Abstract: Objective This study aims to enhance the accuracy of eucalyptus stem volume estimation through remote sensing by ameliorating the detection algorithm to acquire precise age variables, thereby mitigating the saturation issue of eucalyptus stem volume.Method Multi-temporal Landsat-8 and Sentinel-2 data were utilized as the primary data sources. The time series growth curve of eucalyptus was analyzed, and a combination of linear interpolation and dynamic recognition approaches was adopted. The simulated continuous change detection and classification (CCDC) algorithm was employed to identify the felling points, and the least squares method along with significant changes in slope and intercept was used to analyze the felling points and estimate the age of eucalyptus. On this basis, two variable sets were constructed using remote sensing features and eucalyptus age: variable set 1 (comprising band values, vegetation indices, and texture features) and variable set 2 (consisting of variable set 1 and age). Four models, namely multiple linear regression (MLR), k-nearest neighbor (KNN), random forest (RF), and support vector regression (SVR), were utilized to estimate eucalyptus stem volume.Result The recognition accuracy of the simulated CCDC detection algorithm for 100 samples was 82%, and the distance correlation coefficient between age and stem volume was 0.71, which was significantly higher than that of other remote sensing variables. The results of eucalyptus stem volume estimation indicated that the R² of variable set 1 was 0.40, the RMSE (root mean square error) was 41.11 m³·hm^–2, and the rRMSE (relative root mean square error) was 34%. The R² of variable set 2 was 0.83, the RMSE was 22.08 m³·hm^–2, and the rRMSE was 18%. In variable set 1, the model calculation outcomes were weak, with a low R² value, high RMSE, and high rRMSE. However, with the introduction of the age variable in variable set 2, the model calculation results were notably improved, especially in the SVR model, where the R² increased to 0.83, and the RMSE and rRMSE decreased obviously. The introduction of the age variable enhanced the accuracy of the eucalyptus stem volume estimation model.Conclusion The improved simulated CCDC change detection method has augmented the accuracy of age variables, and age is significantly correlated with stem volume. The introduction of the age variable has significantly enhanced the estimation performance of the model, with the estimation accuracy increasing by 16 percentage points.

Key words: forestry remote sensing, change detection, stem volume, age, Eucalyptus