Applications of machine learning algorithms in forest growth and yield prediction

Lei Xiangdong

doi:10.12171/j.1000-1522.20190356

Journal of Beijing Forestry University > 2019 > 41(12): 23-36. > DOI: 10.12171/j.1000-1522.20190356

Lei Xiangdong. Applications of machine learning algorithms in forest growth and yield prediction[J]. Journal of Beijing Forestry University, 2019, 41(12): 23-36. DOI: 10.12171/j.1000-1522.20190356

Citation:

Lei Xiangdong. Applications of machine learning algorithms in forest growth and yield prediction[J]. Journal of Beijing Forestry University, 2019, 41(12): 23-36. DOI: 10.12171/j.1000-1522.20190356

Citation:

Lei Xiangdong. Applications of machine learning algorithms in forest growth and yield prediction[J]. Journal of Beijing Forestry University, 2019, 41(12): 23-36. DOI: 10.12171/j.1000-1522.20190356

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Applications of machine learning algorithms in forest growth and yield prediction

Lei Xiangdong

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

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Received Date: September 10, 2019
Revised Date: November 26, 2019
Available Online: November 27, 2019
Published Date: November 30, 2019

Graphical Abstract

Abstract

Abstract

Forest growth and yield prediction is an important field of forest management science, and modelling forest growth and yield is key to forest management decision-making. The traditional statistical growth models such as linear and nonlinear regression model, mixed-effect model, quantile regression, variable-in-error model are often applied under certain statistical assumptions, such as the data are independent, normally distributed and homoscedastic. The above requirements are usually difficult to be met for forest data with repeated observation and hierarchy. With the development of AI techniques, machine learning provides a new way for forest growth modeling, with the advantages of no requirements on data distribution, extracting deep knowledge from the data, and high accuracy. The applications in forest growth and yield are still less than other domains. We reviewed the main machine learning algorithms including classification and regression tree (CART), multivariate adaptive regression splines (MARS), bagging regression, boosted regression tree (BRT), random forest (RF), artificial neural networks (ANN), k-nearest neighbors (k-NN), and support vector machine (SVM), parameter tuning, software, advantages and challenge. We conclude that machine learning would be widely applied with great potential and its combination with traditional statistical methods would become a trend in forest growth and yield prediction.
- forest growth and yield prediction,
- regression,
- classification,
- machine learning algorithm

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References (110)

