Calculation of live tree timber volume based on particle swarm optimization and support vector regression

Abstract: Establishment of each tree species volume table is an important research subject in forest management. Accurate tree tables were used to determine the forest reserves. Moreover, these tree tables were applied to provide precise forest management decision making references for the forestry center and local forestry authorities. However, because of the difference of increment between different tree species, live tree tables must be revised every 10 years in China. Previously, to establish tree tables, sample trees were selected in the local area according the corresponding rules, and these sample trees were cut down and divided into several sections, and each section's volume were summed up as the total tree volume. Based the analytic data, the unary models between diameter at breast and volume were established, and also, to set diameter at breast and tree height as independent variables, tree volume as dependent variable, the binary models could be established, as well as a ternary model that describes the relationship between volume and 3 independent variables including diameter at breast, tree height, and tree step form. Nevertheless, these models mentioned above are sample linear models or nonlinear models. To estimate the forest stocks in the forest survey, former researchers usually cut down target trees and extracted samples based on the principle of sampling, and then made a corresponding volume table. This felled, destructive, and time-consuming method damaged many growth dominant trees. Tree volume modeling is the key step of volume table establishment, and volume usually was predicted by the volume equation that was derived from experience. However, because of the uncertainty of tree growth, it is difficult to effectively predict the complexity and diversity of the volume model through conventional volume equations. For this reason, the volume prediction accuracy rate is unsatisfactory. In order to promote the volume prediction accuracy rate, the algorithm of particle swarm optimization (PSO) was introduced into the standing tree volume prediction model. Moreover, the parameters were optimized by the support vector regression (SVM). The data of diameters at breast height and tree heights of standing trees were input into SVM, which were used to learn, parameters of SVM were used as the particle of PSO, standing trees volume value that were measured by authors were considered as objective function of PSO, then prediction values of standing trees volume were detected by the optimized parameters which were obtained through mutual co-ordination of particle, and the prediction values of standing trees' volume were verified by the measured value. This research first applied electronic theodolite and artificial measurement to get the stumpage diameter and breast diameter of 400 samples; and then considered stumpage diameter and breast diameter as input data, volumes as output data, trained with 300 samples by PSO-SVM; and finally, compared the results by PSO-SVM, Spurr volume model, and BP neural network by predicting 100 samples. The results showed that PSO-SVM, which showed the highest correlation coefficient between the predicted and measured values (0.91) and lowest average error rate (0.58%), was better than the others.