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Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.