In recent decades remote sensing-based forest stands biomass estimation techniques have a great importance. In this study, the Above-Ground Biomass (AGB) of Iranian northern beech forests was estimated by TM sensor of Landsat 5 satellite. Required Pre-processing and processing tasks was carried out on the images. For estimation of above-ground biomass, 65 sample plots with dimensions of 45m × 45m were laid out in the field. In each sample plot, diameter at the breast height (DBH) of trees higher than 7.5cm was measured and consequently, the above-ground biomass was calculated for each sample plot. 45 and 20 sample plots were considered for modelling and validation processes, respectively. Parametric multivariate linear regression was used for modelling. Pearson correlation between above-ground biomass in sample plots and correspond spectral values in calculated and original bands showed that the near infra-red band (band 4) was most correlated with above-ground biomass at 99% confidence level and correlation coefficient of 0.427. Implementing of stepwise multivariate linear regression method between above-ground biomass and all other remotely sensed variables revealed that the AGB= 6/682b4 – 206/693 model with adjusted R square=0.164 and RMSE=15/4 % is the best model (similar to simple linear regression between AGB and NIR band) for estimation of Iranian northern beech forests above-ground biomass in studied area. Conclusively, this approach is able to estimate woody biomass in pure beech stands relatively good especially in small scales.