The increasing concentration of greenhouse gases has been identified as a main cause of increase of global mean temperatures since the mid-20th century. The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and inexpensive way for mitigating the CO2 level in the atmosphere. Hence, accurate measurement of biomass will be of great importance to global carbon cycle and climate change. This study performed a wavelet-based forest aboveground biomass estimation approach in a temperate deciduous forest, Kheyroud Kenar forest in north part of Iran. Wavelet analysis, specifically two-dimensional discrete wavelet transform (DWT) was applied to ALOS PALSAR images to obtain wavelet coefficients (WCs), which were correlated with forest inventory data using multiple linear regression analysis to investigate the relationship. The results indicate that Db wavelet coefficients correlate better with field biomass data than other parameters. For the first level of the decomposition, the correlation coefficient is 0.5 while for second level, the overall R value increased up to 0.75. This study demonstrates that wavelet-based biomass estimation could be a very promising approach for providing better biomass estimation; however, further research is needed for identifying robust wavelet coefficients and optimizing procedures. Keywords: ALOS PALSAR, Wavelet analysis, Forest biomass, Multiple regression analysis.
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