Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a
knowledge reward function or priority map, indicating the most important areas
to visit. In this paper we propose a method to create priority maps for
monitoring or intervention of dynamic spreading processes such as wildfires.
The presented optimization framework utilizes the properties of positive
systems, in particular the separable structure of value (cost-to-go) functions,
to provide scalable algorithms for surveillance and intervention. We present
results obtained for a 16 and 1000 node example and convey how the priority map
responds to changes in the dynamics of the system. The larger example of 1000
nodes, representing a fictional landscape, shows how the method can integrate
bushfire spreading dynamics, landscape and wind conditions. Finally, we give an
example of combining the proposed method with a travelling salesman problem for
UAV path planning for wildfire intervention.