Project Overview

Ice Cloud is a NASA ACCESS project to perform a proof of concept (small scale) transformation of the ICESAT2 ATL06 product into a cloud native, usable, and scalable format for downstream scientific exploitation. This repository includes the code to perform the transformation into the Entwine Point Tile (EPT) format and a series of tutorials on how to use the generated endpoints. EPT is an octree-based storage format that’s encoding-agnostic, lossless, and supports a flexible attribute schema. Additional information about EPT can be found at entwine.io.

How to Execute EPT Conversion for ATL06 Data

1. Start by pulling down the repository:

git clone [email protected]:access/ice-cloud.git
cd ice-cloud

2. Make a copy of the Example Settings and save locally as settings.py. Within that file you are able to adjust the spatial and temporal bounds of your query. You must also populate the EARTHDATA_UID and EARTHDATA_EMAIL values from your personal EARTHDATA account. If you do not have an accounts, you will need to signup for one at https://urs.earthdata.nasa.gov/users/new
3. Grab the required Docker containers for execution. See Get Docker for instructions on how to install Docker.

docker pull pdal/pdal
docker pull connormanning/entwine

4. Install the following pip requirements:

h5py==2.10.0
icepyx==0.3.0
tqdm==4.42.1

5. Now you are ready to execute the converter! Simply call the ept_converter.py with Python 3.x python ept_converter.py
6. Lastly you will need to enter your EARTHDATA Password when prompted by the script!

How to Use EPT Data

In these tutorials we will explore a few simple examples of how to exploit the ATL 06 EPT endpoints.

Setting Up The Environment - Installing Conda

If you need to install conda, please follow the installation instructions.

After completing the installation, run the following commands to setup your Conda local PDAL environment:

    conda create --yes --name pdal-env --channel conda-forge python-pdal
    conda install --name pdal-env --channel conda-forge python-pdal
    conda activate pdal-env
    conda update python-pdal

At this point you will now have a local full PDAL conda environment that will allow you to make PDAL CLI commands directly.

How to work with PDAL and Pipelines

When working with PDAL you can pass all parameters as a command-line variable, but you are also able to pass the parameters in as a pipeline. A pipeline is essentially a JSON file that contains the following stages:

• Readers: instructs PDAL where the file is, what driver to use when reading the file (most file formats will default based on extension), and allows for mapping of custom dimensions.
• Filters: instructs PDAL on how to modify/process the points that have been loaded into memory. Examples include reprojection, decimation, colorization, height above ground, etc.
• Writers: instructs PDAL on where to write the output and in what format.

To pass a pipeline to PDAL, the command will look like this: pdal pipeline <filename.json>

Now that we have reviewed the basics of how to setup a PDAL environment and are familiar with Pipelines, we are going to dive into a few examples. The examples will be broken out in the following areas:

Filetype Conversions

Merging and Filtering

Python Operations