Secondary succession is a process that is often observed taking place in former agricultural ecosystems. Its characteristics are especially important in protected areas, for the purposes of monitoring and protective measures. Effective mapping of succession is facilitated by the development of automated methodologies based on remote sensing data, which are capable of complementing traditional field research. The objective of this work is to determine whether the classification of high-resolution hyperspectral and light detection and ranging (LiDAR) data with the use of the random forest algorithm enables us to produce an accurate succession species map. First, feature extraction techniques are applied to 1-m hyperspectral images and a ∼7  point  /  m² dense point cloud. Minimum noise fraction layers and vegetation indices are calculated from the hyperspectral data and geometry related indices from the LiDAR data. Finally, the recursive feature elimination algorithm is applied to the combined dataset and the reference polygons to select the optimal set of features for subsequent classification. The results indicate that the proposed methodology has the potential to be used operationally. The final classification product is characterized by a relatively high Cohen’s kappa value of 0.68, with single species classified with various accuracies, expressed by F1 scores ranging from 0.45 to 0.87.