Model construction of Larix principis-rupprechtii canopy volume and surface area based on BP neural network

Objective The BP neural network model was applied to predict the canopy volume and surface area of Larix principis-rupprechtii, and the optimal form of canopy volume and surface area estimation model of L. principis-rupprechtii was explored in order to provide new ideas for the future prediction model.

Method Taking L. principis-rupprechtii in Pangquangou Nature Reserve of Shanxi Province, northern China as the research object, the canopy volume and surface area of L. principis-rupprechtii were constructed using BP neural network based on 678 observational data obtained from six (60 m × 60 m) fixed plots.

Result Through model training, the canopy volume and surface area estimation model of L. principis-rupprechtii was obtained based on BP neural network. Based on BP neural network, the number of input layer nodes∶number of hidden layer nodes∶number of output layer nodes was 6∶9∶1. Canopy volume data R² = 0.948, MAE = 5.40 m³, RMSE = 18.40; surface area data R² = 0.957, MAE = 3.33 m², RMSE = 14.41. The performance of L. principis-rupprechtii canopy volume and surface area model based on BP neural network was positively correlated with the number of input factors. The optimal model had 6 input factors, i.e. crown width, tree height, DBH, max. crown height, projection length of the first live branch in the direction perpendicular to trunk, and crown base height.

Conclusion The input variables include information related to trunk size and crown configuration characteristics. The model can realize the prediction for the crown volume and surface area of Larix principis-rupprechtii trees effectively.