Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches
This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees....
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| Veröffentlicht in: | Forest science and technology Jg. 21; H. 1; S. 110 - 122 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Seoul
Taylor & Francis
02.01.2025
Taylor & Francis Ltd Taylor & Francis Group 한국산림과학회 |
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| ISSN: | 2158-0103, 2158-0715, 2158-0715 |
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| Abstract | This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms-Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)-to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity.
Innovative use of machine learning in forest management practices
Enhanced predictive accuracy for plant biomass and volume metrics
Assessment of total biomass and its components (leaves, branches, stems, and roots)
Leveraging machine learning to predict essential forest metrics |
|---|---|
| AbstractList | This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms-Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)-to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. Innovative use of machine learning in forest management practices Enhanced predictive accuracy for plant biomass and volume metrics Assessment of total biomass and its components (leaves, branches, stems, and roots) Leveraging machine learning to predict essential forest metrics This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. KCI Citation Count: 0 |
| Author | Antúnez, Pablo Ruiz-Aquino, Faustino Wehenkel, Christian Calixto-Valencia, Celi Gloria Valenzuela-Encinas, César Basave-Villalobos, Erickson Sarmiento-Bustos, David |
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| Cites_doi | 10.3389/fnagi.2017.00329 10.1002/wics.49 10.1163/9789004211810 10.1002/9781118874059.ch7 10.1016/j.foreco.2008.07.022 10.1007/978-3-540-75171-7 10.1007/s11277-017-5224-x 10.1007/s10694-020-01056-z 10.1080/21580103.2013.834277 10.1111/0272-4332.00040 10.3389/fpls.2013.00402 10.1890/07-0539.1 10.1016/j.foreco.2018.12.019 10.1038/nbt1386 10.1139/er-2018-0034 10.1007/s00226-021-01309-2 10.1016/j.jenvman.2023.117251 10.1177/1536867X20909688 10.1038/s41561-023-01274-4 10.1007/978-3-642-31537-4_13 10.3390/rs11161944 10.3390/su14127154 10.19136/era.a8n1.2616 10.4249/scholarpedia.1883 10.1007/978-1-4302-5990-9 10.1093/forsci/fxaa032 10.3389/fnbot.2013.00021 10.1007/978-1-4419-9326-7_5 10.1007/s11676-021-01425-6 10.1257/jep.15.4.143 10.1016/j.ieri.2014.03.004 10.5849/forsci.10-057 10.1007/s10208-008-9026-0 10.1214/aos/1013203451 10.1007/1-4020-4393-7 10.1890/ES14-00251.1 10.26525/jtfs2023.35.2.130 10.1007/978-3-030-89010-0_9 10.1007/s00521-019-04644-5 10.1016/j.marpetgeo.2022.105597 10.21037/atm.2016.03.37 10.1016/j.knosys.2021.106993 10.3389/fenvs.2024.1291327 10.18637/jss.v046.i07 10.1007/3-540-36755-1_24 10.1067/msy.2000.102173 10.1039/C0AN00387E 10.1016/j.neunet.2013.03.001 10.1016/j.foreco.2015.08.015 10.1007/s11749-016-0481-7 10.1038/nbt1206-1565 10.1109/CANDO-EPE60507.2023.10418014 10.1080/24749508.2018.1522837 10.1007/978-3-319-18305-3_1 10.1016/j.apgeog.2013.09.024 10.1007/978-3-642-02532-7 10.1111/j.1442-9993.1992.tb00790.x 10.1007/978-3-642-34062-8_32 10.1016/B978-0-443-19415-3.00011-6 10.3390/app13148275 10.1007/978-3-642-04274-4_96 10.1007/978-3-642-38652-7_2 |
