Qualitative and quantitative analysis of multi-sensor data is becoming increasingly useful as a method of improving our understanding of complex environments, and can be an effective tool in the arsenal to help climate scientists to predict sea level rise due to change in the mass balance of large glaciers in the Arctic and Antarctic. A novel approach to remote sensing of the continuously changing polar environment involves the use of coincident RADARSAT-2 synthetic aperture radar (SAR) imagery and Landsat 7 visible/near-infrared imagery, combined with digital elevation models (DEM) developed from Multiple Altimeter Beam Experimental Lidar (MABEL) data sets.
MABEL is a scaled down model of the lidar altimeter that will eventually be flown on ICESat-2, and provides dense along-track and moderate slope (cross-track) elevation data over narrow (~198 m) aircraft transects. Because glacial terrain consists of steep slopes, crevices, glacial lakes, and outflow into the sea, accurate slope information is critical to our understanding of any changes that may be happening in the ice sheets. RADARSAT-2 operates in the C-band, at a wavelength of 5.55 cm, and was chosen partly for its ability to image the Earth under all atmospheric conditions, including clouds. The SAR images not only provide spatial context for the elevation data found using the lidar, but also offer key insights into the consistency of the snow and ice making up the glacier, giving us some idea of mean temperature and surface conditions on the ice sheet. Finally, Landsat 7 images provide us with information on the extent of the glacier, and additional understanding of the state of the glacial surface.
To aid in the analysis of the three data sets, proper preparation of each data set must first be performed. For the lidar data, this required the development of a new data reduction technique, based on statistical analysis, to reduce the number of received photons to those representing only the surface return. Accordingly, the raw SAR images require calibration, speckle reduction, and geocorrection, before they can be used. Landsat 7 bands are selected to provide the most contrast between rock, snow, and other surface features, and compiled into a three-band red, green, blue (RGB) image.
By qualitatively analyzing images and data taken only a short time apart using multiple imaging modalities, we are able to accurately compare glacial surface features to elevation provided by MABEL, with the goal of increasing our understanding of how the glacier is changing over time.
Quantitative analysis performed throughout this thesis has indicated that there is a strong correlation between top-of-the-atmosphere reflectance (Landsat 7), σ,0-calibrated HH and HV polarized backscatter coefficients (RADARSAT-2), elevation (MABEL), and various surface features and glacial zones on the ice sheet. By comparing data from unknown or mixed surfaces to known quantities scientists can effectively estimate the type of glacial zone the area of interest occurs in. Climate scientists can then use this data, along with long-term digital elevations models, as a measure of predicting climate change.
Imaging Science (MS)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
J.A.N. van Aardt
Horan, Kimberly H., "The Use of Coincident Synthetic Aperture Radar and Visible Imagery to Aid in the Analysis of Photon-Counting Lidar Data Sets Over Complex Ice/Snow Surfaces" (2013). Thesis. Rochester Institute of Technology. Accessed from
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