The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.