Background: The aim of this study was to investigate near infrared (NIR) spectroscopy ability to estimate nanofibril concentration, physical and mechanical properties of Kraft paper reinforced with cellulose nanofibrils (CNF).
Results: principal component analysis (PCA) revealed no separation among specimens related to nanofibril content. Partial least squares regression (PLS-R) models for estimating nanofibril content, tensile index, stretch and resistance to air passage yielded R²cv ranging from 0.73 to 0.98. Partial least squares - discriminant analysis (PLS-DA) correctly classified up to 93% of the paper specimens both by grammage and nanofibril content using NIR spectra.
Conclusion: this approach appears to be suitable for predicting physical and mechanical properties of Kraft papers and can detect cellulose nanofibril content in the paper matrix.
Modeling diameter distributions of mixed-oak stands in northwestern Turkey
Background: Diameter distribution models, based on the Weibull function, were developed for even-aged 15 mixed-oak stands (Turkey oak, Sessile oak, and Hungarian oak) in northwestern Turkey. Two 16 modeling methods were considered. Weibull parameters were recovered from either equation 17 predicting Dq and Dvar (method of moments) or equations predicting Dq and D90 (hybrid 18 method). For each modeling method, three estimation methods were considered: (a) Least 19 Squares method, (b) CDF Regression method in which regression coefficients were estimated 20 separately for each species, and (c) CDF Regression method in which regression coefficients 21 were simultaneously estimated for all species.
Results: Results indicated that the hybrid method coupled 22 with the CDF Regression estimation method yield best results in this study. Similar results were 23 obtained when the regression coefficients were estimated either separately for each species or 24 simultaneously for all species.
Conclusion: The proposed models enable one to predict diameter distribution 25 of a given mixed-oak species stand in northwestern Turkey, using limited stand information. 26 These models are useful tools for the inventory and management of mixed-oak stands..
Assessment of alternative forest road routes and landslide susceptibility mapping using machine learningAssessment of alternative forest road routes and landslide susceptibility mapping using machine learning
Background: Forest roads are among the most basic infrastructure used for forestry activities and services. To facilitate the increased amount of wood harvesting adequately, the existing road network may require modifications to allow forest transportation within harvesting units that are not yet accessed by the roads. The construction of a forest road can trigger landslides, so the necessary constraints should be considered when the road is being planned to preclude such problems. Landslide Susceptibility Mapping (LSM) has become an integral part of the growing process of machine learning (ML), providing a more effective platform for practitioners, planners, and decision-makers. This study aims to reveal the most suitable alternative routes for a forest road, especially in areas susceptible to landslides, and to provide an effective tool for decision-makers. For this purpose, two models were developed through ML: Logistic Regression (LR) and Random Forest (RF). Elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from the fault, the road, and the stream, and lithology were considered as the main landslide susceptibility factors in these models.
Results: The best model was obtained by the RF approach with an Area Under ROC Curve (AUC) value of 81.9%, while the LR model was 78.2%. LSM data was used as a base, and alternative routes were obtained through CostPath analysis.
Conclusion: It has been shown that the ML methods used in this study can positively contribute to decision-making by providing more effective LSM calculations in studies to determine alternative routes in a forest road network.