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Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of R-2 = 0.70 and 0.25 for 2009-2011 and 2011-2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (R-2 = 0.88 and RMSE = 54.54 kg m(-2)) compared to two texture (R-2 = 0.85 and RMSE = 60.65 kg m(-2)) and single texture band combinations (R-2 = 0.64 and RMSE = 94.13 kg m(-2)). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB.