基于多层感知机的长白落叶松人工林林分生物量模型
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摘要
【目的】神经网络模型能避免林分生物量模型建模时自变量共线性与异方差问题,研究多层感知机在林分生物量模型中的应用,为森林经营单位、区域生物量和碳储量的估算提供方法和依据。【方法】以长白落叶松人工林为研究对象,利用吉林省一类清查固定样地的917组数据,分别建立了基于传统的对数转化后线性模型和神经网络多层感知机的地上生物量和总生物量模型。使用AIC、决定系数(R~2)、均方根误差(RMSE)、相对均方根误差(RMSEr)和平均绝对误差(MAE)来评价模型。【结果】估计精度最高的模型是输入单元为林分平均胸径(D)-平均高(H)-林分密度指数(S)-海拔(HB)-坡向(PX)-坡位(PW)、2个隐藏层、隐单元数为40-20的神经网络模型,与传统对数转换线性回归模型相比,地上生物量和总生物量模型的调整决定系数(Adj.R~2)分别从0.902 1提高到了0.914 1,从0.897 9提高到了0.908 9;RMSEr分别从6.330 5%降低到了5.992 2%,从6.490 1%降低到了6.153 6%。包含立地因子的神经网络模型比未包含立地因子的神经网络模型估计精度略有提升,地上生物量与总生物量的Adj.R~2分别提高了0.88%和0.99%,RMSEr分别降低了5.33%和5.46%。【结论】多层感知机生物量模型的估计精度比传统回归模型略有提高,但它可以避免模型选型和违背传统统计假设的处理等问题,且能够一次性计算地上生物量和总生物量模型,有一定优势。
[Objective] Neural network model can avoid the collinearity and heteroscedasticity of variables in modeling forest stand biomass. This paper aims to apply multi-layer perceptron networks to forest biomass model to provide methods for the calculation and prediction of forest biomass and carbon stocks at forest management unit and regional levels. [Method] Based on 917 observations from the sample plots of larch plantations from national forest inventory in Jilin Province of northeastern China, the aboveground and total biomass models by log-transformed linear regression, and multi-layer perceptron networks with and without site factors were established. AIC,adjusted R~2,RMSE, RMSEr and MAE were used to evaluate the models. [Result] The model with the highest prediction accuracy was the neural network one with the input unit quadratic mean diameters(D), stand mean height(H), stand density index(S), altitude(HB), slope(PD),slope aspect(PX), slope position(PW), two hidden layers and hidden unit number of 40-20. Compared with traditional log-transformed linear regression model, the adjusted R~2 of the aboveground and total biomass models was increased from 0.902 1 to 0.914 1, and from 0.897 9 to 0.908 9, RMSEr was decreased from6.330 5% to 5.992 2%, and from 6.490 1% to 6.153 6%, respectively. The neural network model with site factors had slightly higher estimation accuracy than that without site factors. The adjusted R~2 of the aboveground and total biomass models was increased by 0.88% and 0.99%, and RMSEr decreased by 5.33%and 5.46%, respectively. [Conclusion] Biomass model based on multi-layer perceptron networks had similar performance in terms of model accuracy, but it could avoid treating the traditional assumptions such as the collinearity and heteroscedasticity of variables, and had the ability to calculate aboveground and total biomass models at one time.
[Objective] Neural network model can avoid the collinearity and heteroscedasticity of variables in modeling forest stand biomass. This paper aims to apply multi-layer perceptron networks to forest biomass model to provide methods for the calculation and prediction of forest biomass and carbon stocks at forest management unit and regional levels. [Method] Based on 917 observations from the sample plots of larch plantations from national forest inventory in Jilin Province of northeastern China, the aboveground and total biomass models by log-transformed linear regression, and multi-layer perceptron networks with and without site factors were established. AIC,adjusted R~2,RMSE, RMSEr and MAE were used to evaluate the models. [Result] The model with the highest prediction accuracy was the neural network one with the input unit quadratic mean diameters(D), stand mean height(H), stand density index(S), altitude(HB), slope(PD),slope aspect(PX), slope position(PW), two hidden layers and hidden unit number of 40-20. Compared with traditional log-transformed linear regression model, the adjusted R~2 of the aboveground and total biomass models was increased from 0.902 1 to 0.914 1, and from 0.897 9 to 0.908 9, RMSEr was decreased from6.330 5% to 5.992 2%, and from 6.490 1% to 6.153 6%, respectively. The neural network model with site factors had slightly higher estimation accuracy than that without site factors. The adjusted R~2 of the aboveground and total biomass models was increased by 0.88% and 0.99%, and RMSEr decreased by 5.33%and 5.46%, respectively. [Conclusion] Biomass model based on multi-layer perceptron networks had similar performance in terms of model accuracy, but it could avoid treating the traditional assumptions such as the collinearity and heteroscedasticity of variables, and had the ability to calculate aboveground and total biomass models at one time.
引文
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[16]国家林业与草原局.立木生物量模型及碳计量参数落叶松[S].北京:中国标准出版社,2016.National Forestry and Grassland Administration of the People’s Republic of China.Tree biomass models and related parameters to carbon accounting for Larix[S].Beijing:Standards Press of China,2016.
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[20]Xiao X,White E P,Hooten M B,et al.On the use of logtransformation vs.nonlinear regression for analyzing biological power laws[J].Ecology,2011,92(10):1887-1894.
