The goal of the work is to conduct a feasibility study on the topic of visual navigation during lunar landing using neuromorphic hardware and software - specifically event cameras and spiking neural networks. These lightweight and low power devices are promising for the next generations of landers. The event camera is simulated during several optimal descents. The results are processed using convolutional spiking neural networks to produce optical flow, which is then turned into vehicle egomotion estimates using continuous homography relations. It is found that a relevant event-based dataset can be created and, to an extent, validated. The chosen spiking neural networks are capable of recognizing the lander motion from the events, but struggle to focus on local detail and to properly generalize as a result. They however, do not far worse than their non-spiking counterparts.