Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Doing Business As Name:University of Alaska Fairbanks Campus
  • Benjamin M Jones
  • (907) 474-7209
Award Date:11/27/2019
Estimated Total Award Amount: $ 295,256
Funds Obligated to Date: $ 295,256
  • FY 2020=$295,256
Start Date:12/01/2019
End Date:11/30/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.083
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:RII Track-4: PermaSense: Investigating Permafrost Landscapes in Transition Using Multidimensional Remote Sensing, Data Fusion, and Machine Learning Techniques
Federal Award ID Number:1929170
DUNS ID:615245164
Parent DUNS ID:048679567
Program:EPSCoR Research Infrastructure
Program Officer:
  • Andrea Johnson
  • (703) 292-5164

Awardee Location

Street:West Ridge Research Bldg 008
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of Connecticut
Street:438 Whitney Road Ext., Unit 1133
County:Storrs Mansfield
Cong. District:02

Abstract at Time of Award

Permafrost underlies around 25% of the Northern Hemisphere terrestrial landscape. Its degradation is impacting northern landscapes and societies. With an increase in the number of remote sensing platforms since the early 2000s, the ability to detect and measure permafrost-region disturbances over large areas has become more feasible. This research, a project we call PermaSense, will provide funding to increase the research capacity of a non-tenure track Research Assistant Faculty member and an early career post-doctoral researcher through extended visits, training, and collaboration at the University of Connecticut (UConn) Department of Natural Resources & the Environment (DNRE). PermaSense will allow the PI and postdoc to build upon their permafrost-region field and remote sensing research program by acquiring new data fusion and machine learning techniques. PermaSense products are directly relevant to the State of Alaska and the nation. The skills and knowledge transfer gained during this project will increase the capacity of permafrost research and remote sensing at the University of Alaska Fairbanks. The research addresses several of the Interagency Arctic Research Policy Committee (IARPC) performance elements related to permafrost, terrestrial ecosystems, coastal resilience, and environmental intelligence as specified in the FY2017-2021 Arctic Research Plan. Permafrost is defined as ground that remains at or below 0 degrees Celsius for at least two consecutive years. Disturbance and warming of near-surface permafrost may lead to widespread terrain instability in ice-rich permafrost regions, impacting ecosystems, hydrology, infrastructure, society, and soil-carbon dynamics. Remote sensing is an important resource for observing, documenting, and better understanding landscape change from local to pan-Arctic scales. However, no one remote sensing tool is particularly suited for detecting and observing the suite of landscape change scenarios associated with transitioning permafrost. PermaSense will investigate myriad land surface changes occurring in permafrost regions using multidimensional remote sensing, data fusion, and machine learning techniques. PermaSense will enhance methodological developments and adaptations to unseal faster, deeper and more accurately analyze large volumes of multidimensional remote-sensing data to address the guiding research question: How extensive is contemporary permafrost degradation in the Arctic and Subarctic? We will conduct an analysis of multidimensional remote sensing observations at four representative permafrost-region study areas that capture the variability in the lateral extent of permafrost. The inherent differences in ecology, climate, landscape history, and their role in transitioning permafrost regions will be tested using common approaches across all sites as well as value-added products available for particular regions. PermaSense will develop an online resource and toolset that will provide spatially and temporally scalable information on permafrost region disturbances and a tool for local planning activities, the scientific community, and regional decision-makers tasked with responding to permafrost regions in transition. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Publications Produced as a Result of this Research

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Jones, Benjamin M and Tape, Ken D and Clark, Jason A and Nitze, Ingmar and Grosse, Guido and Disbrow, Jeff "Increase in beaver dams controls surface water and thermokarst dynamics in an Arctic tundra region, Baldwin Peninsula, northwestern Alaska" Environmental Research Letters, v.15, 2020, p.. doi:10.1088/1748-9326/ab80f1 Citation details  

Zhang, Weixing and Liljedahl, Anna K. and Kanevskiy, Mikhail and Epstein, Howard E. and Jones, Benjamin M. and Jorgenson, M. Torre and Kent, Kelcy "Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images" Remote Sensing, v.12, 2020, p.. doi:10.3390/rs12071085 Citation details  

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.