The use of tree crown variables in over-bark diameter and volume prediction models

Abstract

© iForest -Biogeosciences and Forestry IntroductionCrown characteristics are an important component of growth and yield models. Tree crown research contributes to several key forest ecosystem attributes: biodiversity, productivity, forest management, forest environment, and wildlife (Avery & Burkhart 2002). The crown of a tree has a strong influence on stem shape, as foliage provides carbohydrates for tree growth and development of the whole tree and their vertical distribution influences stem shape (Leites & Robinson 2004, Li & Weiskittel 2010. Crown ratio (CR) is considered as an expression of the tree's photosynthetic potential, and therefore commonly included as a key variable in growth and yield models.Tree stem shape has been commonly modeled using taper models (Muhairwe 1994). Taper models are used to estimate diameter along the bole at any given height, so tree volume can then be determined based on these diameters and corresponding heights. The auxiliary variables used to increase the accuracy of existing taper equations include: (1) crown dimensions; (2) stand and site variables; and (3) upper stem diameter measurements (Trincado & Burkhart 2006). Larson (1963) reported that within the crown, stem diameters at particular heights are generally smaller when compared with trees of the same dimensions but shorter crowns. As a result, tree boles cannot be completely described as a function of bole length and diameter. In attempts to describe tree taper, numerous models of varying complexity have been advanced. In most mathematical models, taper is modeled in terms of dbh (diameter at breast height) and total height. A few researchers have considered using crown variables (e.g., crown length -CL, CR, and crown height -CH) as covariates (Newnham 1992, Leites & Robinson 2004, Jiang et al. 2007) for describing tree profiles because of the relationship between crown and stem form development, but previous studies have shown mixed results on the benefit of adding crown variables in taper models. The main crown variable utilized in taper and volume models was CR (Petersson 1999, Jiang et al. 2007, Li & Weiskittel 2010, Jiang & Liu 2011. However, CL is an interesting variable, which may influence the prediction of diameter and volume in combination with CR (Mäkela 2002). For this reason, some forms of CR and CL functions were incorporated into the tree-stem taper and volume prediction models in this study.For stem taper and volume predictions using regression analysis, an appropriate nonlinear function must first be identified, which is a very difficult task. The main reason that artificial neural network (ANN) applications have received attention is that the methodology is comparable to statistical modeling and ANNs can be seen as a complementary effort (without the restrictive assumption of a particular statistical model) or as an alternative approach to fitting nonlinear models to data. Due to the fact that Neural Networks (NNs) attempt to find the best nonlinear function based on the network's complexity, witho...

Development of a stem taper equation and modelling the effect of stand density on taper for Chinese fir plantations in Southern China

“…Some variables related to forest management have long been recognized such as planting density, fertilization, thinning and age ( Gray, 1956 ; Bi & Turner, 1994 ; Palma, 1998 ; Sharma & Zhang, 2004 ). Some tree-level or stand-level indices (e.g., crown height, ratio, and site class) have been introduced into taper equations to improve modeling performance ( Burkhart & Walton, 1985 ; Valenti & Cao, 1986 ; Newnham, 1992 ; Muhairwe, Lemay & Kozak, 1994 ; Özçelik, Diamantopoulou & Brooks, 2014 ). In contrast, stand density is more easily obtained; in addition, Sharma & Zhang (2004) introduced stand density information into a previously developed variable taper equation for Black Spruce and found improved fit statistics and predictive accuracy.…”

Stem Taper Estimations With Artificial Neural Networks for Mixed Oriental Beech and Kazdaği Fir Stands in Karabük Region, Turkey

“…The ANN models developed in this study included three layers: input, hidden and output layers. The feed-forward backpropagation network structure was chosen, since this structure has been very popular in forestry literature due to its estimation success (e.g., Özçelik et al, 2014;Diamantopoulou et al, 2015). The Levenberg-Marquardt algorithm was used as learning algorithm in ANN models because of the same reason with selecting network structure.…”

“…Since the initial studies on stem taper estimations, the regression analysis approach has been commonly utilized (e.g., Kozak et al, 1969;Max and Burkhart, 1976;Clark et al, 1991;Kozak, 2004;Özçelik and Brooks, 2012;Corral Rivas et al, 2017). However, the knowledge on the use of ANN techniques for stem taper estimation is quite limited (e.g., Diamantopoulou, 2010;Özçelik et al, 2010;Leite et al, 2011;Özçelik et al, 2014;Nunes and Görgens, 2016), although this approach has been widely used in some other research areas of forestry (e.g., Diamantopoulou et al, 2009;Diamantopoulou and Millios, 2010;Cai et al, 2012;Diamantopoulou and Özçelik, 2012;Ashraf et al, 2013;Castro et al, 2013;Özçelik et al, 2013;Yurtseven et al, 2013;Ashraf et al, 2015;Diamantopoulou et al, 2015;Vahedi, 2016;Nazerian et al, 2018).…”

Total tree height predictions via parametric and artificial neural network modeling approaches

“…Most research has focused on mixed-models to understand h-d relationships (Calama & Montero 2004, Sharma & Parton 2007, Lhotka 2012, Ozçelik et al 2013, Gómez-García et al 2014, 2015, Mehtätalo et al 2015, Adamec & Drápela 2016, Bronisz & Mehtätalo 2020. In Turkey, h-d equations were developed for some tree species at regional scale (Diamantopoulou & Ozçelik 2012, Ozçelik et al 2014, Catal & Carus 2018.…”