Abstract and subjects
•Three canopy stratification methods were used in natural oak forests.•Stand basal area model was developed considering canopy stratifications.•Additive predictions of stand basal area in canopy stratifications were solved.
Stand basal area is an important variable for evaluating forest productivity. In this study, four types of growth models were used for modeling the stand basal area of natural oak forests in Hunan Province, China. The results showed that the Schumacher model (Eq. (3)) performed the best. Because of different canopy layers in natural oak secondary forests, it is important to develop stand basal area models considering canopy stratification. Three widely used canopy stratification methods were used for natural oak forests: the clustering analysis of tree height, classification scheme of the International Union of Forest Research Organizations (IUFRO) standard, and canopy competition cut-off height (CCH) method. According to the standards set forth in the National Standards of the People’s Republic of China (GB/T 26424-2010), the forests were divided into two layers: upper and lower. Among the three canopy stratification approaches, the IUFRO method was selected as the best. In addition, it should be noted that the sum of the basal area predictions at different layers can generate inconsistent results, as compared to predictions from the total stand basal area model. To solve the additivity of stand basal area predictions, the additive stand basal area growth model was established by nonlinear seemingly unrelated regression (NSUR). Comparing with the whole stand basal area growth model without stratification, the R2 values of basal area growth models with stratification increased by 4.78% (from 0.9277 to 0.9721). In addition, compared with equations estimated separately, the NSUR method increased R2 by 0.82% (from 0.9721 to 0.9801). The results reveal that the NSUR method can be applied to solve the additivity of stand basal area predictions at each layer and can improve model precision, which provides helpful insights into developing stand growth models in natural forests.