Abstract
The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.
Highlights
In forest monitoring, the diameter at breast height (DBH) is the diameter of a cross-section of a tree trunk 1.3 m above the ground
Biological, and remote sensing factors, generalized regression neural network (GRNN) produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in mean absolute percentage error (MAPE), 1.9655 cm for the root-mean-squared error (RMSE), 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively
The Gompertz model provided the strongest generalizability with the best performance metrics of 1.7622 cm in MAE, 15.63% in MAPE, and 0.5092 in R2, which were even better than the modeling result
Summary
In forest monitoring, the diameter at breast height (DBH) is the diameter of a cross-section of a tree trunk 1.3 m above the ground. Empirical models require some biological factors such as tree height, age, and crown width as independent variable sets, and introduce some regression estimation methods, such as curve, Gompertz, Schumacher, or Richards models, in estimating DBH [6,7,8,9,10]. Their construction is intuitive and simple, the variability of the empirical relationship between the dependent and independent variables means the empirical models are less adaptable. The combination further increases the complexity and flexibility of the models
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