Jason R. Janke, Ph.D.
Assistant Professor
E-mail: jason.janke@usm.edu
Office: Arthell Kelley Hall, Room 117
Phone : (601) 266-5985
Fax : (601) 266-6219
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Jason R. Janke, Ph.D. E-mail: jason.janke@usm.edu
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Education B.A. 1997 Valparaiso University |
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My research attempts to integrate GIS and remote sensing technologies to investigate problems in physical geography. My dissertation work, focusing on rock glaciers in the Front Range, exemplifies this approach. In the first paper, I investigated the distribution and topoclimates of rock glaciers and temperate glaciers using GIS overlay with DEM variables and a series of statistical tests. In the second paper, I created a predictive GIS permafrost distribution model using a multinomial logistics regression based on rock glacier DEM variables and a land cover weighting procedure, derived from TM satellite imagery. Uncertainty of the model was evaluated using MAAT data obtained from climate stations, BTS measurements, and a GIS Monte Carlo simulation technique. Scenarios were run to predict permafrost extent for future and past climates, the topic of a third paper. Visual, animated models were developed to demonstrate changes in the areal extent of permafrost distribution and assess spatial uncertainty, a valuable tool for identifying hazards associated with melting permafrost. In the fourth paper, I obtained a 40-plus year record of flow by updating displacement networks on local rock glaciers. Error was graphically represented in a series of maps. The flow rates have not significantly changed; therefore, climate and talus input have remained steady. The final section of this project utilizes a combination of photogrammetric and GIS measurements to monitor horizontal and vertical flow. Orthophotos and DEMs for dates ranging from 1970 to 1999 were created using softcopy photogrammetry software and were imported into a GIS. Large rocks were outlined, a centroid was determined, and points were connected on the temporal sequence to track flow. Kriging was utilized to convert measurements into a continuous field, and change was detected by subtracting different temporal grids. Spatial uncertainty of horizontal movement was assessed by deriving a mean error term for each point, which was later converted to a continuous field, whereas vertical error was represented by a global error term and a local relative error method. The methodology employed is unique in that both GIS and photogrammetry were used to track flow and address spatial uncertainty, an important variable that is neglected in other studies. Although uncertainty is often high, the methodology provides a reasonable account of long-term flow, which identifies potential hazards associated with melting ice or a climatically induced signal.
URL: http://www.usm.edu/geog/faculty/raber.htm |