IMAGE OF THE WEEK
Dry forest margins in the western United States may be more resilient to climate change than previously thought if managed appropriately, according to a study published by Lucas Harris and Alan Talyor in Ecosphere. See news story. Image: Lucas Harris.
Jiayan Zhao successfully defended his dissertation on September 21.
Call for maps: Shelter: An Atlas endeavors to map shelter in its myriad contexts and conditions and at all scales of research and geography. As a project of Guerrilla Cartography, Shelter: An Atlas is a non-profit venture and will be financed through crowd-funding and grants. Money raised to fund the project will finance the printing and distribution of a full-color 12” x 12” bound volume. All maps are due October 31, 2020, 11:59 PM PST. See submission guidelines here.
September 25, the Thinking Spatially symposium will explore the topics of politics and polarization. The symposium is for everyone interested in politics, partisanship, idealism, voting patterns, racism, civil rights, community development, mapping, and more. View the full schedule, featured presenters, and register to attend. 9 a.m.-noon, via Zoom.
Anthony Robinson will serve as a panelist on the topic, “Social Engineering with Data: Disinformation & Destabilization of Geo-Political Order” at The Institute for Computational Data Sciences virtual symposium, “The Data Deluge: Opportunities and Challenges,” on October 22–23. Registration is open.
Coffee Hour with Qiusheng Wu
Using Google Earth Engine for interactive mapping and analysis of large-scale geospatial datasets
- 4:00 p.m., Friday, September 25, 2020
- Coffee Hour to Go on Zoom
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. In this presentation, I will first introduce the geemap Python package (https://giswqs.github.io/geemap) for interactive mapping and analysis with Earth Engine. Then, I will introduce the Earth Engine plugin for QGIS along with 300+ Python examples. Lastly, I will demonstrate how Earth Engine can be used for automated mapping of surface water and wetland inundation dynamics with 1-m resolution aerial imagery and LiDAR data.
A warming climate and more frequent wildfires do not necessarily mean the western United States will see the forest loss that many scientists expect. Dry forest margins may be more resilient to climate change than previously thought if managed appropriately, according to Penn State researchers.
ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
Seroussi, S. Nowicki, A. Payne, H. Goelzer, W. Lipscomb, A. Abe-Ouchi, C. Agosta, T. Albrecht, X. Asay-Davis, A. Barthel, R. Calov, R. Cullather, C. Dumas, B.. Galton-Fenzi, R. Gladstone, N. Golledge, J. Gregory, R. Greve1, T. Hattermann, M. Hoffman, A. Humbert, P. Huybrechts, N. Jourdain, T. Kleiner, E. Larour, G. Leguy, D. Lowry, C. Little, M. Morlighem, F. Pattyn, T. Pelle, S. Price, A. Quiquet, R. Reese, N. Schlegel, A. Shepherd, E. Simon, R. Smith, F. Straneo, S. Sun, L. Trusel, J. Van Breedam, R. van de Wal, R. Winkelmann, C. Zhao, T. Zhang, and T. Zwinger.
Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.
The Effects of Visual Realism on Spatial Memory and Exploration Patterns in Virtual Reality
Jiawei Huang and Alexander Klippel
26th ACM Symposium on Virtual Reality Software and Technology (VRST ’20) https://doi.org/10.1145/3385956.3418945
Understanding the effects of environmental features such as visual realism on spatial memory can inform a human-centered design of virtual environments. This paper investigates the effects of visual realism on object location memory in virtual reality, taking account of individual differences, gaze, and locomotion. Participants freely explored two environments which varied in visual realism, and then recalled the locations of objects by returning the misplaced objects back to original locations. Overall, we did not find a significant relationship between visual realism and object location memory. We found, however, that individual differences such as spatial ability and gender accounted for more variance than visual realism. Gaze and locomotion analysis suggest that participants exhibited longer gaze duration and more clustered movement patterns in the low realism condition. Preliminary inspection further found that loco-motion hotspots coincided with objects that showed a significant gaze time difference between high and low visual realism levels. These results suggest that high visual realism still provides positive spatial learning affordances but the effects are more intricate.