*

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

Ramazan Özçelik (1), Maria J Diamantopoulou (2)   , John R Brooks (3)

iForest - Biogeosciences and Forestry, Volume 7, Issue 3, Pages 132-139 (2014)
doi: https://doi.org/10.3832/ifor0878-007
Published: Jan 13, 2014 - Copyright © 2014 SISEF

Research Articles


Linear and nonlinear crown variable functions for 173 Brutian pine (Pinus brutia Ten.) trees were incorporated into a well-known compatible volume and taper equation to evaluate their effect in model prediction accuracy. In addition, the same crown variables were also incorporated into three neural network (NN) types (Back-Propagation, Levenberg-Marquardt and Generalized Regression Neural Networks) to investigate their applicability in over-bark diameter and stem volume predictions. The inclusion of crown ratio and crown ratio with crown length variables resulted in a significant reduction of model sum of squared error, for all models. The incorporation of the crown variables to these models significantly improved model performance. According to results, non-linear regression models were less accurate than the three types of neural network models tested for both over-bark diameter and stem volume predictions in terms of standard error of the estimate and fit index. Specifically, the generated Levenberg-Marquardt Neural Network models outperformed the other models in terms of prediction accuracy. Therefore, this type of neural network model is worth consideration in over-bark diameter and volume prediction modeling, which are some of the most challenging tasks in forest resources management.

  Keywords


Crown Variables, Taper, Back-Propagation ANNs, Levenberg-Marquardt ANNs, Generalized Regression Neural Networks

Authors’ address

(1)
Ramazan Özçelik
Faculty of Forestry, Süleyman Demirel University, East Campus, TR-32260, Isparta (Turkey)
(2)
Maria J Diamantopoulou
Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece)
(3)
John R Brooks
Division of Forestry and Natural Resources, West Virginia University, 322 Percival Hall, 26506-6125 Morgantown (WV - USA)

Corresponding author

 
Maria J Diamantopoulou
papamich@agro.auth.gr

Citation

Özçelik R, Diamantopoulou MJ, Brooks JR (2014). The use of tree crown variables in over-bark diameter and volume prediction models. iForest 7: 132-139. - doi: 10.3832/ifor0878-007

Academic Editor

Luca Salvati

Paper history

Received: Nov 12, 2012
Accepted: Aug 08, 2013

First online: Jan 13, 2014
Publication Date: Jun 02, 2014
Publication Time: 5.27 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 11971
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 7873
Abstract Page Views: 235
PDF Downloads: 3063
Citation/Reference Downloads: 21
XML Downloads: 779

Web Metrics
Days since publication: 2075
Overall contacts: 11971
Avg. contacts per week: 40.38

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Aug 2019)

