With the increasing size of wildfires (NationalInteragency Fire Center [21]) a need for effec-tive ways of monitoring and containing thefires spread has emerged. The use of un-manned aerial vehicles (UAVs) for optical mon-itoring of a spreading wildfire is appropriatedue to e.g., the longevity of missions and thehazardous environment.With the increasing size of fires more UAVsare needed to effectively monitor an area, lead-ing to more potential conflicts between UAVs,thereby requiring the need for communicationif a UAV were to deviate from its plannedroute. However there are many situationswhere communication between UAVs is notpractical or even impossible. This project there-fore explores zoning methods that will alloweach agent to have navigational freedom whilenot needing to communicate in order to avoidconflicts.This problem is formulated as a 2-stagestochastic linear programming problem, thatreflects the zoning and flight stage, wherethe environmental uncertainty during flightshould be considered when constructing zones.This is solved using two zoning methods, in-spired by the literature (Khemakhem et al.[18]) as well as a novel routing-based cluster-ing method proposed by the authors. These arethen compared using cumulative route scoresfor routes generated in the resulting zones.Their applicability for dynamic environmentsis further tested by considering how often eachof the zoning solutions routes can be optimallyupdated while respecting zone boundaries.Lastly, the aforementioned zoning methods arecompared with a traditional routing approach,in order to gauge performance degradationdue to zone restriction