Lateral rollover is the leading cause of fatal accidents in light All-Terrain Vehicles (e.g. quad bikes), especially in the agricultural area. The estimation and prediction of hazardous situations are preliminary steps in the design of active security devices. If numerous metrics have already been defined for on-road vehicles, few approaches are suitable for fast motions in a natural environment (mainly due to tire/ground contact specificity and variability). This paper proposes an algorithm dedicated to the estimation and prediction of one metric, namely Lateral Load Transfer (LLT), in order to anticipate rollover situations on an irregular and natural ground. It is based on a vehicle dynamic model, used jointly with a backstepping observer. It allows to take into account tire/ground contact non linearities and variability, which impact the rollover tendency. The efficiency of the metric is investigated through advanced simulations and full scale experiments on a Kymco quad bike.