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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. Las propiedades ópticas de los líquenes se han explorado tradicionalmente en el contexto del mapeo geológico donde la incrustación de líquenes en las rocas puede influir en la detección de minerales de interés. Hasta la fecha, pocos estudios han examinado el potencial de utilizar las propiedades ópticas de los líquenes para clasificarlos; sin embargo, ninguno ha investigado la clasificación de los líquenes tropicales mediante espectroscopia. Aquí exploramos el uso de la reflectancia del infrarrojo cercano visible (VNIR; 450–1000 nm) para discriminar líquenes cortícolas neotropicales; los líquenes más abundantes en los bosques tropicales. Las mediciones de reflectancia en líquenes y su sustrato de corteza se realizaron en 282 muestras de líquenes de 32 especies adheridas a la corteza de su huésped. Usando estas medidas, primero exploramos el grado de mezcla espectral de la corteza y los líquenes mediante la desmezcla lineal de cada espectro de líquenes con el espectro de especies promedio correspondiente y el espectro de corteza. En general, los resultados revelan que las firmas de los líquenes tienden a enmascarar las contribuciones espectrales de la corteza; sin embargo, existen algunos grupos específicos de especies con una alta mezcla de corteza probablemente debido a su naturaleza y las similitudes entre los espectros de líquenes y corteza. A continuación, clasificamos los espectros de líquenes según las formas de crecimiento y los rangos taxonómicos (es decir, familia, género, especie) utilizando cinco clasificadores de aprendizaje automático. Este análisis se realizó en espectros de reflectancia sin procesar y espectros transformados por ondículas para mejorar las características de absorción antes de la clasificación. Como era de esperar, la clasificación de los espectros de líquenes es menos precisa a niveles específicos de especies, en lugar de rangos taxonómicos más altos. Se descubrió que la transformación de ondículas mejora el rendimiento general de la clasificación; sin embargo, la precisión de la clasificación depende del clasificador. De los clasificadores utilizados en este estudio, la discriminación lineal aplicada a los espectros de reflectancia presenta el rendimiento más alto a nivel de especie. Nuestros resultados revelan el potencial de utilizar la reflectancia VNIR como método para discriminar líquenes neotropicales. La metodología introducida podrá realizarse en el campo, permitiendo así el seguimiento de las comunidades de líquenes en los bosques; fomentando así la comprensión actual del papel de los líquenes en el funcionamiento de los ecosistemas. Universidad Nacional, Costa Rica University of Minnesota Twin Cities, USA University of Alberta, Canada Escuela de Ciencias Biológicas

Monitoring of environmental conditions in and around oil sands developments has been underway for years. However, recent reviews have indicated the need for a more comprehensive, scientifically-rigourous and transparent monitoring program and have provided recommendations for design and oversight. This report presents a comprehensive review of industrial applications of an emerging environmental monitoring technology called Wireless Sensor Networks (WSN). This technology consists of a series of individual wireless nodes that have the capacity to measure different micro-climatic as well as other chemical variables at costs that are significantly cheaper that current wired systems. This review describes monitoring in four main sectors: agricultural, environmental, forest, and industrial. The report reviews publications over the last 13-years; none of the case studies are from Alberta. The report also provides a description of the establishment of a new Alberta project in which a WSN is used to monitor environmental conditions at a coal mine reclamation site. The WSN is installed at Coal Valley Mine (CVM, Central Alberta), and it represents a collaborative project between the Centre for Earth Observation Sciences (CEOS) at the University of Alberta, Alberta Environment and Sustainable Resource Development, and CVM. The system, logistical needs, and the data management system used to obtain, visualize and analyze the environmental data currently collected at CVM are described. Given current environmental monitoring needs, plus the large areal extent of the oil sands region, wireless sensor networks have the potential to support traditional monitoring networks. The federal/provincial oil sands environmental monitoring implementation plan specifically mentions the use of remote sensing tools to enhance the monitoring system. More work is required to develop additional sensors specific to chemicals of concern in the oil sands and implement this technology in regional monitoring.