Total number of cites (since 2014): 13
Average cites per year: 2.17

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Avery TE, Burkhart HE (2002)
Forest measurements. McGraw-Hil Inc, New York, USA, pp. 480.
Gscholar
(2)
Bailey RL (1995)
Upper stem volumes from stem analysis data: an overlapping bolts method. Canadian Journal of Forest Research 25: 170-173.
CrossRef | Gscholar
(3)
Basheer IA, Hajmeer M (2000)
Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43: 3-31.
CrossRef | Gscholar
(4)
Bi H (2000)
Trigonometric variable-form taper equations for Australian eucalyptus. Forest Science 46 (3): 397-409.
Gscholar
(5)
Burkhart HE, Walton S (1985)
Incorporation crown ratio into taper equations for loblolly pine trees. Forest Science 31 (2): 478-484.
Online | Gscholar
(6)
Clark A, Souter RA, Schlaegel BE (1991)
Stem profile equations for southern tree species. Research Paper no. SE-282, Southeastern Forest Experiment Station, USDA Forest Service, Asheville, NC, USA.
Gscholar
(7)
Corne SA, Carver SJ, Kunin WE, Lennon JJ, Van Hees WWS (2004)
Predicting forest attributes in southeast Alaska using artificial neural networks. Forest Science 50 (2): 259-276.
Online | Gscholar
(8)
Diamantopoulou MJ (2005)
Artificial neural networks as an alternative tool in pine bark volume estimation. Computers and Electronics in Agriculture 48: 235-244.
CrossRef | Gscholar
(9)
Diamantopoulou MJ (2010)
Filling gaps in diameter measurements on standing tree boles in the urban forest of Thessaloniki, Greece. Environmental Modelling & Software 25: 1857-1865.
CrossRef | Gscholar
(10)
Fausett L (1994)
Fundamentals of neural networks architectures, algorithms and applications. Prentice-Hall, Englewood Cliffs, NJ, USA.
Gscholar
(11)
Haykin S (1994)
Neural networks: a comprehensive foundation. Prentice Hall, NJ, USA.
Online | Gscholar
(12)
Jena RK, Aqel MM, Srivastava P, Mahanti PK (2009)
Soft computing methodologies in bioinformatics. European Journal of Scientific Research 26: 189-203.
CrossRef | Gscholar
(13)
Jiang L, Liu R (2011)
Segmented taper equations with crown ratio and stand density for Dahurian Larch (Larix gmelinii) in northeastern China. Journal of Forestry Research 22: 347-352.
CrossRef | Gscholar
(14)
Jiang L, Brooks JR, Wang J (2005)
Compatible taper and volume equations for yellow-poplar in West Virginia. Forest Ecology and Management 213: 399-409.
CrossRef | Gscholar
(15)
Jiang L, Brooks JR, Hobbs GR (2007)
Using crown ratio in yellow-poplar compatible taper and volume equations. Northern Journal of Applied Forestry 24 (4): 271-275.
Online | Gscholar
(16)
Kozak A, Smith JHG (1993)
Standards for evaluating taper estimating systems. Forestry Chronicle 69: 438-444.
CrossRef | Gscholar
(17)
Kozak A (1997)
Effects of multicollinearity and autocorrelation on the variable-exponent taper functions. Canadian Journal of Forest Research 27: 619-629.
CrossRef | Gscholar
(18)
Larson PR (1963)
Stem form development of forest trees. Forest Science Monograph 5 (4): 21-21.
Online | Gscholar
(19)
Leduc DJ, Matney TG, Belli KL, Baldwin VC (2001)
Predicting diameter distributions of longleaf pine plantations: a comparison between artificial neural networks and other accepted methodologies. Research Paper SRS-25, Southern Research Station, USDA Forest Service, Asheville, NC, USA, pp. 24.
Gscholar
(20)
Leite HG, Marques da Silva ML, Binoti DHB, Fardin L, Takizawa FH (2011)
Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks. European Journal of Forest Research 130: 263-269.
CrossRef | Gscholar
(21)
Leites LP, Robinson AP (2004)
Improving taper equations of loblolly pine with crown dimensions in mixed-effects modeling framework. Forest Science 50: 204-212.
Online | Gscholar
(22)
Levenberg K (1944)
A method for the solution of certain problems in least squares. Quarterly of Applied Mathematics 2: 164-168.
Gscholar
(23)
Li R, Weiskittel AR (2010)
Comparison of model forms for estimating stem taper and volume in the primary conifer species of North American Acadian region. Annals of Forest Science 67: 302-520.
CrossRef | Gscholar
(24)
Maier HR, Dandy GC (2000)
Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling andSoftware 15: 101-124.
CrossRef | Gscholar
(25)
Marquardt D (1963)
An algorithm for least squares estimation of non-linear parameters. Journal of the Society for Industrial and Applied Mathematics 11: 431-441.
CrossRef | Gscholar
(26)
Muhairwe CK (1994)
Tree form and taper variation over time for interior lodgepole pine. Canadian Journal of Forest Research 24: 1904-1913.
CrossRef | Gscholar
(27)
Mäkela A (2002)
Derivation of stem taper form the pipe theory in a carbon balance framework. Tree Physiology 22: 891-905.
CrossRef | Gscholar
(28)
Newnham RM (1992)
Variable-form taper function for four Alberta tree species. Canadian Journal of Forest Research 22: 210-223.
CrossRef | Gscholar
(29)
Özçelik R, Diamantopoulou MJ, Wiant HV, Brooks JR (2010)
Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management 91: 742-753.
CrossRef | Gscholar
(30)
Pao HT (2008)
A comparison of neural network and multiple regression analysis in modeling capital structure. Expert Systems with Applications 35: 720-727.
CrossRef | Gscholar
(31)
Patterson DW (1996)
Artificial neural networks. Prentice Hall, Englewood Cliffs, NJ, USA.
Gscholar
(32)
Petersson H (1999)
A segmented stem profile model for Pinus sylvestris. Forest Ecology and Management 124: 13-26.
CrossRef | Gscholar
(33)
Rumelhart DE, Hinton GE, Williams RJ (1986)
Learning internal representations by error propagation. In: “Parallel distributed processing vol. 1” (Rumelhart DE, McClelland JL, Corporate PDP Research Group eds). MIT Press, Cambridge, MA, USA, pp. 318-362.
Gscholar
(34)
SAS Institute Inc (2002)
SAS/ETS User’s Guide, Version 9.0. SAS Institute Inc, Cary, NC, USA.
Gscholar
(35)
Schlaegel BE (1981)
Testing, reporting, and using biomass estimation models. In: Proceedings of the “3rd Meeting of the Southern Forest Biomass Working Group” (Gresham CA ed). Georgetown (SC - USA), 11-12 June 2001. Bell W Baruch Forest Science Institute, Clemson University, NC, USA, pp. 95-112.
Gscholar
(36)
Speckt DF (1991)
A generalized regression neural network. IEEE Transactions on Neural Networks 2: 568-576.
CrossRef | Gscholar
(37)
Swingler K (2001)
Applying neural networks, a practical guide (3rd edn). Morgan Kaufman Publishers Inc, S. Francisco, CA, USA, pp. 300.
Gscholar
(38)
Trincado G, Burkhart HE (2006)
A generalized approach for modeling and localizing stem profile curves. Forest Science 52: 670-682.
Gscholar
(39)
Valenti MA, Cao QV (1986)
Use of crown ratio to improve loblolly pine taper equations. Canadian Journal of Forest Research 16: 1141-1145.
CrossRef | Gscholar
(40)
Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK (2011)
Forest growth and yield modelling. Wiley and Blackwell, London, UK, pp. 415.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website