Structural health monitoring of buildings is useful for a few reasons. It provides information on the usage and cause of damage to a building. This can result in targeted maintenance or allow for potential improvements.

Traditionally, a building can be monitored by installing sensors during construction or maintenance work. These can be strain gauges, inertial measurement units or surveying equipment. However, not all buildings have such sensors installed due to lack of space or the cost of the equipment. To overcome this, a spaceborne technique shows potential, namely multi-temporal interferometric synthetic aperture radar (MT-InSAR). This technique has already been applied to monitor damage to sections of buildings or large structures, for instance facades or deformations of bridges. In the context of entire buildings, the result of an MT-InSAR analysis has not yet been paired with a computational model. This is mainly due to the relatively small scale of a building and low spatial density of the displacement data.

This thesis integrates remote sensing data, acquiring displacements due to mining, with a computational structural finite element model of a church structure. The displacements have been interpolated using MT-InSAR data, which contains projections of nonlinear displacements in vertical and West-East directions. The interpolation has been performed using two techniques. The first is Ordinary Kriging, which is used to obtain a general insight into the deformations and the shape of the deformed region near the church. The second uses the least squares method to fit polynomial shape functions. The resulting displacements of the least squares analysis have been integrated into a nonlinear structural finite element model. The structural model consists of a soil-structure interaction model and nonlinear material properties, and is used to assess crack propagation.

Integrating remote sensing with computational modelling, shows potential in providing a monitoring technique for buildings. The interpolation method can be used to obtain displacements at a building, even when the spatial density of the InSAR analysis is limited. The main limitation is the information on the horizontal displacements obtained by the InSAR technique, where the displacement along the North-South is unknown. Furthermore, the structure and integration can then be performed using a finite element model, which can follow crack propagation and account for soil-structure interaction.

Glacial lake outburst floods (GLOFs) are outbursts caused by the failure of glacial lake moraine dams. Longer ongoing processes, such as moraine dam degradation, or instantaneous events, such as landslides, can trigger dam failure. GLOFs have a catastrophic downstream impact leading to significant economic damages and more than 12000 casualties worldwide until 2015, with Bhutan and Nepal being impacted the most. Climate change causes increasing temperature and precipitation, leading to the expansion of glacial lakes and the destabilisation of glaciers, slopes and moraine dams. Consequently, GLOFs are likely to become more frequent, and glacial lakes require continuous monitoring and analysis to understand and predict GLOF-related hazards.

Since glacial lakes often lie in inaccessible mountainous regions, on-site monitoring is challenging and remote sensing proposes a safe and cost-effective solution. Satellite radar is unaffected by nighttime and clouds, enabling continuous displacement measurements. Interferometric synthetic aperture radar (InSAR) using Sentinel-1 data from 2014 to 2021 was applied at six Himalayan glacial lake areas (Imja, Lunana, Barun, Rolpa, Thulagi and Lumding) to identify potential GLOF hazards and to investigate InSAR's capability as a monitoring tool. Optical, meteorological and topographical data were used to aid in interpreting the InSAR observations; linking displacements to potential hazards and evaluating the limitations of an InSAR-based analysis.

Significant deformation was detected at the terminal moraines of Imja, Thulagi, Rolpa, Lunana and Barun Lakes; on lateral moraines at Rolpa and Lunana Lakes; and on rock glaciers at Imja, Rolpa, Barun and Lunana Lakes. In addition, significant seasonal variation could be distinguished, showing the impact of temperature and precipitation on geomorphological processes and potential hazard developments at glacial lakes. InSAR-related limitations arose in regions with significant topographic variations, extant snow or vegetation covers, and rapid displacements.

This study demonstrates the capability of satellite InSAR as a glacial lake monitoring tool. An InSAR-based analysis is instrumental in highlighting areas from where GLOFs could originate, requiring mitigation measures or further investigation to map the impact of failure. By extending the research frame over multiple years, continuous and long-term monitoring could demonstrate the climatic influence on displacements and GLOF trigger developments.