Climate change has been a heated topic in recent years, and the mass loss of ice sheets is one aspect of it. The Antarctic Ice Sheet has experienced certain mass loss in the form of ice-shelf collapse and (sub-)surface melting, but thorough study remains limited due to the remote location of the continent. Therefore, remote sensing is expected to provide valuable information on Antarctica, in order to monitor its mass balance and gain insights on the extent of climate change.

As one of the factors to the mass loss in Antarctica, subsurface melt can be critical yet hard to capture. The limitation of remote sensing data, especially valid optical images over polar regions may add to the issue. This study aims to exploit the potentiality of SAR Interferometry in detecting subsurface melt, as the microwave bands are not affected by weather and illumination, and the interferometric data may provide supportive information reflecting the properties of the physical environment. It is expected that by using this technique, the gap can be filled in when optical images are not available, or pure SAR images are not informative. The technique is applied to two ice shelves in East Antarctica, Roi Baudouin Ice Shelf and Amery Ice Shelf. And this study is expected to be operated over a broader scale such as the Antarctic continent and Greenland.

Point clouds are becoming one of the most common ways to represent geographical data. The scale of acquisition of point clouds is growing steadily. However, point clouds are often very large in storage size and require computationally intensive operations. The integration of point clouds nowadays still face a lot of challenges. This project focuses on one of these challenges; integrating point clouds of different scales and granularity. Solving this challenge enables appealing visualisation, usability for low and high computation powers and geometrical consistency for analysis. The following question is researched: 'To what extent can a vario-scale approach improve integration of point clouds with varying point densities?'. A data model is created that uses importance as an additional dimension. This dimension contains an importance value which is calculated using two methods. Firstly random assignment of values to the points and secondly exact computed values. To compute this value the smallest distances to its nearest neighbour is assigned as importance value. A web application shows the results. Both random and exact methods show an exponential decay in distribution of the importance value. Though the random methods run much faster, the exact methods preserve much more edges and other details.