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The R Consortium recently connected with Lampros Sp. Mouselimis, the creator of the ICESat2R package, discussing the ICESat-2 mission, a significant initiative in understanding the Earth’s surface dynamics. This NASA mission, utilizing the Advanced Topographic Laser Altimeter System (ATLAS), provides in-depth altimetry data, capturing Earth’s topography with unparalleled precision.
Mouselimis’ contribution, the ICESat2R package, is an R-based tool designed to streamline the analysis of ICESat-2 data. It simplifies accessing, processing, and visualizing the vast datasets generated by ATLAS, which emits 10,000 laser pulses per second to measure aspects like ice sheet elevation, sea ice thickness, and global vegetation biomass. This package enables users to analyze complex environmental changes such as ice-sheet elevation change, sea-ice freeboard, and vegetation canopy height more efficiently and accurately. The R Consortium funded this project.
Lampros Sp. Mouselimis is an experienced Data Analyst and Programmer who holds a degree in Business Administration and has received post-graduate training in Data Processing, Analysis, and Programming. His preferred programming language is R, but he can also work with Python and C++. As an open-source developer, you can find his work on GitHub With over a decade of experience in data processing using programming, he mainly works as a freelancer and runs his own business, Monopteryx, based in Greece. Outside of work, Lampros enjoys swimming, cycling, running, and tennis. He also takes care of two small agricultural fields that are partly filled with olive trees.
You built an R package called ICESat2R using the ICESat-2 satellite. Do you consider your ICESat2R project a success?
ICESat-2 R has 7,252 downloads, which, considering the smaller group of researchers who focus on using ICESat-2 data, qualifies it as a popular tool. It’s not as popular compared to some other remote sensing packages, but I believe it’s been a success based on two main points:
- Contribution to the R users community: I hope that the R programmers who use the IceSat2R R package are now able to process altimetry data without any issues, and, if any, then I’ll be able to resolve these by updating the code in the GitHub and CRAN repositories.
- Personal and Professional achievement: I applied for a grant to the R consortium, and my application was accepted. Moreover, I implemented the code by following the milestone timelines. Seeing a project through and providing it publicly is a success, I believe.
Who uses ICESat2R, and what are the main benefits? Any unique benefits compared to the Python and Julia interfaces?
The users of the ICESat2R package can be professionals, researchers, or R programming users in general. I assume that these users could be:
- Ice scientists, ecologists, and hydrologists (to name a few) who would be interested in the altimeter data to perform their research
- Public authorities or military personnel, who, for instance, would like to process data related to high-risk events such as floods
- Policy and decision-makers (the ICESat-2 data can be used, for instance, in resource management)
- R users that would like to “get their hands dirty” with altimeter data
I am aware of the Python and Julia interfaces, and to tell the truth, I looked at the authors’ code bases before implementing the code, mainly because I wanted to find out the exact source they used to download the ICESat-2 data.
Based on the current implementation, I would say that the benefits of the ICESat2R package are the following:
- The R programming users can use NASA’s OpenAltimetry interface, which, as of December 2023, doesn’t require any credentials
- The R package includes 3 Vignettes (Articles) and detailed documentation (Reference) for the implemented code
What is an interesting example of using ICESat2R?
There are many examples where the ICESat2R package can be used. For instance, a potential use case would be to display differences between a Digital Elevation Model (Copernicus DEM) and land-ice-height ‘ICESat-2’ measurements. The next image shows the ICESat-2 land-ice-height in winter (green) and summer (orange) compared to a DEM,
More detailed explanations related to this use case exist in the Vignette ICESat-2 Atlas Products of the package.
Were there any issues using OpenAltimetry API (the “cyberinfrastructure platform for discovery, access, and visualization of data from NASA’s ICESat-2 mission”)? (NOTE: Currently, the OpenAltimetry API website appears to be down?)
At the beginning of October 2023, I was informed that the OpenAltimetry website (previously https://openaltimetry.org/) has migrated to https://openaltimetry.earthdatacloud.nasa.gov/. I then contacted the support of the National Snow & Ice Data Center, which informed me about the migration of the API interface.
Currently, I have an open issue in my Github repo related to this migration. Once the OpenAltimetry API becomes functional again, I’ll submit the updated version of the ICESat2R package to CRAN.
In your blog post for the copernicusDEM package, you showed a code snippet showing how it loads files, iterates over the files, and uses a for-loop to grab all the data. Can you provide something similar for ICESat2R?
Whenever I submit an R package to CRAN, I include one (or more) vignettes that explain the package’s functionality. Once the package is accepted, I also upload one of the vignettes to my personal blog. This was the case for the CopernicusDEM R package,
but also for the ICESat2R package,
The current version of IceSat2R on CRAN (https://CRAN.R-project.org/package=IceSat2R) is 1.04. Are you still actively supporting IceSat2R? Are you planning to add any major features?
Yes, I still actively support IceSat2R. I always respond to issues related to the package and fix potential bugs or errors. The NEWS page of the package includes the updates since the first upload of the code base to Github.
I don’t plan to add any new features in the near future, but I’m open to pull requests in the Github repository if a user would like to include new functionality that could benefit the R programming community.
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Continue reading: Satellite Data with R: Unveiling Earth’s Surface Using the ICESat2R Package
Understanding the Long-Term Implications of ICESat2R
The ICESat2R package creator Lampros Sp. Mouselimis and the R Consortium have shed light on NASA’s ICESat-2 mission utilizing the Advanced Topographic Laser Altimeter System (ATLAS). This R-based tool streamlines the examination of ICESat-2 data, making accessible, processing, and representing the large datasets generated by ATLAS. With this tool, users can better analyze environmental changes like ice-sheet elevation change, sea-ice freeboard, and vegetation canopy height.
Implications and Future Developments
As Mouselimis has reflected, ICESat2R significantly contributes to the R users community and showcases a personal and professional achievement. The usefulness of this package for wider applications in the future depicts a promising development, especially in environmental studies. Data from ICESat-2 can aid research for ice scientists, ecologists, and hydrologists, as well as assist decision-making processes for public authorities and policymakers. Even R users looking for hands-on experience with altimeter data can benefit from it.
Future developments of open-source packages like ICESat2R could enable broader data analysis capabilities. While existing interfaces like Python and Julia are effective, the ICESat2R package offers unique benefits – it allows R programming users to use NASA’s OpenAltimetry interface without requiring any credentials and it comes with detailed documentation.
Actionable Advice
Despite the current unavailability of OpenAltimetry API, once it becomes functional again, users can expect prompt updates from Mouselimis regarding any issues related to this migration. For those interested to better understand the utility of this package, full vignettes explaining the package’s functionality are provided in Mouselimis’ personal blog and uploaded to Github once they’ve been accepted to CRAN. It’s crucial to stay updated and make the most of these resources.
Indicatively, Mouselimis is open to pull requests in the Github repository if users would like to include new functionality that could be advantageous for the R programming community. For those passionate about data analysis, this is a prime opportunity to contribute to the evolution of the package and, by extension, the field.
Overall, packages such as ICESat2R underscore the importance of collaborative development and the power of technology in understanding and addressing complex environmental issues. For anyone interested in data analysis, whether novice or professional, engaging with such tools can lead not only to skills development but also a significant contribution to pressing global matters.