Time-lapse and UAV Thermal Imaging of Glacial and Periglacial Environments in the Peruvian Andes (Cordillera Blanca, Peru)

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Jeffrey M McKenzie1, Oliver Wigmore2, Caroline Aubry-Wake1, Bryan G Mark3, Robert Ake Hellstrom4 and Laura Lautz5, (1)McGill University, Montreal, QC, Canada, (2)OSU-Byrd Polar Rsrch Ctr, Columbus, OH, United States, (3)Ohio State University Main Campus, Columbus, OH, United States, (4)Bridgewater State University, Bridgewater, MA, United States, (5)Syracuse University, Syracuse, NY, United States
In the tropics, the acquisition of high-resolution geospatial data of high-mountain glacial and periglacial systems presents unique challenges due to remote site access and very high elevations. For glaciers and hydrologic systems, a key variable of interest is surface temperature as it constrains glacier melt rates, traces hydrologic processes, and is needed for the calibration of energy budget models. We present results from two studies that acquired high resolution temperature data from the Cuchillacocha Glacier, Peru (9.24°S, 77.21°W). The glacier resides on the western drainage of the Cordillera Blanca with an elevation range of 4700 to 6096 m. In the first study we use high resolution time-lapse infrared imagery (5-10 minute interval over 3 days; 0.6 m2 pixel size) to observe diel changes in the surface energy budget of the glacier and to demonstrate how radiation from bare rock adjacent to the glacier may affect melt rates. In the second study we use a newly developed, inexpensive unmanned aerial vehicle (UAV) for high resolution multispectral mapping of the glacier (2 cm resolution orthomosaic and 5 cm resolution DEM). We present results showing how the time-lapse and the high-resolution UAV imagery can be combined to further strengthen our understanding of the Cuchillacocha Glacier’s energy budget and possible insights about turbulent heat fluxes. While the new instruments provide unprecedented data acquisition capabilities, there is an outstanding need for proper data correction. Spatial/thermal control points and post-processing algorithms are needed to produce quantifiable datasets. For example, our post-processed time-lapse imagery has an r2 > 0.9 after emissivity, transmissivity and offset corrections.