Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

doi:10.3832/ifor4328-016

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Maria J Diamantopoulou⁽¹⁾ , Aydin Çömez⁽²⁾, Ramazan Özçelik⁽³⁾, Sükrü Teoman Güner⁽⁴⁾

iForest - Biogeosciences and Forestry, Volume 17, Issue 1, Pages 19-28 (2024)
doi: https://doi.org/10.3832/ifor4328-016
Published: Feb 12, 2024 - Copyright © 2024 SISEF

Research Articles

PDF Version

Full Text

Citation

Geo-mapping

Accurate estimates of total tree biomass are of critical importance to obtain reliable estimation of the carbon dioxide weight sequestered from the atmosphere by trees and forest stands. This information has the potential to guide appropriate forest management decisions which allow for both the improvement of forest sustainability and the implementation of multi-task reforestation designs aimed to mitigate the detrimental effects of climate change. The current laborious and tree-destructive procedures needed to attain such information has led to the development of machine learning (ML) models aimed at providing accurate estimations of the tree biomass sequestering the atmospheric carbon dioxide. We tested the Levenberg-Marquardt artificial neural network and the support vector machine for regression techniques as an alternative to non-linear allometric regression (NLR) modelling approaches commonly used for tree biomass estimation. We tested the developed ML models using primary ground-truth data from the Lebanon cedar forests in the Western Inner Anatolian regions of Turkey, and their predictions were compared to those of NLR models developed using the same dataset. The results showed that the ML approaches outperformed the NLR models in accurately estimating tree biomass and its components (above- and belowground dry biomass, dry branches biomass, etc.), and the support vector regression (SVR) models gave the highest accuracy of estimates. Therefore, the carbon dioxide weight sequestered in Lebanon cedar trees were reliably estimated, with the aim of supporting the best forest management practices to be applied in Lebanon cedar tree stands in Turkey.

Keywords

Tree Biomass, Carbon Dioxide Weight, Levenberg-Marquardt Artificial Neural Network, Support Vector Machine For Regression, Lebanon Cedar Trees

Authors’ address

(1)

0000-0002-6003-1285
Faculty of Agriculture, Forestry and Natural Environment, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece)

(2)

Aegean Forestry Research Institute, 35515 Urla, Izmir (Turkey)

(3)

0000-0003-2132-2589
Faculty of Forestry, Isparta University of Applied Sciences, 32260 Isparta (Turkey)

(4)

0000-0002-3058-7899
Department of Forestry, Ulus Vocational School, Bartin University, 74600 Ulus, Bartin (Turkey)

Corresponding author

Maria J Diamantopoulou
mdiamant@for.auth.gr

Citation

Diamantopoulou MJ, Çömez A, Özçelik R, Güner ST (2024). Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees. iForest 17: 19-28. - doi: 10.3832/ifor4328-016

Academic Editor

Giorgio Alberti

Paper history

Received: Feb 15, 2023
Accepted: Nov 15, 2023

First online: Feb 12, 2024
Publication Date: Feb 29, 2024
Publication Time: 2.97 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 81
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 0
Abstract Page Views: 0
PDF Downloads: 59
Citation/Reference Downloads: 0
XML Downloads: 22

Web Metrics
Days since publication: 75
Overall contacts: 81
Avg. contacts per week: 7.56

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Nov 2020)

(No citations were found up to date. Please come back later)

(no Plumx Metrics available for this paper)

Publication Metrics

by Dimensions ^©

Articles citing this article

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

(1)

Aydin AC (2016)
Biomass researches on Taurus cedar (Cedrus libani A. Rich.). PhD thesis, Süleyman Demirel University, Isparta, Turkey, pp. 174.
Gscholar

(2)

Bilski J, Kowalczyk B, Marchlewska A, Zurada JM (2020)
Local Levenberg-Marquardt algorithm for learning feedforward neural networks. Journal of Artificial Intelligence and Soft Computing Research 10 (4): 299-316.
CrossRef | Gscholar

(3)

Binoti DHB, Binoti MLM, Leite HG, Andrade AV, Nogueira GS, Romarco ML, Pitangui CG (2016)
Support vector machine to estimate volume of Eucalypt trees. Revista Árvore 40 (4): 689-693.
CrossRef | Gscholar

(4)

Bolat F, Ercanli I, Günlü A (2023)
Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors. iForest 16: 30-37.
CrossRef | Gscholar

(5)

Boydak M (2003)
Regeneration of Lebanon cedar (Cedrus libani A. Rich.) on karstic lands in Turkey. Forest Ecology and Management 178: 231-243.
CrossRef | Gscholar

(6)

Chen G, Hay G (2011)
A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and QuickBird data. Photogrammetric Engineering & Remote Sensing 7 (9): 733-741.
CrossRef | Gscholar

