Peirce Lewis Obit in Annals | Coffee Hour history | Welcome new post-doc

IMAGE OF THE WEEK

Kaltura screnshotFriday, Sept. 11 was the kickoff Coffee Hour lecture for the fall 2020 semester.  If you missed it and would like to view the recording of Kaitlin Harbeck’s talk on ICESaT-2 or view any previously recorded Coffee Hour lectures, you can go to the Department of Geography Coffee Hour Channel. Each recent semester of Coffee Hour is saved as a playlist, so you can easily find the speaker or topic of interest.

GOOD NEWS

Mikael Hiestand was quoted in the article, “Introducing Students to Scientific Python for Atmospheric Science,” in the September 2020 issue of the Bulletin of the American Meteorological Society.

Welcome to Tatiana Gumucio who has joined as a post-doctoral scholar on Helen Gretrex’s AXA-XL grant on humanitarian weather response in Somalia.

COFFEE HOUR

Coffee Hour with Qiusheng Wu is on Friday, September 25, 2020
Using Google Earth Engine for interactive mapping and analysis of large-scale geospatial datasets

Google Earth Engine is a free cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, Earth Engine has become very popular in the geospatial community and it has been used for numerous environmental applications at local, regional, and global scales.

NEWS

Peirce F. Lewis, 1927–2018

by Ben Marsh & Joseph Wood in the Annals of the American Association of Geographers

All geography rests, finally, upon the land. Few geographers have stayed as connected to the land throughout their professional careers as Peirce F. Lewis, few relished the immediacy of being in the field more than he did, and few imbued that deep love into as many students and students-of-students as he was able to. Land is in the very name of the discipline that he championed for over five decades: cultural landscape. That term refers to the world of human experience, “nearly everything we can see when we go outdoors” (Lewis 1979a, 12).

50+ years of Coffee Hour

Fall 2018 was the fiftieth anniversary of the Department of Geography Coffee Hour, weekly socializing and a lecture on Friday afternoons. Although the methods have modernized, Coffee Hour remains true to its purpose, which Peirce Lewis and Wilbur Zelinksy described in a 1987 article in the Professional Geographer as “creating and preserving a sense of intellectual and social community within the department.”

RECENTLY PUBLISHED

Desktop versus immersive virtual environments: effects on spatial learning

Jiayan Zhao, Tesalee Sensibaugh, Bobby Bodenheimer, Timothy P. McNamara, Alina Nazareth, Nora Newcombe, Meredith Minear & Alexander Klippel
Spatial Cognition & Computation
DOI: 10.1080/13875868.2020.1817925
Although immersive virtual reality is attractive to users, we know relatively little about whether higher immersion levels increase or decrease spatial learning outcomes. In addition, questions remain about how different approaches to travel within a virtual environment affect spatial learning. In this paper, we investigated the role of immersion (desktop computer versus HTC Vive) and teleportation in spatial learning. Results showed few differences between conditions, favoring, if anything, the desktop environment. There seems to be no advantage of using continuous travel over teleportation, or using the Vive with teleportation compared to a desktop computer. Discussing the results, we look critically at the experimental design, identify potentially confounding variables, and suggest avenues for future research.

Automatic detection of volcanic surface deformation using deep learning

Sun, J., Wauthier, C., Stephens, K., Gervais, M., Cervone, G., La Femina, P., & Higgins, M.
Journal of Geophysical Research: Solid Earth
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JB019840
Interferometric Synthetic Aperture Radar (InSAR) provides subcentimetric measurements of surface displacements, which are key for characterizing and monitoring magmatic processes in volcanic regions. The abundant measurements of surface displacements in multitemporal InSAR data routinely acquired by SAR satellites can facilitate near real‐time volcano monitoring on a global basis. However, the presence of atmospheric signals in interferograms complicates the interpretation of those InSAR measurements, which can even lead to a misinterpretation of InSAR signals and volcanic unrest. Given the vast quantities of SAR data available, an automatic InSAR data processing and denoising approach is required to separate volcanic signals that are cause of concern from atmospheric signals and noise. In this study, we employ a deep learning strategy that directly removes atmospheric and other noise signals from time‐consecutive unwrapped surface displacements obtained through an InSAR time series approach using an end‐to‐end convolutional neural network (CNN) with an encoder‐decoder architecture, modified U‐net. The CNN is trained with simulated synthetic unwrapped surface displacement maps and is then applied to real InSAR data. Our proposed architecture is capable of detecting dynamic spatio‐temporal patterns of volcanic surface displacements. We find that an ensemble‐average strategy is recommended to stabilize detected results for varying deformation rates and signal‐to‐noise ratios (SNRs). A case study is also presented where this method is applied to InSAR data covering Masaya volcano, Nicaragua and the results are validated using continuous GPS data. The results confirm that our network can indeed efficiently suppress atmospheric and other noise to reveal the noise‐free surface deformation.

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