References

[1]	Weiskittel A R, Hann D W, Kershaw Jr J A, et al. Forest growth and yield modeling[M]. Chichester: John Wiley and Sons, 2011.
[2]	唐守正, 李希菲, 孟昭和. 林分生长模型研究的进展[J]. 林业科学研究, 1993, 6(6):672−679. doi: 10.3321/j.issn:1001-1498.1993.06.018 Tang S Z, Li X F, Meng Z H. The development of studies on stand growth models[J]. Forest Research, 1993, 6(6): 672−679. doi: 10.3321/j.issn:1001-1498.1993.06.018
[3]	Peng C H. Growth and yield models for uneven-aged stands: past, present and future[J]. Forest Ecology and Management, 2000, 132(2−3): 259−279. doi: 10.1016/S0378-1127(99)00229-7
[4]	Huang S L, Ramirez C, McElhaney M, et al. F³: simulating spatiotemporal forest change from field inventory, remote sensing, growth modeling, and management actions[J]. Forest Ecology and Management, 2018, 415−416: 26−37. doi: 10.1016/j.foreco.2018.02.026
[5]	Cutler D R, Edwards T C, Beard K H, et al. Random forests for classification in ecology[J]. Ecology, 2007, 88(11): 2783−2792. doi: 10.1890/07-0539.1
[6]	Wu C F, Shen H H, Shen A H, et al. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery[J]. Journal of Applied Remote Sensing, 2016, 10(3): 035010. doi: 10.1117/1.JRS.10.035010
[7]	Recknagel F. Applications of machine learning to ecological modelling[J]. Ecological Modelling, 2001, 146(1−3): 303−310.
[8]	Liu Z L, Peng C H, Xiang W H, et al. Application of artificial neural networks in global climate change and ecological research: an overview[J]. Chinese Science Bulletin, 2010, 55(34): 3853−3863. doi: 10.1007/s11434-010-4183-3
[9]	Guan B T, Gertner G. Modeling red pine tree survival with an artificial neural network[J]. Forest Science, 1991, 37(5): 1429−1440.
[10]	Guan B T, Gertner G. Using a parallel distributed processing system to model individual tree mortality[J]. Forest Science, 1991, 37(3): 871−885.
[11]	李际平, 姚东和. BP模型在单木树高与胸径生长模拟中的应用[J]. 中南林学院学报, 1996, 16(3):34−36. Li J P, Yao D H. Application of BP neural network model to the simulation of breast height diameter and tree-height growth[J]. Journal of Central-South Forestry University, 1996, 16(3): 34−36.
[12]	洪伟, 吴承祯, 何东进. 基于人工神经网络的森林资源管理模型研究[J]. 自然资源学报, 1998, 13(1):69−72. doi: 10.3321/j.issn:1000-3037.1998.01.012 Hong W, Wu C Z, He D J. A study on the model of forest resources management based on the artificial neural network[J]. Journal of Natural Resources, 1998, 13(1): 69−72. doi: 10.3321/j.issn:1000-3037.1998.01.012
[13]	浦瑞良, 宫鹏. 应用神经网络和多元回归技术预测森林产量[J]. 应用生态学报, 1999, 10(2):129−134. doi: 10.3321/j.issn:1001-9332.1999.02.001 Pu R L, Gong P. Forest yield prediction with an artificial neural network and multiple regression[J]. Chinese Journal of Applied Ecology, 1999, 10(2): 129−134. doi: 10.3321/j.issn:1001-9332.1999.02.001
[14]	林辉, 彭长辉. 人工神经网络在森林资源管理中的应用[J]. 世界林业研究, 2002, 15(3):22−31. doi: 10.3969/j.issn.1001-4241.2002.03.004 Lin H, Peng C H. Application of artificial neural network in forest resource management[J]. World Forestry Research, 2002, 15(3): 22−31. doi: 10.3969/j.issn.1001-4241.2002.03.004
[15]	黄家荣, 孟宪宇, 关毓秀. 马尾松人工林单木生长神经网络模型研究[J]. 山地农业生物学报, 2004, 23(5):386−391. doi: 10.3969/j.issn.1008-0457.2004.05.003 Huang J R, Meng X Y, Guan Y X. The study on neural network models of individual tree growth in Pinus massoniana plantation[J]. Journal of Mountain Agriculture and Biology, 2004, 23(5): 386−391. doi: 10.3969/j.issn.1008-0457.2004.05.003
[16]	Peng C H, Wen X Z. Recent applications of artificial neural networks in forest resource management: an overview[C/OL]//Environmental Decision Support Systems and Artificial Intelligence. AAAI, 1999 [2019−06−16]. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.487.7652.
[17]	Liu Z L, Peng C H, Work T, et al. Application of machine-learning methods in forest ecology: recent progress and future challenges[J]. Environmental Reviews, 2018, 26(4): 339−350. doi: 10.1139/er-2018-0034
