Modelling and Simulation of Tree Biomass

A primary objective of sustainable bioenergy production is to quantify the available resource supply because all further planning of the value chain hinges on the available biomass that can be converted. Since biomass is costly to transport, the spatial quantification of the resource is also important. Thus, modern approaches to biomass supply chain management must embrace the resource quantity and location as a key element of the supply chain. Data on resource availability are usually obtained from different sources such as remote sensing and terrestrial inventories, as discussed in Chap. 2, which provide information on the spatial distribution of forests and trees and their dimensions but are, as such, not capable of estimating biomass directly with the necessary accuracy. Thus the main purpose of the application of modelling and simulation techniques in this context is the estimation of the biomass resource from broadly available tree and stand variables. This auxiliary information could be sourced from inventories and remote sensing or could be provided by model projections from growth models to estimate the biomass availability.

[1]  J. Hytönen,et al.  Comparison of methods for estimating willow biomass , 1987 .

[2]  Sb Dovey,et al.  Estimating biomass and macronutrient content of some commercially important plantation species in South Africa , 2009 .

[3]  M. C. Jones,et al.  The Statistical Analysis of Compositional Data , 1986 .

[4]  O. W. Eshbach,et al.  Handbook of Engineering Fundamentals , 1937, Nature.

[5]  Raimon Tolosana-Delgado,et al.  "compositions": A unified R package to analyze compositional data , 2008, Comput. Geosci..

[6]  A. F. Militino,et al.  Modelling aboveground tree biomass while achieving the additivity property , 2011, Environmental and Ecological Statistics.

[7]  Laurent Saint-André,et al.  Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo , 2005 .

[8]  V. Labau,et al.  State-of-the-art methodology of forest inventory: a symposium proceedings. , 1990 .

[9]  Sampling oak foliage by the randomized-branch method , 1977 .

[10]  R. Barney,et al.  Biomass distribution and crown characteristics in two Alaskan Piceamariana ecosystems , 1978 .

[11]  E. Kitenge.  Harvesting of invasive woody vegetation (Eucalyptus lehmanii, Leptospermum laevigatum, Acacia cyclops) as energy feedstock in the Cape Agulhas Plain of South Africa , 2011 .

[12]  H. H. Ku,et al.  Notes on the use of propagation of error formulas , 1966 .

[13]  R. Reyment.  Compositional data analysis , 1989 .

[14]  Bernard R. Parresol,et al.  Additivity of nonlinear biomass equations , 2001 .

[15]  B. Parresol.  Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons , 1999, Forest Science.

[16]  R. J. Jessen,et al.  Determining the Fruit Count on a Tree by Randomized Branch Sampling , 1955 .

[17]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[18]  C. L. Todoroki,et al.  Linear mixed-effects models for estimating biomass and fuel loads in shrublands , 2010 .

[19]  R. Briggs,et al.  Forcing additivity of biomass tables: some empirical results , 1984 .

[20]  C. E. Thomas,et al.  A Simultaneous Density-Integral System for Estimating Stem Profile and Biomass: Slash Pine and Willow Oak , 1996 .

[21]  Jari Kokkola.  Drying of pulpwood in northern Finland. , 1993 .

[22]  H. Wiant,et al.  Estimation of plant biomass based on the allometric equation , 1981 .

[23]  B. D. Toit,et al.  Effects of site management on growth, biomass partitioning and light use efficiency in a young stand of Eucalyptus grandis in South Africa , 2008 .

[24]  Harry T. Valentine,et al.  Subsampling trees for biomass, volume or mineral content , 1984 .

[25]  Bernard R. Parresol,et al.  Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume. , 2006 .

[26]  F. J. Pierce,et al.  Contemporary Statistical Models for the Plant and Soil Sciences , 2001 .

[27]  Richard Condit,et al.  Error propagation and scaling for tropical forest biomass estimates. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[28]  T. Seifert,et al.  Effects of irregular stand structure on tree growth, crown extension and branchiness of plantation-grown Pinus patula , 2013 .

[29]  Ariel E. Lugo,et al.  Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data , 1989, Forest Science.

[30]  Lutz Fehrmann,et al.  Comparison of linear and mixed-effect regression models and a k-nearest neighbour approach for estimation of single-tree biomass , 2008 .

[31]  J. Hellmann,et al.  BIAS, PRECISION, AND ACCURACY OF FOUR MEASURES OF SPECIES RICHNESS , 1999 .

[32]  A. Kozak.  Methods for Ensuring Additivity of Biomass Components by Regression Analysis , 1970 .

[33]  G. Baskerville.  Use of Logarithmic Regression in the Estimation of Plant Biomass , 1972 .

[34]  A. Gier,et al.  A new approach to woody biomass assessment in woodlands and shrublands. , 2003 .

[35]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[36]  T. Nordfjell,et al.  Fuel quality changes during seasonal storage of compacted logging residues and young trees , 2007 .

[37]  W. G. Forrest,et al.  Variations in the accumulation, distribution and movement of mineral nutrients in radiata pine plantations , 1969 .

[38]  Vinay Kumar Dadhwal,et al.  Growing stock-based forest biomass estimate for India , 2002 .

[39]  Christian Wirth,et al.  Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation. , 2004, Tree physiology.

[40]  Theo Verwust.  Logarithmic transformations in biomass estimation procedures: violation of the linearity assumption in regression analysis , 1991 .

[41]  Timothy G. Gregoire,et al.  Sampling Methods to Estimate Foliage and Other Characteristics of Individual Trees , 1995 .

[42]  T. Seifert,et al.  Impacts of fructification on biomass production and correlated genetic effects in Norway spruce (Picea abies [L.] Karst.) , 2009, European Journal of Forest Research.

[43]  R. Briggs,et al.  Forcing additivity of biomass tables: use of the generalized least squares method , 1985 .

[44]  B. Parresol,et al.  Application of the Algebraic Difference Approach for Developing Self-Referencing Specific Gravity and Biomass Equations , 2006 .

[45]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .