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The increasing concentration of greenhouse gases in the atmosphere has been identified as contributing to the increase in global mean temperature. Carbon sequestration into trees and forests is an effective and inexpensive method for decreasing the CO2 level in the atmosphere. Hence, accurate measurements of biomass levels will be important to the global carbon cycle and climate change. This study used a wavelet-based forest aboveground biomass (AGB) estimation approach in a temperate deciduous forest. Two-dimensional discrete wavelet transformations was applied to ALOS AVNIR and PALSAR to obtain wavelet coefficients, which were correlated with AGB estimates using multiple linear regression analysis. Different wavelets were tested using this approach. Moreover, vegetation indices and texture parameters were calculated and correlated with AGB estimates. The results indicated that wavelet-based modeling could improve the accuracy of biomass estimation to 75% or even higher in comparison with the accuracy of 30%-40% resulting from past studies using vegetation indices and texture measures. This study demonstrates that wavelet-based biomass estimation could be a promising approach for solving the uncertainty between reflectance or backscatter values from satellite images and forest biomass and therefore provide better biomass estimations.