Inferring vegetation growth from airborne LIDAR data has been a major topic of research. Several algorithms have been developed and tailored for specific applications such as forestry inventory and inspection of electrical lines. In this work we focus on estimation of growth rates for trees near electrical lines in order to access the risk of line failure due to excessive approximation of vegetation, or even a possible tree fall. More concretely, given airborne LIDAR data from two inspections, typically one year apart, two surface models are generated for each identified tree and subsequentially used as input to growth estimation algorithms. Current implementations resort to sampling, hence not making use of the full information carried by such surface models. In this work each surface model is encoded by a real function. The average growth is then computed through exact integration of the difference of both encoding functions. Simulations show that this approach yields better results than current ones.