With the increasing occurrence of wildfires globally, quick and effective detection methods are vital. This paper proposes an innovative solution for wildfire detection using Unmanned Aerial Vehicle (UAV)-assisted detection systems. On the other hand, semantic communication, a technology designed for efficient data transmission in specialized tasks, plays a crucial role in next-generation wireless communications systems. In this paper, the deep joint source-channel coding (DJSCC) scheme has been used for efficient image transmission as a deep learning-based semantic communication technique for wildfire detection. DJSCC improves source and channel coding for semantic communications, offering advantages such as improved energy efficiency, reduced latency, and improved reliability compared to traditional source and channel code schemes. In this paper, the transmitter-receiver operations of the UAV communication system are modeled as a DJSCC, and they are jointly trained while taking into account the effects of the fading channel. The encoder transforms captured images into compact feature vectors, subsequently transmitting them using a reduced number of channels to minimize latency. Rather than engaging in the reconstruction of the input image in the receiver, the classifier performs a classification task using the received signals at the receiver. Alternatively, if the recovery of an image is required to understand the spread of the wildfire, the decoder reconstructs it by using the received signal at the receiver.