[18]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016. Zhou Z H. Machine leaning[M]. Beijing: Tsinghua University Press, 2016.
[19]	Zhou Z H. Machine learning: recent progress in China and beyond[J]. National Science Review, 2018, 5(1): 20. doi: 10.1093/nsr/nwx132
[20]	吴喜之. 应用回归及分类: 基于R[M]. 北京: 中国人民大学出版社, 2016. Wu X Z. Applied regression and classification with R[M]. Beijing: China People’s University Press, 2016.
[21]	Dobbertin M, Biging G S. Using the non-parametric classifier CART to model forest tree mortality[J]. Forest Science, 1998, 44(4): 507−516.
[22]	Fan Z F, Kabrick J M, Shifley S R. Classification and regression tree based survival analysis in oak-dominated forests of Missouri’s Ozark highlands[J]. Canadian Journal of Forest Research, 2006, 36(7): 1740−1748. doi: 10.1139/x06-068
[23]	Adamec Z, Drápela K. Comparison of parametric and nonparametric methods for modeling height-diameter relationships[J]. iForest-Biogeosciences and Forestry, 2017, 10(1): 1−8. doi: 10.3832/ifor1928-009
[24]	Aertsen W, Kint V, van Orshoven J, et al. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests[J]. Ecological Modelling, 2010, 221(8): 1119−1130. doi: 10.1016/j.ecolmodel.2010.01.007
[25]	Räty M, Kangas A. Localizing general models with classification and regression trees[J]. Scandinavian Journal of Forest Research, 2008, 23(5): 419−430. doi: 10.1080/02827580802378826
[26]	Piramuthu S. Input data for decision trees[J]. Expert Systems with Applications, 2008, 34(2): 1220−1226. doi: 10.1016/j.eswa.2006.12.030
[27]	Rejwan C, Collins N C, Brunner L J, et al. Tree regression analysis on the nesting habitat of smallmouth bass[J]. Ecology, 1999, 80(1): 341−348. doi: 10.1890/0012-9658(1999)080[0341:TRAOTN]2.0.CO;2
[28]	Friedman J H. Multivariate adaptive regression splines[J]. Annals of Statistics, 1991, 19(1): 1−67. doi: 10.1214/aos/1176347963
[29]	Prasad A M, Iverson L R, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction[J]. Ecosystems, 2006, 9(2): 181−199. doi: 10.1007/s10021-005-0054-1
[30]	Chojnacky D C, Heath L S. Estimating down deadwood from FIA forest inventory variables in Maine[J]. Environmental Pollution, 2002, 116(Suppl.1): S25−S30.
[31]	Hart S J, Laroque C P. Searching for thresholds in climate-radial growth relationships of Engelmann spruce and subalpine fir, Jasper National Park, Alberta, Canada[J]. Dendrochronologia, 2013, 31(1): 9−15. doi: 10.1016/j.dendro.2012.04.005
[32]	Moisen G G, Frescino T S. Comparing five modelling techniques for predicting forest characteristics[J]. Ecological Modelling, 2002, 157(2/3): 209−225.
[33]	Ou Q X, Lei X D, Shen C C. Individual tree diameter growth models of larch-spruce-fir mixed forests based on machine learning algorithms[J]. Forests, 2019, 10(2): 187. doi: 10.3390/f10020187
[34]	Lee T S, Chiu C C, Chou Y C, et al. Mining the customer credit using classification and regression tree and multivariate adaptive regression splines[J]. Computational Statistics and Data Analysis, 2006, 50(4): 1113−1130. doi: 10.1016/j.csda.2004.11.006
[35]	Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree[J]. Journal of Hydrology, 2018, 559: 499−509. doi: 10.1016/j.jhydrol.2018.02.061
[36]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5−32. doi: 10.1023/A:1010933404324
[37]	Friedman J H. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 2001, 29(5): 1189−1232.
[38]	Goldstein A, Kapelner A, Bleich J, et al. Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation[J]. Journal of Computational and Graphical Statistics, 2015, 24(1): 44−65. doi: 10.1080/10618600.2014.907095
[39]	Strobl C, Boulesteix A L, Kneib T, et al. Conditional variable importance for random forests[J]. BMC bioinformatics, 2008, 9: 307. doi: 10.1186/1471-2105-9-307
[40]	Weiskitte A R, Crookston N L, Radtke P J. Linking climate, gross primary productivity, and site index across forests of the western United States[J]. Canadian Journal of Forest Research, 2011, 41(8): 1710−1721. doi: 10.1139/x11-086
