The application of artificial neural network and multiple linear regression in modeling the volume of residual stand using environmental data and remote sensing

In order to manage the forests and optimal and sustainable utilization of the forest, it seems necessary to know the information on the volume of the residual stand. In this study, a systematic randomized inventory was carried out in 186 circular 10-acre plots in the educational and research forest of Darabkola, Sari, Golestan, Iran and the volume of each plot was obtained. In the next step, the physiographic layers of the area were prepared using the topographic map and the vegetation characteristics were prepared using the LISS-III image of the IRS-P6 satellite with a resolution of 23.5. After preparing physiographic layers and vegetation characteristics, their value was calculated for all plots. Then, regarding these variables and using two methods of multilayer perceptron neural network and multiple regression model, modeling was done. The results showed that the multiple linear model could model the volume changes in the region with higher accuracy (R2=0.75 and RMSE=0.3). The results of this research can be used in management planning and as one of the effective factors in the design of logging routes and forest roads so that areas with larger volumes are covered more.