Software and Data#
On this page you’ll find information about the computing environment and datasets that we’ll be using in this tutorial.
Below, you’lll see a list of the python libraries we’ll be using in this example. This is the full list of libraries across all notebooks.
import numpy as np
import xarray as xr
import rioxarray as rxr
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from shapely.geometry import Polygon
from shapely.geometry import Point
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature
from geocube.api.core import make_geocube
This tutorial also uses several functions that are stored in the script
itslivetools.py. It is located in the github repo for this tutorial. If you clone the repo, it should be available to import to the tutorial notebooks. Otherwise, if you would like to use
itslivetools.py, download the script and move it to your working directory.
Running tutorial on the cloud#
This link will launch a preconfigured jupyterlab environment on mybinder.org:
Running tutorial material locally#
To run the notebooks contained in this tutorial on your local machine
itslivetools_env conda environment (
conda env create -f environment-unpinned.yml) based on the
environment.yml file here. This should work on any platform (linux, osx, windows) and will install the latest versions of all dependencies.
Alternatively, the code repository for this tutorial (https://github.com/e-marshall/itslive) also contains “lock” files for Linux (conda-linux-64.lock.yml) and MacOS (conda-osx-64.lock.yml) that pin exact versions of all required python packages for a reproducible computing environment.
The velocity data that we’ll be using is from the ITS_LIVE dataset. This dataset contains global coverage of land ice velocity data at various temporal frequencies and in various formats. Follow the link to explore the data that’s available for a particular region you may be interested in. ITS_LIVE has multiple options for data access; this example will focus on using zarr datacubes that are stored in s3 buckets on AWS.
ITS_LIVE velocity data is accessed in a raster format and the data covers a large swath of terrain covering land that is glaciated and non-glaciated. We want to select just the pixels that cover glaciated surfaces; to do this, we use glacier outlines from the Randolph Glacier Inventory. The RGI region used in this tutorial is made available as a GeoParquet file in the tutorial repository.
Head to the next page to see how we start accessing and working with this data