  Given the importance of forests and their role in maintaining the ecological
balance, which directly affects the planet, the climate, and the life on this
planet, this research presents the problem of forest fire monitoring using
drones. The forest monitoring process is performed continuously to track any
changes in the monitored region within the forest. During fires, drones'
capture data is used to increase the follow-up speed and enhance the control
process of these fires to prevent their spread. The time factor in such
problems determines the success rate of the fire extinguishing process, as
appropriate data at the right time may be the decisive factor in controlling
fires, preventing their spread, extinguishing them, and limiting their losses.
Therefore, this research presented the problem of monitoring task scheduling
for drones in the forest monitoring system. This problem is solved by
developing several algorithms with the aim of minimizing the total completion
time required to carry out all the drones' assigned tasks. System performance
is measured by using 990 instances of three different classes. The performed
experimental results indicated the effectiveness of the proposed algorithms and
their ability to act efficiently to achieve the desired goal. The algorithm
$RID$ achieved the best performance with a percentage rate of up to 90.3% with
a time of 0.088 seconds.
