This report presents the Final design of the Design Synthesis Exercise (DSE) to 'Capture a Small Asteroid and Change its Orbit' at the Faculty of Aerospace Engineering at Delft University of Technology. The bachelor programme 'Aerospace Engineering' comprises several projects enabling students to explore aeronautics and space from different kinds of perspectives. The Design Synthesis Exercise serves as the conclusion to this programme. During this final project students integrate their previously obtained knowledge and skill to examine a specific design problem in groups of ten students for the duration of eleven weeks. This final report is the last in a series of four and documents the detailed design of the concept that was chosen in the mid-term report.

NASA's ICE, Cloud and Land Elevation Satellite-2 (ICESat-2) has been measuring the topography of the Earth's surface since its launch in September 2018. Equipped with a single instrument, namely, the Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 is able to acquire the along track vertical profiles of its laser footprints. While satellite based land classification has traditionally been performed with the use of multi-spectral data, which doesn't consider the vertical structure of the surface in question, the three-dimensional Light Detection and Ranging (LiDAR) product provided by ATLAS allows for the observation of the vertical structure of the illuminated surface. This provides information for the discrimination of surfaces that are only distinctly different from one another in this dimension, such as different types of vegetative species. Moreover, a greater understanding of how the signal interacts with different land types will benefit current and future users of the data. This study presents a first look at the potential of the base scientific data set provided by ATLAS, "ATL03", as a means of land type classification. Features extracted from ATL03 vertical profiles are used to classify multiple land types in The Netherlands, namely, "Artificial Surfaces", "Agricultural Areas", "Forest and Semi-Natural Areas", "Wetlands" and "Water Bodies". 100m grid cells were classified and validated with the CORINE land cover database. The overall classification accuracy was 71.2%, however, after a visual inspection of the misclassification errors it was found that that the actual accuracy was a minimum of 5.5% higher, that is, 76.7%. 51 features were created to discriminate between land classes and their importance per class was analysed. In general, simple statistical parameters, such as the standard deviation and percentile ranges worked well in distinguishing between classes. For the classes with a greater vertical range, such as "Artificial Surfaces", features that described the height and prominence of its scattering surfaces were most important.