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The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth's species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data. [ABSTRACT FROM AUTHOR]