Traditional mission scheduling methods are unable to meet the timeliness requirements of emergency surveying. Different size and overlaps of different missions lead to inefficient scheduling and poor mission returns. Especially for UAVs, based on their agile and flexible ability, the scheduling result becomes diversiform; as affected by environment and unmanned aerial vehicle performance, different scheduling will lead to different time costs and mission payoffs. An effective scheduling solution is to arrange the UAVs reasonably to complete as many as missions possible with better quality and satisfaction of different demands. This paper proposes a method for mission decomposition or aggregation to generate a mission unit for specific UAVs based on the spatio-temporal constraints of different missions and UAV observation ability demands. In this way, the problems of lack or redundancy of resource scheduling, which can be caused by mission overload, various information demands and spatial overlapping will be effectively reduced. Furthermore, the global efficiency evaluation function is built by considering typical scheduling objectives, such as mission returns, priority and load balancing of resources. Then, an improved ant colony algorithm is designed to acquire an optimal scheduling scheme and the dynamic adjustment strategy is employed. Finally, the correctness and validity are demonstrated by the simulation experiment.