(7)

Cortes C, Vapnik VN (1995)
Support vector networks. Machine Learning 20 (3): 273-297.
CrossRef | Gscholar

(8)

Diamantopoulou MJ, Ozçelik R, Yavuz H (2018)
Tree-bark volume prediction via machine learning: a case study based on black alder’s tree-bark production. Computers and Electronics in Agriculture 151: 431-440.
CrossRef | Gscholar

(9)

Diamantopoulou MJ (2022)
Simulation of over-bark tree bole diameters, through the RFr (Random Forest regression) algorithm. Folia Oecologica 49 (2): 93-101.
CrossRef | Gscholar

(10)

Draper NR, Smith H (1998)
Applied regression analysis (3rd edn). John Wiley and Sons, Inc, New York, USA, pp. 706.
Gscholar

(11)

Durkaya B, Durkaya A, Makineci E, Ulküdür M (2013)
Estimation of above-ground biomass and sequestered carbon of Taurus Cedar (Cedrus libani L.) in Antalya, Turkey. iForest 6: 278-284.
CrossRef | Gscholar

(12)

Fausett L (1994)
Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc, USA, pp. 461.
Gscholar

(13)

GDF (2015)
Forest resources. The General Directorate of Forests, Ankara, Turkey, pp. 32.
Gscholar

(14)

GDM (2020)
Meteorological data. General Directorate of Meteorology, Ministry of Environment, Urbanization and Climate Change, Ankara, Turkey, web site. [in Turkish]
Online | Gscholar

(15)

Güner S, Diamantopoulou MJ, Poudel K, Ozçelik R (2022)
Employing artificial neural network for effective biomass prediction: an alternative approach. Computers and Electronics in Agriculture 192: 106596.
CrossRef | Gscholar

(16)

Guo Y, Li Z, Zhang X, Chen E, Bai L, Tian X, He Q, Feng Q, Li W (2012)
Optimal support vector machines for forest above-ground biomass estimation from multisource remote sensing data. In: Proceedings of the “IEEE International Geoscience and Remote Sensing Symposium”. Munich (Germany) 22-27 July 2012. IEEExplore 2012: 6388-6391.
CrossRef | Gscholar

(17)

Hamidi SK, Zenner EK, Bayat M, Fallah A (2021)
Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest. Annals of Forest Science 78: 4.
CrossRef | Gscholar

(18)

Haykin SS (2009)
Neural networks and learning machines (3rd edn). Pearson Education Inc., Upper Saddle River, Pearson Prentice Hall, New Jersey, USA, pp. 906.
Gscholar

(19)

IBM (2021)
IBM SPSS Regression, version 28.0. IBM Corp. Released, Armonk, NY, USA, pp. 35.
Gscholar

(20)

IUSS Working Group WRB (2015)
World reference base for soil resources 2014, updated 2015. World Soil Resources Reports no. 106, FAO, Rome, Italy, pp. 192.
Gscholar

(21)

Kalman D (2009)
Leveling with Lagrange: an alternate view of constrained optimization. Mathematics Magazine 82 (3): 186-196.
CrossRef | Gscholar

(22)

Konukçu M (2001)
Forests and Turkish Forestry. State Planning Organization Series, Ankara, DPT Publication, Turkey, pp. 238.
Gscholar

(23)

Kralicek K, Huy B, Poudel KP, Temesgen H, Salas C (2017)
Simultaneous estimation of above-and below-ground biomass in tropical forests of Vietnam. Forest Ecology and Management 390: 147-156.
CrossRef | Gscholar

(24)

Kriesel D (2007)
A brief introduction to neural networks (ZETA2-EN). Scalable and efficient NN framework, written in JAVA. dkriesel.com, pp. 226.
Online | Gscholar

(25)

Levenberg K (1944)
A method for the solution of certain non-linear problems in least squares Quarterly of Applied Mathematics 2 (2): 164-168.
CrossRef | Gscholar

(26)

Liashchynskyi P, Liashchynskyi P (2019)
Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv: Computer Science.
CrossRef | Gscholar

(27)

Maesano M, Santopuoli G, Moresi FV, Matteucci G, Lasserre B, Scarascia Mugnozza G (2022)
Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy. iForest 15: 451-457.
CrossRef | Gscholar

(28)

Maier HR, Dandy GC (2000)
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environmental Modelling and Software 15 (1): 101-124.
CrossRef | Gscholar

(29)

Malek S, Miglietta F, Gobakken T, Naesset E, Gianelle D, Dalponte M (2019)
Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques. iForest 12 (3): 323-329.
CrossRef | Gscholar

(30)

Marquardt DW (1963)
An algorithm for least-squares estimation of nonlinear parameters Journal of the Society for Industrial and Applied Mathematics 11 (2): 431-441.
CrossRef | Gscholar

