Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm

Ou Qiangxin; Lei Xiangdong; Shen Chenchen; Song Guotao

doi:10.13332/j.1000-1522.20180266

Journal of Beijing Forestry University > 2019 > 41(9): 9-19. > DOI: 10.13332/j.1000-1522.20180266

Ou Qiangxin, Lei Xiangdong, Shen Chenchen, Song Guotao. 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. DOI: 10.13332/j.1000-1522.20180266

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Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm

1.
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2.
Xinyuan Wooden Industry Co., Changbai Mountian Forest Industry Group, Dunhua, 133714, Jilin, China

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Received Date: August 14, 2018
Revised Date: January 11, 2019
Available Online: August 25, 2019
Published Date: August 31, 2019

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Abstract

Abstract

Objective Individual tree growth can be controlled by many factors, such as climate, competition, stand factor and so on. It is necessary to clarify the dominant factors affecting tree growth from climate and stand variables with appropriate methods. Machine learning methods, such as random forest and so on, provide a new way. Testing the reliability of random forest algorithm in analyzing the effects of stand factors and climate on individual tree growth is necessary. The algorithm is expected to provide a new method for forest growth and yield prediction.
Method Long-term continuous monitoring data of larch-spruce-fir sample plots repeatedly measured for 25 years (1986−2010) in Wangqing Forest Bureau of Jilin Province, northeastern China were used. Random forest algorithm was used to build individual tree radial growth model with 52 candidate independent variables as competition, stand factor and climate. The effects of climate and stand factors on individual tree radial growth were analyzed. More concretely, 52 random forest models were built based on 52 hyperparametric combinations (ntree = 1 000 and mtry = {1, 2, 3, ···, 52}). And 10-fold cross validation was used to train and evaluate these models. The relative importance and partial dependence of independent variables affecting individual tree radial growth were analyzed based on the full data set and the optimal random forest model.
Result The random forest model with ntree = 1 000 and mtry = 12 had the best generalization ability among all 52 random forest models. This model had the maximal determination coefficient of cross validation (R²_cv, R²_cv = 0.54), the minimal root mean square error of cross validation (RMSE_cv), mean absolute deviation of cross validation (MAE_cv) and relative root mean square error of cross validation (rRMSE_cv) (RMSE_cv = 0.14 cm, MAE_cv = 0.10 cm and rRMSE_cv = 50%). Individual tree radial growth was affected mostly by stand factors with the relative importance over 80.00%. What’s more, among the 9 stand factors, the sum of basal area larger than the subject tree (BAL) had the highest impact on individual tree radial growth, the number of trees per hectare (N) had the lowest impact, and the impact of other 5 factors were in between. Besides, individual tree radial growth decreased with the increase of BAL, basal area per hectare (BA), N and average DBH (D_g). On the other hand, individual tree radial growth increased with the increase of the ratio of DBH of subject tree to average DBH (RD), DBH at the beginning of the period (D₀) and the ratio of DBH of subject tree to the maximal DBH (DDM). In addition, individual tree radial growth was less affected by climatic factors, with the relative importance was below 20.00%. What’s more, all the 44 climatic variables had little impact on individual tree radial growth (the relative importance < 1%). And the ratio of average precipitation in growing season (April−September) to annual precipitation (Pratio), the total annual solar radiation (Asr), the ratio of average precipitation in growing season (April−September) to the relative humidity in growing season (April−September) (Gspgsrh) and solar radiation duration in growing season (April−September) (Gssr) were top four relatively important climate variables.
Conclusion Random forest model can be used to reasonably analyze the complex relationship between each predicted variables and individual tree radial growth. Individual tree radial growth is mostly affected by stand factors, but less affected by climatic factors. In general, the climatic factors had very limited ability in explaining the variation of individual tree radial growth at local scale, while the stand factors, such as competition and stand factor, are the main drivers to individual tree radial growth. Random forest models have good performance in model generalization and accuracy. Both the variable importance and partial dependence plots have reasonable forestry interpretation.
- stand factor,
- climate,
- random forest,
- individual tree DBH growth

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References (27)

