Sediment budget models use the presence of riparian vegetation as a main factor contributing to the decrease of sediment production across river systems. Some models focusing at site scale rely on parameters of root tensile strength and root architecture to quantify the reinforcement provided by the presence of roots in riverbanks. The tensile strength to rupture in relation to diameter provides information of resistance strength of individual roots, whereas root architecture describes the abundance, root diameter and spatial distribution of roots across the bank face. Being focused on single species at site scale and developed for setting of low diversity of tree species in temperate climes, this model structure does not provide the necessary support for a catchment scale application in a tropical environment. In this work, we propose a stochastic approach to upscale root architecture data, collected during extensive fieldwork, to catchment scale using vegetation information derived from LIDAR imagery (canopy high and projected foliage cover) with 1m of spatial resolution. We focused our data collection and analysis at species and assemblage level in order to better characterize forest structures and at the same time target key species dominant in the reach. The Spearman index, a non-parametric correlation value is calculated between root architecture from field sites and LIDAR imagery data, and then a probability density function is fitted to the field data. Several analytical functions were tested through Kolmogorov-Smirnov test and ranked according to their respective p-values. The stochastic Monte Carlo simulation is executed, constrained by the Spearman index, and based on the parameters of the chosen analytical functions found for each site and each distinct riparian forest structures. This method provides a way to upscale root characteristics an essential variable for sediment budget models, and the stochastic nature of the process allows the quantification of model uncertainties. Finally, this framework is capable of characterizing a vegetation community with high species diversity, a typical setting of Queensland rivers.