Suoming Liu

Crown width (CW) is an important indicator for assessing tree health, vitality, and stability, as well as being used to predict forestry models and evaluate forest dynamics. However, acquiring CW data is laborious and time-consuming, making it crucial to establish a convenient and accurate CW prediction model for forest management. In this study, we developed three models capable of conducting calibration: generalized models (GM), quantile regression models (QR), and mixed-effects models (MIXED). The aim was to effectively improve the prediction accuracy of CW using data from Dahurian larch (Larix gmelinii Rupr.) in Northeastern China. Different sampling designs were applied, including selecting the thickest, thinnest, intermediate, and random trees, with 1 to 10 sample trees for each design. The results showed that all models achieved accurate CW predictions. MIXED displayed the most superior fitting statistics than GM and QR. In model validation, with the increase in the number of sample trees, the model prediction accuracy gradually improved and the model differences gradually reduced. MIXED produced the smallest RMSE, MAE, and MAPE across all sampling designs. The intermediate tree sampling design with the best validation statistics for the given sample size was selected as the final sampling design. Under intermediate tree sampling design, MIXED required a minimum of five sample trees, while GM and QR required at least five and six sample trees for calibration, respectively. Generally, we suggested selecting MIXED as the final CW prediction model and using the intermediate tree sampling design of five trees per plot. This study could provide ideas and support for forest managers to accurately and efficiently predict CW.