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This review article examines past and current research on the application of near-infrared (NIR) reflectance/transmittance spectroscopy (NIRS) for real-time monitoring of moisture content and density of solid wood. Most of the applications of NIRS on solid wood have focussed on the application of multivariate statistics as exploratory tools for the prediction of physical, chemical and mechanical properties, such as moisture content, density, stiffness, cellulose and lignin content. However, very few studies on the development of optical models and the use of NIRS transmittance techniques on solid wood have been reported. NIRS technology has the potential to be used as a rapid tool that could be employed for at-line measurement and monitoring of wood properties in the forest products industry.Keywords: wood properties, near-infrared spectroscopy, multivariate statistics, optical model, transmittance, reflectance résumé Cet article de revue examine la recherche passée et actuelle sur l'application de la spectroscopie proche infra-rouge en mode réflectance/transmittance pour le suivi en temps réel de la teneur en eau et de la densité du bois solide. La plupart des applications de la spectroscopie proche infra-rouge sur le bois solide ont porté sur l'application des méthodes statistiques multi-variées pour l' estimation des propriétés physiques, chimiques et mécaniques, telles que la teneur en eau, la densité, la dureté, le contenu en cellulose et en lignine. Cependant, peu d' études ont porté sur le développement de modèles optiques et sur l'utilisation de la spectroscopie proche infra-rouge en mode transmittance pour le bois solide. La spectroscopie proche infra-rouge a le potentiel d' être utilisée comme un outil rapide de mesure et suivi en temps réel des propriétés du bois dans l'industrie des produits forestiers.

Mapping wetlands with high spatial and thematic accuracy is crucial for the management and monitoring of these important ecosystems. Wetland maps in New Brunswick (NB) have traditionally been produced by the visual interpretation of aerial photographs. In this study, we used an alternative method to produce a wetland map for southern New Brunswick, Canada, by classifying a combination of Landsat 8 OLI, ALOS-1 PALSAR, Sentinel-1, and LiDAR-derived topographic metrics with the Random Forests (RF) classifier. The images were acquired in three seasons (spring, summer, and fall) with different water levels and during leaf-off/on periods. The resulting map has eleven wetland classes (open bog, shrub bog, treed bog, open fen, shrub fen, freshwater marsh, coastal marsh, shrub marsh, shrub wetland, forested wetland, and aquatic bed) plus various non-wetland classes. We achieved an overall accuracy classification of 97.67%. We compared 951 in-situ validation sites to the classified image and both the 2106 and 2019 reference maps available through Service New Brunswick. Both reference maps were produced by photo-interpretation of RGB-NIR digital aerial photographs, but the 2019 NB reference also included information from LiDAR-derived surface and ecological metrics. Of these 951 sites, 94.95% were correctly identified on the classified image, while only 63.30% and 80.02% of these sites were correctly identified on the 2016 and 2019 NB reference maps, respectively. If only the 489 wetland validation sites were considered, 96.93% of the sites were correctly identified as a wetland on the classified image, while only 58.69% and 62.17% of the sites were correctly identified as a wetland on the 2016 and 2019 NB reference maps, respectively.

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