Biomonitoring plays a crucial role in the assessment of air quality, as it allows to estimate the presence of pollutants, by measuring deviations from normality of the components of an ecosystem. Lichens are among the organisms most commonly used as bioindicators. The present study deals with the classification of lichen taxa from images, by means of a machine learning process based on patch classification. A given image is divided in non-overlapping patches, and each of them undergoes feature extraction and classification, eventually being associated to a category. Three different methods for extracting patch descriptors are investigated: (i) handcrafted descriptors based on classical feature extractor algorithms, (ii) convolutional neural networks employed as feature extractors, and (iii) scattering networks, which combine wavelet convolutions and nonlinear operators. For each of these methods, the descriptors are used as inputs for a classification algorithm. The whole process is evaluated in terms of classification accuracy, empirically determining the most appropriate parameters for the different models implemented. By using the dataset of lichens of this study, best results (~ 0.89 accuracy) are obtained with a specific hand-crafted descriptor (dense SIFT), thus providing insights on the kind of representation which is most suitable for the task. Keywords: Computer vision; Machine learning; Neural network; Species recognition; Lichen; In-field classification.