Prediction of tree crown width in natural mixed forests using deep learning algorithm

Abstract

Crown width (CW) is one of the most important tree metrics, but obtaining CW data is laborious and time-consuming, particularly in natural forests. The Deep Learning (DL) algorithm has been proposed as an alternative to traditional regression, but its performance in predicting CW in natural mixed forests is unclear. The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in north-eastern China, to analyse the contribution of tree size, tree species, site quality, stand structure, and competition to tree CW prediction, and to compare DL models with nonlinear mixed effects (NLME) models for their reliability. An amount of total 10,086 individual trees in 192 subplots were employed in this study. The results indicated that all deep neural network (DNN) models were free of overfitting and statistically stable within 10-fold cross-validation, and the best DNN model could explain 69% of the CW variation with no significant heteroskedasticity. In addition to diameter at breast height, stand structure, tree species, and competition showed significant effects on CW. The NLME model (R2 ​= ​0.63) outperformed the DNN model (R2 ​= ​0.54) in predicting CW when the six input variables were consistent, but the results were the opposite when the DNN model (R2 ​= ​0.69) included all 22 input variables. These results demonstrated the great potential of DL in tree CW prediction.