Forest biomass and estimate its value has a significant role in climate change. Because of land constraints and time-consuming methods to estimate biomass, using remote sensing is an effective alternative to terrestrial methods. In this study, in order to improve the accuracy of estimates of forest biomass to earlier research, optical image AVNIR-2 and PALSAR radar satellite ALOS images used with data from ground-based College of Agriculture, Tehran University of North region Kheiroudkenar. This stude procedure respectively 1 - features extraction from images, 2 - select features using genetic algorithms, 3 - Biomass estimated with features selected by regression analysis and neural networks. Evaluating the results of the application of neural networks and regression analysis on the features selected by genetic algorithms, neural networks represent the accuracy over 70 percent and regression analysis represent the accuracy to about 15 percent. For this reason, the use of neural networks in a way that has been used in this study for the northern forests and the complex structures is recommended.