IN002-07
Fusion of ICESat-2 and complementary remote sensing data for interactive visualization in Jupyter

Monday, 7 December 2020: 07:24
Virtual
Alice Cima1, Facundo Sapienza1, Tasha Snow2,3, Shane Grigsby4, Lindsey Justine Heagy1, Fernando Perez1 and Matthew Siegfried4, (1)University of California, Berkeley, Statistics, Berkeley, CA, United States, (2)University of Colorado Boulder, Boulder, CO, United States, (3)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (4)Colorado School of Mines, Geophysics, Golden, CO, United States
Recorded Presentation
Abstract:
A fundamental component of any exploratory data analysis consists of visualizing data and features in a meaningful way. This step can be challenging in high-dimensional settings and with very large datasets. Data from NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) fall into this category, as the satellite provides 12 different products, each with different spatial and temporal resolutions that highlight different features that the user can exploit and combine to answer scientific questions. In order to facilitate data exploration and understanding in this context, we introduce some visualization tools that can at the same time handle large datasets and ensure fluent interactivity and customization.

Some visualization tools for ICESat-2 are already available, such as OpenAltimetry, an online platform that allows the user to select specific areas, dates and products to obtain estimated elevation profiles and photon information. Our project shares with OpenAltimetry the emphasis on interactivity, but differs in that, by virtue of being available as Python tools, it is specifically designed to be embedded in a programmable and reproducible scientific workflow. It also focuses on data integration between ATL06/03 and external sources (e.g. VIIRS imagery or other observational satellites, and climate model outputs). We aim to provide a tool for researchers to filter, analyze, and interpret ATL06 height data through an interface that combines various ATL06 parameters, information from other satellites, and statistics and time-series on user-defined areas. To accomplish these goals, we take advantage of the icepyx library for fetching data, JupyterLab to set up our workflow, and plotting and dashboarding tools in the Jupyter and Pangeo ecosystems to combine informative and responsive visualizations in an interactive computing environment.

In this presentation, we will provide an overview of the architecture behind our visualization tools and a demonstration of the possibilities they offer to the research community.

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