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| References | e_1_3_2_28_1 e_1_3_2_49_1 e_1_3_2_20_1 e_1_3_2_41_1 e_1_3_2_66_1 e_1_3_2_22_1 e_1_3_2_43_1 e_1_3_2_24_1 e_1_3_2_45_1 e_1_3_2_26_1 e_1_3_2_47_1 e_1_3_2_68_1 e_1_3_2_62_1 e_1_3_2_83_1 e_1_3_2_60_1 e_1_3_2_81_1 e_1_3_2_16_1 e_1_3_2_39_1 e_1_3_2_9_1 e_1_3_2_18_1 e_1_3_2_7_1 e_1_3_2_31_1 e_1_3_2_54_1 e_1_3_2_77_1 e_1_3_2_10_1 e_1_3_2_33_1 e_1_3_2_75_1 e_1_3_2_12_1 e_1_3_2_35_1 Mahesh B. (e_1_3_2_52_1) 2020; 9 e_1_3_2_58_1 Perry P.J. (e_1_3_2_61_1) 1991 e_1_3_2_5_1 e_1_3_2_37_1 e_1_3_2_56_1 e_1_3_2_79_1 e_1_3_2_3_1 Snowdon P. (e_1_3_2_74_1) 2000 e_1_3_2_50_1 e_1_3_2_73_1 e_1_3_2_71_1 Kuhn M. (e_1_3_2_48_1) 2024; 223 e_1_3_2_27_1 e_1_3_2_29_1 Prodan M. (e_1_3_2_64_1) 1997 Boswell D. (e_1_3_2_14_1) 2002; 11 e_1_3_2_42_1 e_1_3_2_65_1 e_1_3_2_21_1 e_1_3_2_44_1 e_1_3_2_63_1 e_1_3_2_23_1 e_1_3_2_46_1 e_1_3_2_69_1 e_1_3_2_25_1 e_1_3_2_67_1 e_1_3_2_80_1 e_1_3_2_40_1 e_1_3_2_82_1 e_1_3_2_17_1 e_1_3_2_38_1 e_1_3_2_8_1 e_1_3_2_19_1 e_1_3_2_2_1 e_1_3_2_55_1 e_1_3_2_76_1 e_1_3_2_11_1 e_1_3_2_32_1 e_1_3_2_53_1 e_1_3_2_6_1 e_1_3_2_13_1 Eguiluz P.T. (e_1_3_2_30_1) 1982; 71 e_1_3_2_34_1 e_1_3_2_59_1 e_1_3_2_4_1 e_1_3_2_15_1 e_1_3_2_36_1 e_1_3_2_57_1 e_1_3_2_78_1 e_1_3_2_51_1 e_1_3_2_72_1 e_1_3_2_70_1 |
| References_xml | – ident: e_1_3_2_70_1 doi: 10.3389/fnagi.2017.00329 – ident: e_1_3_2_22_1 – ident: e_1_3_2_53_1 doi: 10.1002/wics.49 – ident: e_1_3_2_33_1 doi: 10.1163/9789004211810 – ident: e_1_3_2_49_1 doi: 10.1002/9781118874059.ch7 – ident: e_1_3_2_59_1 doi: 10.1016/j.foreco.2008.07.022 – ident: e_1_3_2_44_1 – ident: e_1_3_2_24_1 doi: 10.1007/978-3-540-75171-7 – ident: e_1_3_2_79_1 doi: 10.1007/s11277-017-5224-x – ident: e_1_3_2_2_1 doi: 10.1007/s10694-020-01056-z – ident: e_1_3_2_19_1 doi: 10.1080/21580103.2013.834277 – ident: e_1_3_2_69_1 doi: 10.1111/0272-4332.00040 – ident: e_1_3_2_27_1 doi: 10.3389/fpls.2013.00402 – ident: e_1_3_2_26_1 doi: 10.1890/07-0539.1 – ident: e_1_3_2_83_1 doi: 10.1016/j.foreco.2018.12.019 – ident: e_1_3_2_66_1 – ident: e_1_3_2_46_1 doi: 10.1038/nbt1386 – ident: e_1_3_2_51_1 doi: 10.1139/er-2018-0034 – ident: e_1_3_2_78_1 doi: 10.1007/s00226-021-01309-2 – ident: e_1_3_2_8_1 doi: 10.1016/j.jenvman.2023.117251 – ident: e_1_3_2_15_1 – ident: e_1_3_2_23_1 – ident: e_1_3_2_71_1 doi: 10.1177/1536867X20909688 – ident: e_1_3_2_80_1 doi: 10.1038/s41561-023-01274-4 – ident: e_1_3_2_60_1 doi: 10.1007/978-3-642-31537-4_13 – ident: e_1_3_2_32_1 doi: 10.3390/rs11161944 – ident: e_1_3_2_72_1 doi: 10.3390/su14127154 – ident: e_1_3_2_6_1 doi: 10.19136/era.a8n1.2616 – ident: e_1_3_2_62_1 doi: 10.4249/scholarpedia.1883 – ident: e_1_3_2_9_1 doi: 10.1007/978-1-4302-5990-9 – ident: e_1_3_2_28_1 doi: 10.1093/forsci/fxaa032 – ident: e_1_3_2_57_1 doi: 10.3389/fnbot.2013.00021 – ident: e_1_3_2_25_1 doi: 10.1007/978-1-4419-9326-7_5 – ident: e_1_3_2_5_1 doi: 10.1007/s11676-021-01425-6 – ident: e_1_3_2_42_1 doi: 10.1257/jep.15.4.143 – ident: e_1_3_2_37_1 doi: 10.1016/j.ieri.2014.03.004 – ident: e_1_3_2_67_1 doi: 10.5849/forsci.10-057 – ident: e_1_3_2_76_1 doi: 10.1007/s10208-008-9026-0 – ident: e_1_3_2_35_1 doi: 10.1214/aos/1013203451 – ident: e_1_3_2_47_1 – ident: e_1_3_2_77_1 doi: 