Near infrared spectroscopy for estimating properties of kraft paper 2 reinforced with cellulose nanofibrils

 

Abstract

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

 

Abstract

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

 

Abstract

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.