Estimation of Leaf Area Index Model of Dendrocalamus giganteus Plantation Based on Bayesian Method

The research takes Cangyuan County and Xinping County where Dendrocalamus giganteus is widely cultivated in Yunnan as the study area. Taking the allometric growth equation as the LAI basic model, combined with the data of 73 sample plots on the ground, the model parameters were fitted by nonlinear least squares, Bayesian method with prior information and hierarchical Bayesian method. The fitting effect was evaluated by using the determination coefficient(R²), root mean square error(RMSE) and estimation accuracy(E). The results show that the R², RMSE and E of the traditional least square method and Bayesian method with prior information are 0.4875, 0.0071, 75.31% and 0.4874, 0.0070, 75.31% respectively without adding random effect variables. After introducing the random effect variable, the R², RMSE and E of the hierarchical Bayesian method are 0.6733, 0.0057 and 80.27%, respectively. The estimation effect is significantly improved compared with the least square method and the Bayesian method with prior information. R² increases by 0.1858, RMSE decreases by 0.0014, and E increases by 4.96%. Therefore, for the samples with obvious regional differences, hierarchical Bayesian method can significantly improve the accuracy of model parameter estimation, which is suitable for the model parameter estimation of sampling data on medium and large scales.