References

[1]	Jiang X Y, Huang J G, Cheng J, et al. Interspecific variation in growth responses to tree size, competition and climate of western Canadian boreal mixed forests[J]. Science of the Total Environment, 2018, 631−632: 1070−1078. doi: 10.1016/j.scitotenv.2018.03.099
[2]	Clark J S, Bell D M, Kwit M C, et al. Competition-interaction landscapes for the joint response of forests to climate change[J]. Global Change Biology, 2014, 20(6): 1979−1991. doi: 10.1111/gcb.12425
[3]	Rollinson C R, Kaye M W, Canham C D. Interspecific variation in growth responses to climate and competition of five eastern tree species[J]. Ecology, 2016, 97(4): 1003−1011.
[4]	Matsushita M, Takata K, Hitsuma G, et al. A novel growth model evaluating age-size effect on long-term trends in tree growth[J]. Functional Ecology, 2015, 29(10): 1250−1259. doi: 10.1111/1365-2435.12416
[5]	Martínez-Vilalta J, López B C, Loepfe L, et al. Stand- and tree-level determinants of the drought response of Scots pine radial growth[J]. Oecologia, 2012, 168(3): 877−888. doi: 10.1007/s00442-011-2132-8
[6]	Messaoud Y, Chen H Y H. The influence of recent climate change on tree height growth differs with species and spatial environment[J]. Plos One, 2011, 6(2): e14691. doi: 10.1371/journal.pone.0014691
[7]	Subedi N, Sharma M. Climate-diameter growth relationships of black spruce and jack pine trees in boreal Ontario, Canada[J]. Global Change Biology, 2013, 19(2): 505−516. doi: 10.1111/gcb.12033
[8]	Castagneri D, Nola P, Motta R, et al. Summer climate variability over the last 250 years differently affected tree species radial growth in a mesic Fagus-Abies-Picea old-growth forest[J]. Forest Ecology and Management, 2014, 320: 21−29. doi: 10.1016/j.foreco.2014.02.023
[9]	Chen L, Huang J G, Stadt K J, et al. Drought explains variation in the radial growth of white spruce in western Canada[J]. Agricultural and Forest Meteorology, 2017, 233: 133−142. doi: 10.1016/j.agrformet.2016.11.012
[10]	Cortini F, Filipescu C N, Groot A, et al. Regional models of diameter as a function of individual tree attributes, climate and site characteristics for six major tree species in Alberta, Canada[J]. Forests, 2011, 2(4): 814−831. doi: 10.3390/f2040814
[11]	Latreille A, Davi H, Huard F, et al. Variability of the climate-radial growth relationship among Abies alba trees and populations along altitudinal gradients[J]. Forest Ecology and Management, 2017, 396: 150−159. doi: 10.1016/j.foreco.2017.04.012
[12]	Toledo M, Poorter L, Peña-Claros M, et al. Climate is a stronger driver of tree and forest growth rates than soil and disturbance[J]. Journal of Ecology, 2011, 99(1): 254−264. doi: 10.1111/j.1365-2745.2010.01741.x
[13]	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
[14]	Breiman L, Friedman J H, Stone C J, et al. Classification and regression trees[M]. London: Chapman and Hall, 1984.
[15]	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
[16]	Kuhn M, Johnson K. Applied predictive modeling[M]. New York: Springer, 2013.
[17]	Breiman L. Random forest[J]. Machine Learning, 2001, 45(1): 5−32. doi: 10.1023/A:1010933404324
[18]	高若楠, 苏喜友, 谢阳生, 等. 基于随机森林的杉木适生性预测研究[J]. 北京林业大学学报, 2017, 39(12):36−43. Gao R N, Su X Y, Xie Y S, et al. Prediction of adaptability of Cunninghamia lanceolata based on random forest[J]. Journal of Beijing Forestry University, 2017, 39(12): 36−43.
[19]	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.
[20]	余黎, 雷相东, 王雅志, 等. 基于广义可加模型的气候对单木胸径生长的影响研究[J]. 北京林业大学学报, 2014, 36(5):22−32. Yu L, Lei X D, Wang Y Z, et al. Impact of climate on individual tree radial growth based on generalized additive model[J]. Journal of Beijing Forestry University, 2014, 36(5): 22−32.
[21]	沈琛琛. 气候敏感的长白落叶松立地指数模型研究[D]. 北京: 中国林业科学研究院, 2012. Shen C C. Climate-sensitive site index model of Larix olgensis Henry[D]. Beijing: Chinese Academy of Forestry, 2012.
[22]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016. Zhou Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016.
[23]	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
[24]	张瑞英, 庞勇, 李增元, 等. 结合机载LiDAR和LANDSAT ETM+数据的温带森林郁闭度估测[J]. 植物生态学报, 2016, 40(2):102−115. doi: 10.17521/cjpe.2014.0366 Zhang R Y, Pang Y, Li Z Y, et al. Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data[J]. Chinese Journal of Plant Ecology, 2016, 40(2): 102−115. doi: 10.17521/cjpe.2014.0366
[25]	Breiman L. Statistical modeling: the two cultures[J]. Statistical Science, 2001, 16(3): 199−231.
[26]	Alam S A, Huang J G, Stadt K J, et al. Effects of competition, drought stress and photosynthetic productivity on the radial growth of white spruce in western Canada[J/OL]. Frontiers in Plant Science, 2017, 8: 1−15 [2018−03−15]. https://www.frontiersin.org/articles/10.3389/fpls.2017.01915/full.
[27]	孟宪宇. 测树学[M]. 3版. 北京: 中国林业出版社, 2006. Meng X Y. Forest mensuration edition)[M]. 3rd ed. Beijing: China Forestry Publishing House, 2006.

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Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm