Abstract: To investigate the early geographical location and incidence rate of Masson pine infected with pine wood nematode disease, in early July 2021, remote sensing images were collected by a hyperspectral imager mounted on UAV, and support vector machines were selected for supervised classification. Based on the inversion model of early disease susceptibility, the geographical location and related information of M. pine in the early stage of disease susceptibility were successfully extracted. The results showed that: (1) Using three-band combined true color images of 460 nm, 525 nm and 635 nm for ROI mapping, M. Pine has a high degree of separation from other ground cover. (2) Based on supervised classification of support vector machine, the geographic location and hyperspectral reflectance data of 741 Masson pines were obtained successfully. (3) Combined with the monitoring model, 64 suspected infected M. pine were extracted. Through random sampling and microscopic examination, the accuracy rate of infected plants in the cluster range of M. pine was 86.67%, that is, the incidence rate of M. pine in the forest was 7.49%. In conclusion, the incidence of pine wood nematode in the natural state of M. pine forest in southern of Sichuan was preliminatively revealed, which is helpful to guide the early and accurate control of pine wood nematode disease in the future.