Abstract: In this research, Pinus densata in Shangri-La was taken as the research object. Based on permanent sample plots of National Forest Inventory data in 1987–2017 and the corresponding Landsat TM/OLI data, the remote sensing factors were extracted. Based on these factors, their variables were calculated for aboveground biomass modelling using multilinear regression (MLR) and random forest (RF) methods. The variables include, the average annual variation, the 5-years-interval and 10-years-interval variations and the change rate. The results showed that model effect of RF was better than that of MLR in the 5 variables. In RF models, the best modeling effect was to use the change rate of 5 years, the R² was 0.956, the RMSE in the modeling effect was 0.664 t/(hm²·a), and the RMSE in the prediction effect was 2.285 t/(hm²·a). The texture factors are the most contributing factor in the aboveground biomass change of P. densata modeling. The dynamic change estimating model of aboveground biomass of P. densata based on the remote sensing factors' change rate can improve the estimation accuracy of biomass change, and it provides reference for the dynamic estimation of forest aboveground biomass.