[41]	Bond-Lamberty B, Rocha A V, Calvin K, et al. Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest[J]. Global Change Biology, 2014, 20(1): 216−227. doi: 10.1111/gcb.12404
[42]	Kilham P, Hartebrodt C, Kändler R G. Generating tree-level harvest predictions from forest inventories with random forests[J]. Forests, 2019, 10(1): 20.
[43]	欧强新, 雷相东, 沈琛琛, 等. 基于随机森林算法的落叶松-云冷杉混交林单木胸径生长预测[J]. 北京林业大学学报, 2019, 41(9):9−19. Ou Q X, Lei X D, Shen C C, et al. Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm[J]. Journal of Beijing Forestry University, 2019, 41(9): 9−19.
[44]	Nunes M H, Görgens E B. Artificial intelligence procedures for tree taper estimation within a complex vegetation mosaic in Brazil[J/OL]. PLoS One, 2016, 11(5): e0154738 [2019−10−02]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0154738.
[45]	De ’ath G. Boosted trees for ecological modeling and prediction[J]. Ecology, 2007, 88(1): 243−251. doi: 10.1890/0012-9658(2007)88[243:BTFEMA]2.0.CO;2
[46]	Freeman E A, Moisen G G, Coulston J W, et al. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance[J]. Canadian Journal of Forest Research, 2016, 46(3): 323−339. doi: 10.1139/cjfr-2014-0562
[47]	Kuhn M, Johnson K. Applied predictive modeling[M]. New York: Springer, 2013.
[48]	Elith J, Leathwick J R, Hastie T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008, 77(4): 802−813. doi: 10.1111/j.1365-2656.2008.01390.x
[49]	Mezei P, Grodzki W, Blaženec M, et al. Host and site factors affecting tree mortality caused by the spruce bark beetle (Ips typographus) in mountainous conditions[J]. Forest Ecology and Management, 2014, 331: 196−207. doi: 10.1016/j.foreco.2014.07.031
[50]	Sproull G J, Adamus M, Bukowski M, et al. Tree and stand-level patterns and predictors of Norway spruce mortality caused by bark beetle infestation in the Tatra Mountains[J]. Forest Ecology and Management, 2015, 354: 261−271. doi: 10.1016/j.foreco.2015.06.006
[51]	Oguro M, Imahiro S, Saito S, et al. Relative importance of multiple scale factors to oak tree mortality due to Japanese oak wilt disease[J]. Forest Ecology and Management, 2015, 356: 173−183. doi: 10.1016/j.foreco.2015.07.016
[52]	Cai W H, Yang J, Liu Z H, et al. Post-fire tree recruitment of a boreal larch forest in Northeast China[J]. Forest Ecology and Management, 2013, 307: 20−29. doi: 10.1016/j.foreco.2013.06.056
[53]	De Cauwer V, Fichtler E, Beeckman H, et al. Predicting site productivity of the timber tree Pterocarpus angolensis[J]. Southern Forests: a Journal of Forest Science, 2017, 79(3): 259−268. doi: 10.2989/20702620.2016.1256042
[54]	Razakamanarivo R H, Grinand C, Razafindrakoto M A, et al. Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: a multiple regression approach[J]. Geoderma, 2011, 162(3−4): 335−346.
[55]	Lin D M, Anderson-Teixeira K J, Lai J S, et al. Traits of dominant tree species predict local scale variation in forest aboveground and topsoil carbon stocks[J]. Plant and Soil, 2016, 409(1−2): 435−446.
[56]	欧强新, 李海奎, 杨英. 福建地区马尾松生物量转换和扩展因子的影响因素[J]. 生态学报, 2017, 37(17):5756−5764. Ou Q X, Li H K, Yang Y. Factors affecting the biomass conversion and expansion factor of Masson pine in Fujian Province[J]. Acta Ecologica Sinica, 2017, 37(17): 5756−5764.
[57]	欧强新, 李海奎, 雷相东, 等. 基于清查数据的福建省马尾松生物量转换和扩展因子估算差异解析:3种集成学习决策树模型的比较[J]. 应用生态学报, 2018, 29(6):2007−2016. Ou Q X, Li H K, Lei X D, et al. Difference analysis in estimating biomass conversion and expansion factors of masson pine in Fujian Province, China based on national forest inventory data: a comparison of three decision tree models of ensemble learning[J]. Chinese Journal of Applied Ecology, 2018, 29(6): 2007−2016.
[58]	Ren Y, Chen S S, Wei X H, et al. Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China[J]. Journal of Forestry Research, 2016, 27(4): 919−930. doi: 10.1007/s11676-016-0237-y
