Bursaphelenches xylophilus disease, also known as pine wilt disease, is a fatal one caused by the parasitism of pine wilt nematode in pine trees. It’s difficult to prevent and control the disease because of rapid infection and spread. The recognition and estimation of the disease play a significant role in the protection of forest resources and ecological environment in China. Studies have shown that the chlorophyll and water content in Pinus Massoniana will reduce gradually when the pest degree deepens and the spectral reflectance of Pinus Massoniana in different pest degree appears to be greatly different. Therefore, the spectral analysis technique has unique advantages in pest degree estimation. In this paper, the variation regularities of the spectral characteristic parameters were studied for the samples with different pest degrees. Then the measured spectral characteristic parameters were taken as independent variables and the quantization of samples’ disease degree as dependent variables to construct an estimation model for the pest degree with the help of linear regression equation. Valuable efforts made on the spectral characteristic selection and the evaluation model could provide significant guidance for the estimation of Bursaphelenchus xylophilus disease, as well as providing scientific support and application reference for related research and local precision agriculture.Firstly, the variation of the spectral reflectance in the green, red and near infrared bands was studied; six spectral characteristic parameters indicating the degree of pest damage were conducted, including the peak reflectance and their corresponding wavelengths (positions) of the green, and red bands, as well as the slope and position of the red edge; the correlation between spectral characteristic parameter and pest degree was analyzed. Next, the liner models for estimating the pest degree of Pinus Massoniana samples were constructed. The steps consisted of (1) calculating the reflectance of spectral characteristic parameters such as green peak (RGP), reflection of red band (FRB) and red edge slope (RES) for samples in healthy, mild, moderate and severe pest degree; (2) quantizing the pest degree of these samples; (3) taking the measured spectral characteristic parameters as the independent variables and the quantitative value of the pest degree as the dependent variables, and constructing the pest degree estimation models with the linear regression equation. In the experiments, the Pinus Massoniana samples from Yongsheng Forest Farm and the area of Maohe Zhai in Fuling District of Chongqing were investigated and Pinus Massoniana trees belonging to healthy, infected and completely dead categories were tested and monitored separately and randomly. An ASD field spectrometer, FieldSepc4 with a range of 350 to 1 100 nm and a resolution of 1nm, was used to collect spectral data for Pinus Massoniana samples. 70 records of effective spectral data for Pinus Massoniana trees collected were divided into five levels, i.e. healthy, infected mildly, moderately, severely as well as dead according to the different pest levels. Spectral data was then processed by Matlab software to generate the spectral reflectance curves. The spectral characteristic parameters with wavelength covering the green region (510～580 nm), the red region (620～680 nm) and the near infrared region (680～780 nm) were calculated and the estimation models for pest degree were constructed. The results demonstrated that: (1) the spectral characteristics for dead samples such as green peak and red band disappear, at the same time, the steep uptrend of the red edge is leveled. For the remaining kinds of samples, the spectral parameters RGP, FRB and RES are negatively correlated with the pest degree. The deeper the pest degree is, the smaller the parameter is, that is Health (RGP)>Mild (RGP)>Moderate (RGP)>Severe (RGP), Health (FRB)>Mild (FRB)>Moderate (FRB)>Severe (FRB) and Health (RES)>Mild (RES)>Moderate (RES)>Severe (RES); (2) with the deepening of pest degrees, GPP moves towards the longer wave direction called “red shift” in the green peak position while RBP and REP move towards the shorter wave direction called “blue shift” in the red valley position as well as the red edge position; (3) compared with univariate linear estimation models, the bivariate models generate higher correlation coefficients, but smaller estimation error and residual. In the experiment, the two Pinus Massoniana trees were estimated. The results for the bivariate linear estimation models were PD=2.990 7 and PD=3.679 and corresponded with the actual observations. In our following research, the correlation analysis on the spectral characteristics will be extended to the 1 100～2 500 nm bands.