Pine wilt disease(PWD),characterized by rapid onset and high mortality rate constitutes a severe threat to forest ecological security in China.Consequntly,the timely and accurate monitoring of infected trees is imperative for mitigating its spread..Centered on the physiological and morphological attributes of PWD-infected pine trees across varying disease stages(early,middle,late,and terminal),this paper systematically reviews the monitoring principles and research progress in remote sensing unmanned aerial vehicle(UAV)remote sensing,and multi-source remote sensing data fusion technologies.The analysis reveals that while remote sensing exhibits distinct advantages in large-scale macroscopic surveys of infected trees at the middle,late,and terminal stages,its efficacy in detecting faint early-stage signals is hampered by constraints in spatial,temporal,and spectral resolution.UAV-based visible-light and multispectral remote sensing combine high spatial resolution with relatively cost-effectiveness for middle- and late-stage monitoring,however,they frequently fail to capture early physiological stress signals.Conversely,UAV hyperspectral remote sensing,benefiting from its continuous narrow-band characteristics,serves as the most potent approach for discerning subtle spectral changes at the early stage,despite challenges associated with high cost and complex data processing.By integrating multi-dimensional spatiotemporal information,multi-source remote sensing data fusion can mitigate the limitations inherent in individual data sources,representing an important development direction for achieving fine-scale,full-cycle monitoring of infected trees.Finally,considering the limitations of existing studies,this paper outlines future perspective on the development of low-cost,high-sensitivity sensors and the construction of full-cycle intelligent early-warning platforms,providing theoretical support and technical references for the scientific prevention and control of pine wilt disease.