The optical properties of lichens have been traditionally explored in the context of geological mapping where the encrustation of lichens on rocks may influence the detection of minerals of interest. As of today, few studies have looked into the potential of using the optical properties of lichens to classify them; however, none has investigated the classification of tropical lichens using spectroscopy. Here we explore the use of the visible-near infrared reflectance (VNIR; 450–1000 nm) to discriminate Neotropical corticolous lichens; the most abundant lichens in tropical forests. Reflectance measurements on lichens and their bark substrate were performed on 282 lichens samples of 32 species attached to their host's bark. Using these measurements, we first explored the degree of spectral mixing of bark and lichens by linear unmixing each lichen spectrum with the corresponding average species spectrum and bark spectrum. Overall, the results reveal that the lichen signatures tend to mask the spectral contributions from bark; however, there are some specific groups of species with high bark mixing probably due to their nature and the similarities between the lichen and bark spectra. Next, we classified the lichen spectra based on growth forms and taxonomic ranks (i.e., family, genus, species) using five machine learning classifiers. This analysis was conducted on raw reflectance spectra and wavelet-transformed spectra to enhance the absorption features prior to classification. As expected, the classification of lichen spectra is less accurate at species-specific levels, rather than higher taxonomic ranks. The wavelet transformation was found to enhance the general performance of classification; however, the accuracy of the classification depends on the classifier. Of the classifiers used in this study, linear discrimination applied to reflectance spectra presents the highest performance at the species level. Our results reveal the potential of using the VNIR reflectance as a method to discriminate Neotropical lichens. The introduced methodology may be conducted in the field, thus allowing the monitoring of lichen communities in forests; thereby furthering the current understanding of the role of lichens in ecosystem functioning.

地衣的光学特性传统上是在地质制图的背景下进行的，在这种情况下，地衣在岩石上的结壳可能会影响目标矿物的检测。到目前为止，很少有研究探讨使用地衣的光学特性对它们进行分类的潜力。然而，没有人使用光谱法研究热带地衣的分类。在这里，我们探索使用可见-近红外反射率（VNIR; 450–1000 nm）来区分新热带皮质类地衣。热带森林中最丰富的地衣。对附着在寄主树皮上的32个物种的282个地衣样品进行了地衣及其树皮基质的反射率测量。使用这些测量，我们首先通过线性分解每个地衣谱与相应的平均物种谱和树皮谱来探讨树皮和地衣的光谱混合程度。总的来说，结果表明，地衣的特征倾向于掩盖树皮的光谱贡献。然而，由于它们的性质以及地衣和树皮光谱之间的相似性，某些特定种类的树皮混合度很高。接下来，我们使用五个机器学习分类器，根据生长形式和分类等级（即科，属，种）对地衣光谱进行分类。该分析是在原始反射光谱和小波变换光谱上进行的，以增强分类前的吸收特性。不出所料，在物种特定水平上，地衣光谱的分类不太准确，而不是更高的生物分类等级。发现小波变换增强了分类的总体性能；但是，分类的准确性取决于分类器。在这项研究中使用的分类器中，应用于反射光谱的线性判别在物种水平上表现出最高的性能。我们的结果揭示了使用VNIR反射率作为区分新热带地衣的方法的潜力。引入的方法可以在野外进行，从而可以监测森林中的地衣群落；从而进一步了解了地衣在生态系统功能中的作用。在这项研究中使用的分类器中，应用于反射光谱的线性判别在物种水平上表现出最高的性能。我们的结果揭示了使用VNIR反射率作为区分新热带地衣的方法的潜力。引入的方法可以在野外进行，从而可以监测森林中的地衣群落；从而进一步了解了地衣在生态系统功能中的作用。在这项研究中使用的分类器中，应用于反射光谱的线性判别在物种水平上表现出最高的性能。我们的结果揭示了使用VNIR反射率作为区分新热带地衣的方法的潜力。引入的方法可以在野外进行，从而可以监测森林中的地衣群落；从而进一步了解了地衣在生态系统功能中的作用。