[21]符利勇,雷渊才,孙伟,等.不同林分起源的相容性生物量模型构建[J].生态学报,2014,34(6):1464-1470.Fu L Y,Lei Y C,Sun W,el al.Development of compatible biomass models for trees from different stand origin[J].Acta Ecologica Sinica,2014,34(6):1464-1470.
[22]?z?elik R,Diamantopoulou M J,Brooks J R,et al.Estimating tree bole volume using artificial neural network models for four species in Turkey[J].Journal of Environmental Management,2010,91(3):742-753.
[2]雷相东,张会儒,牟惠生.东北过伐林区蒙古栎林分相容性生物量模型研究[J].第四纪研究,2010,30(3):559-565.Lei X D,Zhang H R,Mu H S.Compatible stand biomass models of Mongolia oak forests in over logged forest regions,Northeast China[J].Quaternary Sciences,2010,30(3):559-565.
[3]程堂仁,冯菁,马钦彦,等.基于森林资源清查资料的林分生物量相容性线性模型[J].北京林业大学学报,2007,29(5):110-113.Cheng T R,Feng J,Ma Q Y,et al.Linear models with compatibility of stand biomass based on the forest resource inventory data[J].Journal of Beijing Forestry University,2007,29(5):110-113.
[4]董利虎,李凤日.大兴安岭东部主要林分类型乔木层生物量估算模型[J].应用生态学报,2018,29(9):2825-2834.Dong L H,Li F R.Stand-level biomass estimation models for the tree layer of main forest types in East Daxing’an Mountains,China.[J].Chinese Journal of Applied Ecology,2018,29(9):2825-2834.
[5]欧光龙,胥辉,王俊峰,等.思茅松天然林林分生物量混合效应模型构建[J].北京林业大学学报,2015,37(3):101-110.Ou G L,Xu H,Wang J F,et al.Building mixed effect models of stand biomass for Simao pine(Pinus kesiya var.langbianensis)natural forest[J].Journal of Beijing Forestry University,2015,37(3):101-110.
[6]赵嘉诚,李海奎.杉木单木和林分水平地下生物量模型的构建[J].林业科学,2018,54(2):81-89.Zhao J C,Li H K.Establishment of below-ground biomass equations for Chinese fir at tree and stand level[J].Scientia Silvae Sinicae,2018,54(2):81-89.
[7]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.
[8]Sileshi G W.A critical review of forest biomass estimation models,common mistakes and corrective measures[J].Forest Ecology and Management,2014,329:237-254.
[9]董利虎.东北林区主要树种及林分类型生物量模型研究[D].哈尔滨:东北林业大学,2015.Dong L H.Developing individual and stand-level biomass equations in Northeast China forest area[D].Harbin:Northeast Forest University,2015.
[10]Nandy S,Singh R,Ghosh S,et al.Neural network-based modelling for forest biomass assessment[J].Carbon Management,2017,8(4):305-317.
[11]Stas S M,Rutishauser E,Chave J,et al.Estimating the aboveground biomass in an old secondary forest on limestone in the Moluccas,Indonesia:comparing locally developed versus existing allometric models[J].Forest ecology and management,2017,389:27-34.
[12]Ercanl??,GünlüA,?enyurt M,et al.Artificial neural network models predicting the leaf area index:a case study in pure evenaged Crimean pine forests from Turkey[J].Forest Ecosystems,2018,5(1):29.
[13]?z?elik 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.
[14]王轶夫,孙玉军,郭孝玉.基于BP神经网络的马尾松立木生物量模型研究[J].北京林业大学学报,2013,35(2):17-21.Wang Y F,Sun Y J,Guo X Y.Single-tree biomass modeling of Pinus massoniana based on BP neural network[J].Journal of Beijing Forestry University,2013,35(2):17-21.
[15]解雅麟,王海燕,雷相东.基于3-PG模型的长白落叶松人工林生长和生物量模拟[J].南京林业大学学报(自然科学版),2018,42(1):141-148.Xie Y L,Wang H Y,Lei X D.Growth and biomass simulation of Larix olgensis plantations based on 3-PG model[J].Journal of Nanjing Forestry University(Natural Science Edition),2018,42(1):141-148.
[16]国家林业与草原局.立木生物量模型及碳计量参数落叶松[S].北京:中国标准出版社,2016.National Forestry and Grassland Administration of the People’s Republic of China.Tree biomass models and related parameters to carbon accounting for Larix[S].Beijing:Standards Press of China,2016.
[17]Goodfellow I,Bengio Y,Courville A,et al.Deep learning[M].Cambridge:MIT Press,2016.
[18]Uzun H,Y?ld?z Z,Goldfarb J L,et al.Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis[J].Bioresource Technology,2017,234:122-130.
[19]Tieleman T,Hinton G.Lecture 6.5-rmsprop:divide the gradient by a running average of its recent magnitude[J].COURSERA:Neural Networks for Machine Learning,2012,4(2):26-31.
[20]Xiao X,White E P,Hooten M B,et al.On the use of logtransformation vs.nonlinear regression for analyzing biological power laws[J].Ecology,2011,92(10):1887-1894.
[21]符利勇,雷渊才,孙伟,等.不同林分起源的相容性生物量模型构建[J].生态学报,2014,34(6):1464-1470.Fu L Y,Lei Y C,Sun W,el al.Development of compatible biomass models for trees from different stand origin[J].Acta Ecologica Sinica,2014,34(6):1464-1470.
[22]?z?elik R,Diamantopoulou M J,Brooks J R,et al.Estimating tree bole volume using artificial neural network models for four species in Turkey[J].Journal of Environmental Management,2010,91(3):742-753.