Investigation of the compatibility taper equations of Schima superba based on nonlinear measurement errors

Forest resource environment monitoring aims to observe, analyze, and evaluate both the number and quality of forest resources, which has aroused great attention by scholars from all over the world. Investigating stand growth condition is of great significance to the number estimation and management of forest resources. In order to more accurately estimate the increment and stand volume of Schima superba Gardn. et Champ., this study focused on forest survey data of Schima superba artificial forest and determined the optimal basic models of the DBH growth, the tree height curve, and the taper equation using the TOPSIS method with entropy weights, during which the coefficient of determination ( R 2 ), the model prediction precision ( P (%)), the model accuracy ( V ), the absolute bias (AB), the root-mean-square error (RMSE), and the sum of squares of errors (SSE) were selected as the evaluation indexes. Then, the equations were combined using nonlinear measurement error method for parameter solving. Results show that the new model shows higher precision with remarkably enhanced evaluation indexes. Among the three optimal basic models, the V, AB, the RMSE, and the SSE decreased significantly. According to the paired t test results, the predicted values using the new model with measurement error model exhibit no significant difference from the measured data, suggesting good adaptivity. Moreover, the present results can more accurately calculate the diameter and stand volume of any part of Schima superba , provide the basis for calculating both biomass and carbon sequestration, and offer technical supports for formulating the merchantable volume table. Therefore, owing to the consideration of model parameter inaccuracy caused by the measurement errors in forest survey, the established equation performs well in compatibility and consistency. The present study can provide scientific theoretical basis and decision schemes for the forest environment monitoring and evaluation, forest management, etc.