": {},
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"source": [
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Can you reproject and update the CRS of second band of data to 'ESPG:3857' and plot your results?\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"```python\n",
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"rda[1].rio.reproject(\"EPSG:3857\").rio.write_crs(\"EPSG:3857\", inplace=True).plot()\n",
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"```\n",
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":::\n",
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"::::\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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"nbformat": 4,
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}

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