[59]	Aertsen W, Kint V, De Vos B, et al. Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees[J]. Plant and Soil, 2012, 354(1−2): 157−172.
[60]	Mitsopoulos I, Xanthopoulos G. Effect of stand, topographic, and climatic factors on the fuel complex characteristics of Aleppo (Pinus halepensis Mill.) and Calabrian (Pinus brutia Ten.) pine forests of Greece[J]. Forest Ecology and Management, 2016, 360: 110−121. doi: 10.1016/j.foreco.2015.10.027
[61]	Fricker G A, Synes N W, Serra-Diaz J M, et al. More than climate? Predictors of tree canopy height vary with scale in complex terrain, Sierra Nevada, CA (USA)[J]. Forest Ecology and Management, 2019, 434: 142−153. doi: 10.1016/j.foreco.2018.12.006
[62]	王星. 大数据分析: 方法与应用[M]. 北京: 清华大学出版社, 2013. Wang X. Big data analysis: methods and applications[M]. Beijing: Tsinghua University Press, 2013.
[63]	Ciaburro G, Venkateswaran B. 神经网络: R语言实现[M]. 李洪成, 译. 北京: 机械工业出版社, 2018. Ciaburro G, Venkateswaran B. Neural networks with R[M]. Li H C, trans. Beijing: China Machine Press, 2018.
[64]	Ciaburro G, Venkateswaran B. Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles[M]. Birmingham: Packt Publishing, 2017.
[65]	Hasenauer H, Merkl D, Weingartner M. Estimating tree mortality of Norway spruce stands with neural networks[J]. Advances in Environmental Research, 2001, 5(4): 405−414. doi: 10.1016/S1093-0191(01)00092-2
[66]	Castro R V O, Boechat Soares C P, Leite H G, et al. Individual growth model for Eucalyptus stands in Brazil using artificial neural network[J/OL]. ISRN Forestry, 2013, 2013: Article ID 196832 [2019−05−18]. https://www.hindawi.com/journals/isrn/2013/196832/.
[67]	Reis L P, de Souza A L, dos Reis P C M, et al. Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest[J]. Ecological Engineering, 2018, 112: 140−147. doi: 10.1016/j.ecoleng.2017.12.014
[68]	da Rocha S J S S, Torres C M M E, Jacovine L A G, et al. Artificial neural networks: modeling tree survival and mortality in the Atlantic Forest biome in Brazil[J]. Science of the Total Environment, 2018, 645: 655−661. doi: 10.1016/j.scitotenv.2018.07.123
[69]	Bayat M, Ghorbanpour M, Zare R, et al. Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran[J]. Computers and Electronics in Agriculture, 2019, 164: 104929. doi: 10.1016/j.compag.2019.104929
[70]	Soares F A A M N, Flôres E L, Cabacinha C D, et al. Recursive diameter prediction and volume calculation of eucalyptus trees using multilayer perceptron networks[J]. Computers and Electronics in Agriculture, 2011, 78(1): 19−27. doi: 10.1016/j.compag.2011.05.008
[71]	Ashraf M I, Zhao Z Y, Bourque C P A, et al. Integrating biophysical controls in forest growth and yield predictions with artificial intelligence technology[J]. Canadian Journal of Forest Research, 2013, 43(12): 1162−1171. doi: 10.1139/cjfr-2013-0090
[72]	Vieira G C, de Mendonça A R, da Silva G F, et al. Prognoses of diameter and height of trees of eucalyptus using artificial intelligence[J]. Science of the Total Environment, 2018, 619: 1473−1481. doi: 10.1016/j.scitotenv.2017.11.138
[73]	马翔宇, 段文英, 崔金刚. 白桦人工林单木生长的人工神经网络模型研究[J]. 森林工程, 2009, 25(3):30−33, 38. doi: 10.3969/j.issn.1001-005X.2009.03.007 Ma X Y, Duan W Y, Cui J G. Study on the artificial neural network model of individual tree growth in the Betula platyphlla plantation[J]. Forest Engineering, 2009, 25(3): 30−33, 38. doi: 10.3969/j.issn.1001-005X.2009.03.007
[74]	沈剑波, 雷相东, 李玉堂, 等. 基于BP神经网络的长白落叶松人工林林分平均高预测[J]. 南京林业大学学报(自然科学版), 2018, 42(2):147−154. Shen J B, Lei X D, Li Y T, et al. Prediction mean height for Larix olgensis plantation based on Bayesian-regularization BP neural network[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2018, 42(2): 147−154.
