Show simple item record

dc.contributor.authorPanda, Santosh K.
dc.date.accessioned2018-08-07T17:37:33Z
dc.date.available2018-08-07T17:37:33Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11122/9106
dc.descriptionThesis (Ph.D.) University of Alaska Fairbanks, 2011
dc.description.abstractAn up-to-date permafrost distribution map is critical for making engineering decisions during the planning and design of any engineering project in Interior Alaska. I used a combination of empirical-statistical and remote sensing techniques to generate a high-resolution spatially continuous near-surface (< 1.6 m) permafrost map by exploiting the correlative relationships between permafrost and biophysical terrain parameters. A Binary Logistic Regression (BLR) model was used to establish the relationship between vegetation type, aspect-slope and permafrost presence. The logistic coefficients for each variable class obtained from the BLR model were supplied to respective variable classes mapped from remotely sensed data to estimate permafrost probability for every pixel. The BLR model predicts permafrost presence/absence at an accuracy of 88%. Near-surface permafrost occupies 37% of the total study area. A permafrost map based on the interpretation of airborne electromagnetic (EM) resistivity data shows 22.5 -- 43.5% of the total study area as underlain by permafrost. Permafrost distribution statistics from both the maps suggest near-surface permafrost distribution in the study area is sporadic (10 -- 50 % of the area underlain by permafrost). Changes in air temperature and/or winter snow depth are important factors responsible for permafrost aggradation or degradation. I evaluated the effects of past and recent (1941-2008) climate changes on permafrost and active-layer dynamics at selected locations using the Geophysical Institute Permafrost Laboratory model. Results revealed that active-layer thickness reached 0.58 and 1.0 m, and mean annual permafrost temperature increased by 1.6 and 1.7 �C during 1966-1994 at two sites in response to increased mean annual air temperature, mean summer air temperature and winter snow depth. The study found that active-layer thickness is not only a function of summer air temperature but also of mean annual air temperature and winter snow depth. Model simulation with a projected (2008-2098) climate scenario predicts 0.22 m loss of near-surface permafrost at one site and complete permafrost disappearance at another site by 2098. The contrasting permafrost behaviors at different sites under similar climate scenarios highlight the role of soil type and ground ice volume on permafrost dynamics; these factors determine permafrost resilience under a warming climate.
dc.subjectGeology
dc.subjectRemote sensing
dc.subjectGeographic information science and geodesy
dc.titlePermafrost Distribution Mapping And Temperature Modeling Along The Alaska Highway Corridor, Interior Alaska
dc.typeThesis
dc.type.degreephd
dc.identifier.departmentDepartment of Geology and Geophysics
dc.contributor.chairPrakash, Anupma
refterms.dateFOA2020-03-05T16:37:38Z


Files in this item

Thumbnail
Name:
Panda_S_2011.pdf
Size:
2.882Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record