10.1007/1-4020-4393-7 – ident: e_1_3_2_17_1 – ident: e_1_3_2_68_1 doi: 10.1890/ES14-00251.1 – ident: e_1_3_2_7_1 doi: 10.26525/jtfs2023.35.2.130 – ident: e_1_3_2_56_1 doi: 10.1007/978-3-030-89010-0_9 – ident: e_1_3_2_39_1 doi: 10.1007/s00521-019-04644-5 – ident: e_1_3_2_41_1 doi: 10.1016/j.marpetgeo.2022.105597 – ident: e_1_3_2_82_1 doi: 10.21037/atm.2016.03.37 – ident: e_1_3_2_43_1 doi: 10.1016/j.knosys.2021.106993 – ident: e_1_3_2_63_1 – ident: e_1_3_2_55_1 – ident: e_1_3_2_34_1 doi: 10.3389/fenvs.2024.1291327 – ident: e_1_3_2_11_1 doi: 10.18637/jss.v046.i07 – ident: e_1_3_2_54_1 doi: 10.1007/3-540-36755-1_24 – ident: e_1_3_2_29_1 doi: 10.1067/msy.2000.102173 – volume: 9 start-page: 381 issue: 1 year: 2020 ident: e_1_3_2_52_1 article-title: Machine learning algorithms-a review publication-title: International Journal of Science and Research – ident: e_1_3_2_10_1 doi: 10.1039/C0AN00387E – ident: e_1_3_2_16_1 – ident: e_1_3_2_20_1 doi: 10.1016/j.neunet.2013.03.001 – ident: e_1_3_2_3_1 doi: 10.1016/j.foreco.2015.08.015 – ident: e_1_3_2_13_1 doi: 10.1007/s11749-016-0481-7 – start-page: 586 volume-title: IICA/GTZ year: 1997 ident: e_1_3_2_64_1 – ident: e_1_3_2_58_1 doi: 10.1038/nbt1206-1565 – ident: e_1_3_2_36_1 doi: 10.1109/CANDO-EPE60507.2023.10418014 – volume: 11 start-page: 16 year: 2002 ident: e_1_3_2_14_1 article-title: Introduction to support vector machines publication-title: Department of Computer Science and Engineering University of California San Diego – ident: e_1_3_2_75_1 doi: 10.1080/24749508.2018.1522837 – ident: e_1_3_2_31_1 doi: 10.1007/978-3-319-18305-3_1 – ident: e_1_3_2_65_1 – ident: e_1_3_2_40_1 doi: 10.1016/j.apgeog.2013.09.024 – ident: e_1_3_2_38_1 – ident: e_1_3_2_81_1 doi: 10.1007/978-3-642-02532-7 – ident: e_1_3_2_18_1 doi: 10.1111/j.1442-9993.1992.tb00790.x – ident: e_1_3_2_50_1 doi: 10.1007/978-3-642-34062-8_32 – ident: e_1_3_2_73_1 – start-page: 133 volume-title: Australian Greenhouse Office year: 2000 ident: e_1_3_2_74_1 – volume: 71 start-page: 30 issue: 38 year: 1982 ident: e_1_3_2_30_1 article-title: Clima y distribución del género Pinus en México publication-title: Ciencia Forestal – ident: e_1_3_2_12_1 doi: 10.1016/B978-0-443-19415-3.00011-6 – ident: e_1_3_2_4_1 doi: 10.3390/app13148275 – volume: 223 start-page: 48 issue: 7 year: 2024 ident: e_1_3_2_48_1 article-title: Package ‘caret’ publication-title: The R Journal – start-page: 231 volume-title: The Pines of México and Central America. Timber Press year: 1991 ident: e_1_3_2_61_1 – ident: e_1_3_2_21_1 doi: 10.1007/978-3-642-04274-4_96 – ident: e_1_3_2_45_1 doi: 10.1007/978-3-642-38652-7_2 |
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| SubjectTerms | Algorithms allometric modeling allometry Biomass Branches Effectiveness Forest management forests heteroskedasticity Learning algorithms Least squares Machine learning machine learning algorithms in forestry Neural networks Pinus pseudostrobus predicting forest biomass Prediction models quantile regression in forest management Quantiles Random Forest algorithm regression analysis root systems Stems Support vector machines technology Trees 임학 |
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| Title | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
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