[75]	车少辉, 张建国, 段爱国, 等. 杉木人工林胸径生长神经网络建模研究[J]. 西北农林科技大学学报(自然科学版), 2012, 40(3):84−92. Che S H, Zhang J G, Duan A G, et al. Modelling tree diameter growth for Chinese fir plantations with neural networks[J]. Journal of Northwest A&F University (Natural Sciences Edition), 2012, 40(3): 84−92.
[76]	龙滔, 覃连欢, 叶绍明. 基于BP神经网络连栽桉树人工林生长量预测[J]. 东北林业大学学报, 2012, 40(5):122−125. doi: 10.3969/j.issn.1000-5382.2012.05.030 Long T, Qin L H, Ye S M. Prediction for the growth of Eucalyptus plantations with continuous-planting rotations based on BP neural network[J]. Journal of Northeast Forestry University, 2012, 40(5): 122−125. doi: 10.3969/j.issn.1000-5382.2012.05.030
[77]	Reis L P, de Souza A L, Mazzei L, et al. Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks[J]. Forest Ecology and Management, 2016, 382: 161−167. doi: 10.1016/j.foreco.2016.10.022
[78]	Vahedi A A. Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran[J]. Biomass and Bioenergy, 2016, 88: 66−76. doi: 10.1016/j.biombioe.2016.03.020
[79]	Özçelık R, Diamantopoulou M J, Eker M, et al. Artificial neural network models: an alternative approach for reliable aboveground pine tree biomass prediction[J]. Forest Science, 2017, 63(3): 291−302. doi: 10.5849/FS-16-006
[80]	Wu C Y, Chen Y F, Peng C H, et al. Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change[J]. Journal of Environmental Management, 2019, 234: 167−179.
[81]	徐奇刚, 雷相东, 国红, 等. 基于多层感知机的长白落叶松人工林林分生物量模型[J]. 北京林业大学学报, 2019, 41(5):97−107. Xu Q G, Lei X D, Guo H, et al. Stand biomass model of Larix olgensis plantations based on multi-layer perceptron networks[J]. Journal of Beijing Forestry University, 2019, 41(5): 97−107.
[82]	Hlásny T, Trombik J, Bošeľa M, et al. Climatic drivers of forest productivity in Central Europe[J]. Agricultural and Forest Meteorology, 2017, 234−235: 258−273. doi: 10.1016/j.agrformet.2016.12.024
[83]	Lima M B D O, Junior I M L, Oliveira E M, et al. Artificial neural networks in whole-stand level modeling of Eucalyptus plants[J]. African Journal of Agricultural Research, 2017, 12(7): 524−534. doi: 10.5897/AJAR2016.12068
[84]	Yousefpoor M, Shahraji T R, Eslam B A, et al. The use of artificial neural network to evaluate the effects of human and physiographic factors on forest stock volume[J]. Journal of Applied Sciences and Environmental Management, 2016, 20(4): 1017−1024.
[85]	林卓, 吴承祯, 洪伟, 等. 基于BP神经网络和支持向量机的杉木人工林收获模型研究[J]. 北京林业大学学报, 2015, 37(1):42−54. Lin Z, Wu C Z, Hong W, et al. Yield model of Cunninghamia lanceolata plantation based on back propagation neural network and support vector machine[J]. Journal of Beijing Forestry University, 2015, 37(1): 42−54.
[86]	Tavares J I D S, da Rocha J E C, Ebling Â A, et al. Artificial neural networks and linear regression reduce sample intensity to predict the commercial volume of Eucalyptus Clones[J]. Forests, 2019, 10(3): 268. doi: 10.3390/f10030268
[87]	刘鑫, 王海燕, 雷相东, 等. 基于BP神经网络的天然云冷杉针阔混交林标准树高−胸径模型[J]. 林业科学研究, 2017, 30(3):368−375. Liu X, Wang H Y, Lei X D, et al. Generalized height-diameter model for natural mixed spruce-fir coniferous and broadleaf forests based on BP neural network[J]. Forest Research, 2017, 30(3): 368−375.
[88]	Diamantopoulou M J, Özçelik R, Crecente-Campo F, et al. Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods[J]. Biosystems Engineering, 2015, 133: 33−45. doi: 10.1016/j.biosystemseng.2015.02.013
[89]	薛薇. R语言数据挖掘方法及应用[M]. 北京: 电子工业出版社, 2016. Xue W. Data Mining method with R language and its application[M]. Beijing: Publishing House of Electronics Industry, 2016.
[90]	Che S H, Tan X H, Xiang C W, et al. Stand basal area modelling for Chinese fir plantations using an artificial neural network model[J]. Journal of Forestry Research, 2019, 30(5): 1641−1649. doi: 10.1007/s11676-018-0711-9
[91]	Maltamo M, Kangas A. Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution[J]. Canadian Journal of Forest Research, 1998, 28(8): 1107−1115. doi: 10.1139/x98-085