(31)

Matlab (2022)
MATLAB version 19.12.0, release name 2022a, num. 47. The MathWorks Inc., Natick, MA, USA.
Online | Gscholar

(32)

Messinger J, Güney A, Zimmermann R, Ganser B, Bachmann M, Remmele S, Aas G (2015)
Cedrus libani: a promising tree species for Central European forestry facing climate change? European Journal of Forest Research 134: 1005-1017.
CrossRef | Gscholar

(33)

Nandy S, Singh R, Ghosh S, Watham T, Kushwaha SPS, Kumar AS, Dadhwal VK (2017)
Neural network-based modelling for forest biomass assessment. Carbon Management 8 (4): 305-317.
CrossRef | Gscholar

(34)

Njana MA, Meilby H, Eid T, Zahabu E, Malimbwi RE (2016)
Importance of tree basic density in biomass estimation and associated uncertainties: a case of three mangrove species in Tanzania. Annals of Forest Science 73: 1073-1087.
CrossRef | Gscholar

(35)

Olson DL, Delen D (2008)
Advanced data mining techniques. Springer Verlag, Berlin, Heidelberg, Germany, pp. 180.
CrossRef | Gscholar

(36)

Ozçelik R, Diamantopoulou MJ, Crecente-Campo F, Eler U (2013)
Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models. Forest Ecology and Management 306: 52-60.
CrossRef | Gscholar

(37)

Ozçelik R, Diamantopoulou MJ, Eker M, Gürlevik N (2017)
Artificial neural network models: an alternative approach for reliable aboveground pine tree biomass prediction. Forest Science 63 (3): 291-302.
CrossRef | Gscholar

(38)

Parresol BR (2001)
Additivity of nonlinear biomass equations. Canadian Journal of Forest Research 31 (5): 865-878.
CrossRef | Gscholar

(39)

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011)
Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12 (85): 2825-2830.
CrossRef | Gscholar

(40)

Poudel KP, Temesgen H, Radtke PJ, Gray AN (2019)
Estimating individual-tree aboveground biomass of tree species in the western USA. Canadian Journal of Forest Research 49 (6): 701-714.
CrossRef | Gscholar

(41)

Python Software Foundation (2022)
Python 3.9.17. Python.org.
Online | Gscholar

(42)

Singh V, Gupta I, Gupta HO (2007)
ANN-based estimator for distillation using Levenberg-Marquardt approach. Engineering Applications of Artificial Intelligence 20 (2): 249-259.
CrossRef | Gscholar

(43)

Smola AJ, Schölkopf B (2004)
A tutorial on support vector regression. Statistics and Computing 14 (3): 199-222.
CrossRef | Gscholar

(44)

Tavares Júnior IDS, Rocha JECD, Ebling A, Chaves ADS, Zanuncio JC, Farias AA, Leite HG (2019)
Artificial neural networks and linear regression reduce sample intensity to predict the commercial volume of Eucalyptus clones. Forests 10 (3): 268.
CrossRef | Gscholar

(45)

Van Rossum G, Drake FL (2011)
The Python language reference manual. Network Theory Ltd., New York, USA.
Gscholar

(46)

Vapnik VN (1998)
Statistical learning theory. Wiley, New York, pp. 768.
Gscholar

(47)

Vapnik VN (2000)
The nature of statistical learning theory (2nd edn). Springer-Verlag, New York, USA, pp. 314.
CrossRef | Gscholar

(48)

Wilamowski BM, Yu H (2010)
Improved computation for Levenberg-Marquardt training. IEEE Transactions on Neural Networks 21 (6): 930-937.
CrossRef | Gscholar

(49)

Wu D (2014)
Estimation of forest volume based on LM-BP neural network model. Computer Modelling and New Technologies 18 (4): 131-137.
Online | Gscholar

(50)

Wu D, Ji Y (2015)
Dynamic estimation of forest volume based on multi-source data and neural network model. Journal of Agricultural Science 7(3): 18.
CrossRef | Gscholar

(51)

Youquan J, Lixi Z, Ou D, Weiheng X, Zhongke F (2012)
Calculation of live tree timber volume based on particle swarm optimization and support vector regression. Transactions of the Chinese Society of Agricultural Engineering 29 (20): 160-167.
CrossRef | Gscholar

(52)

Zhao D, Kane M, Teskey R, Markewitz D (2016)
Modeling aboveground biomass components and volume-to-weight conversion ratios for loblolly pine trees. Forest Science 62 (5): 463-473.
CrossRef | Gscholar

iForest - Biogeosciences and Forestry

Contents

Search

Journal Info

Journal Subjects and Fields

For Authors

For Reviewers

For Readers

SISEF Publishing

Search iForest Contents