Research Papers

Stand basal area model for Cunninghamia lanceolata (Lamb.) Hook. plantations based on a multilevel nonlinear mixed-effect model across south-eastern China

DOI: 10.2989/20702620.2013.769750
Author(s): LiFang ZhaoCenter for Earth Observation and Digital Earth, China, ChunMing LiInstitute of Forest Resource Information Techniques, China


Based on a multilevel nonlinear mixed-effect model approach, a stand basal area model was developed for Cunninghamia lanceolata (Lamb.) Hook. plantations belonging to the National Forest Inventory in China. The database consists of 583 plots embracing 18 different blocks within three seed source sites in this study. Of the plots, 80% were randomly selected for model fitting and 20% were carried out for model validation. The modified Chapman–Richards and Schumacher models were evaluated to find a basic model. The explanatory variables included stand dominant height, stand age and total number of stems per hectare. After selection of the basic model, the fitting and predictive ability of a multilevel nonlinear mixed-effect model was analysed. Site-, block- and plot-level random-effects terms were assessed for their contributions to improve model prediction over the ordinary least squares (OLS) method widely used in forest management. In addition, within-plot variance–covariance structure was taken into account owing to the repeated measurements and hierarchical structure of the data set. When evaluating the predictive accuracy of the final model, the first measurement was used for estimation of random parameters. The Chapman–Richards model was finally selected for the basic model based on model-fitting statistics, and both the fitting model and validation data with site-, block- and plot-level random effects showed a substantial improvement compared with the OLS method. After taking into account a reasonable variance–covariance structure, the model performed better than the model developed using only random effects.

Get new issue alerts for Southern Forests: a Journal of Forest Science