[92]	Lantz B. 机器学习与R语言[M]. 李洪成, 许金炜, 李舰, 译. 北京: 机械工业出版社, 2017. Lantz B. Machine learning with R[M]. Li H C, Xu J W, Li J, trans. Beijing: China Machine Press, 2017.
[93]	Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction[M]. 2nd ed. New York: Springer, 2009.
[94]	Lantz B. Machine Learning with R[M]. Birmingham: Packt Publishing, 2013.
[95]	Ridgeway G. Generalized boosted models: a guide to the GBM package[Z/OL]. [2019−10−13]. https://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf.
[96]	Friedman J H. Stochastic gradient boosting[J]. Computational Statistics and Data Analysis, 2002, 38(4): 367−378. doi: 10.1016/S0167-9473(01)00065-2
[97]	Thessen A E. Adoption of machine learning techniques in ecology and earth science[J/OL]. PeerJ PrePrints, 2016, 4: e1720v1 [2019−05−06]. https://peerj.com/preprints/1720.pdf.
[98]	Fielding A H. Cluster and classification techniques for the biosciences[M]. London: Cambridge University Press, 2006.
[99]	Corona-Núñez R O, Mendoza-Ponce A, López-Martínez R. Model selection changes the spatial heterogeneity and total potential carbon in a tropical dry forest[J]. Forest Ecology and Management, 2017, 405: 69−80. doi: 10.1016/j.foreco.2017.09.018
[100]	Temesgen H, Ver Hoef J M. Evaluation of the spatial linear model, random forest and gradient nearest-neighbour methods for imputing potential productivity and biomass of the Pacific Northwest forests[J]. Forestry, 2015, 88(1): 131−142. doi: 10.1093/forestry/cpu036
[101]	Jevšenak J, Levanič T. Should artificial neural networks replace linear models in tree ring based climate reconstructions?[J]. Dendrochronologia, 2016, 40: 102−109. doi: 10.1016/j.dendro.2016.08.002
[102]	Görgens E B, Montaghi A, Rodriguez L C E. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics[J]. Computers and Electronics in Agriculture, 2015, 116: 221−227. doi: 10.1016/j.compag.2015.07.004
[103]	Wang Y H, Raulier F, Ung C H. Evaluation of spatial predictions of site index obtained by parametric and nonparametric methods: a case study of lodgepole pine productivity[J]. Forest Ecology and Management, 2005, 214(1/3): 201−211.
[104]	高若楠, 谢阳生, 雷相东, 等. 基于随机森林模型的天然林立地生产力预测研究[J]. 中南林业科技大学学报, 2019, 39(4):39−46. Gao R N, Xie Y S, Lei X D, et al. Study on prediction of natural forest productivity based on random forest model[J]. Journal of Central South University of Forestry and Technology, 2019, 39(4): 39−46.
[105]	Zhang H, Wang K L, Zeng Z X, et al. Large-scale patterns in forest growth rates are mainly driven by climatic variables and stand characteristics[J]. Forest Ecology and Management, 2019, 435: 120−127. doi: 10.1016/j.foreco.2018.12.054
[106]	Guan H Y, Yu Y T, Ji Z, et al. Deep learning-based tree classification using mobile LiDAR data[J]. Remote Sensing Letters, 2015, 6(11): 864−873. doi: 10.1080/2150704X.2015.1088668
[107]	Sun Y, Liu Y, Wang G, et al. Deep learning for plant identification in natural environment[J/OL]. Computational intelligence and neuroscience, 2017, 2017: Article ID 7361042 [2019−05−18]. https://www.hindawi.com/journals/cin/2017/7361042/.
[108]	Pearline S A, Kumar V S, Harini S. A study on plant recognition using conventional image processing and deep learning approaches[J]. Journal of Intelligent and Fuzzy Systems, 2019, 36(3): 1997−2004. doi: 10.3233/JIFS-169911
[109]	Wang G, Sun Y, Wang J X. Automatic image-based plant disease severity estimation using deep learning[J/OL]. Computational intelligence and neuroscience, 2017, 2017: Article ID 2917536 [2019−05−16]. https://www.hindawi.com/journals/cin/2017/2917536/.
[110]	Asner G P, Brodrick P G, Philipson C, et al. Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo[J]. Biological Conservation, 2018, 217: 289−310. doi: 10.1016/j.biocon.2017.10.020

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Applications of machine learning algorithms in forest growth and yield prediction

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