Streamside vegetation plays a multifunctional role in many interconnected hydraulic, ecological and sedimentological processes of great interest for flood risk assessment and river restoration practices, which need frequent riparian vegetation maintenance interventions. The reuse of riparian vegetation as a potential energy source represents a sustainable way to support the costs of buffer management. As a consequence, reliable and cost-efficient modelling of woody biomass is crucial to quantify the theoretical energy budget of riparian corridors and to drive decision-making processes. Potential capabilities of multi-spectral satellite data in retrieving riparian woody biomass (B) are explored in this paper. The method is organized in five sequential steps: 1) riparian vegetation mapping; 2) Principal Component Analysis of the vegetation spectral signatures; 3) estimation of the correlation structure between arboreal spectral signatures and ground-observed biomechanical properties tree high (h) and stem diameter (D); 4) identification/calibration/validation of spectral-based predictive models of h and D and 5) use of a standard allometric relationship to calculate the riparian woody biomass. The methodology is tested over a 3 km reach of the forested floodplain of the Avisio river (Trentino Alto Adige, Italy) by using an extended field surveys and a synchronous SPOT-5 multi-spectral image acquired on 28/08/2004. Results showed strong correlation coefficients between spectral signatures and vegetation parameters (r(k)(h) = 0.783 and r(k)(D) = 0.695) and valuable satellite capabilities in retrieving biomechanical parameters through tri-parametric power lows (R-2(h) = 0.732 and R-2(D) = 0.619). The remotely-derived woody biomass map shows comparable estimates than those obtainable thought ground measurements (RMSE(B) = 0.019 m(3)) and represents a repeatable and accurate device to assess the potential energy budget within riparian corridors. A simple simulated scenario of the riparian management is also provided to assess the biomass-derived net calorific value and the corresponding cost-benefit analysis. (C) 2011 Elsevier Ltd. All rights reserved.