  Earth observation resources are becoming increasingly indispensable in
disaster relief, damage assessment and related domains. Many unpredicted
factors, such as the change of observation task requirements, to the occurring
of bad weather and resource failures, may cause the scheduled observation
scheme to become infeasible. Therefore, it is crucial to be able to promptly
and maybe frequently develop high-quality replanned observation schemes that
minimize the effects on the scheduled tasks. A bottom-up distributed
coordinated framework together with an improved contract net are proposed to
facilitate the dynamic task replanning for heterogeneous Earth observation
resources. This hierarchical framework consists of three levels, namely,
neighboring resource coordination, single planning center coordination, and
multiple planning center coordination. Observation tasks affected by
unpredicted factors are assigned and treated along with a bottom-up route from
resources to planning centers. This bottom-up distributed coordinated framework
transfers part of the computing load to various nodes of the observation
systems to allocate tasks more efficiently and robustly. To support the prompt
assignment of large-scale tasks to proper Earth observation resources in
dynamic environments, we propose a multiround combinatorial allocation (MCA)
method. Moreover, a new float interval-based local search algorithm is proposed
to obtain the promising planning scheme more quickly. The experiments
demonstrate that the MCA method can achieve a better task completion rate for
large-scale tasks with satisfactory time efficiency. It also demonstrates that
this method can help to efficiently obtain replanning schemes based on original
scheme in dynamic environments.
