In this study, airborne hyperspectral imagery and LiDAR data were combined to establish spectral and 3-dimensional structural characteristics of land cover and forest types in the Liukuei Experimental Forest (LEF). Statistical and machine learning algorithms were used to develop automated classification models. In total, 19 variables were prepared as model candidate variables, which were divided into three major categories, including representative hyperspectral bands, vegetation indices calculated using hyperspectral data, and canopy structural indices derived from LiDAR data. Redundant variables were excluded by a correlation analysis, and the models were determined using 8 variables. Assessment of the importance of the variables showed that canopy height was an important structural feature for interpreting the land cover/forest types. Although more structural indices were included among the predictor variables selected by the specific tree-species classification model, they were less important than the vegetative indices. For the land cover/forest type classification, the difference between the overall accuracy of support vector machine (SVM) and random forest (RF) model was 0.24%. Both models yielded an overall accuracy of 75% with similar levels of confusion between classification categories. For specific tree-species classification, the overall accuracy of RF was the highest (86.79%), followed by SVM (85%). The maximum likelihood classification (MLC) had relatively poor performance in both land cover/forest type classification and specific tree-species classification. These non-parametric machine learning models, which do not rely on data following particular statistical distribution, are more suitable for classification purposes when using data from different sensors or auxiliary variables. Their classification accuracy was more robust than traditional classification techniques such as MLC, especially when the feature space is complex. Overall, machine learning algorithms that integrate hyperspectral information and LiDAR-derived structural variables can effectively distinguish more-detailed forest cover types.