深度学习和传统方法模拟杉木树高-胸径模型比较  

Comparison of Deep Learning and Traditional Models to Simulate the Height-DBH Relationship of Chinese Fir

作　　者：梁瑞婷 孙玉军[1] 李芸 LIANG Rui-ting;SUN Yu-jun;LI Yun(National Forestry&Grassland Administration Key Laboratory of Forest Resources&Environmental Management,Beijing Forestry University,Beijing 100083,China)

机构地区：[1]北京林业大学森林资源和环境管理国家林业和草原局重点开放性实验室,北京100083

出　　处：《林业科学研究》2021年第6期65-72,共8页Forest Research

基　　金：国家自然科学基金“基于树木生长过程的长白落叶松树冠模型”(No.31870620);林业科学技术推广项目“基于分水岭算法的森林植被碳储量监测技术成果推广应用”([2019]06)。

摘　　要：[目的]基于深度学习算法,建立多隐藏层的杉木树高-胸径神经网络模型,探索一种更高效低偏的树高模型研建方法,提高杉木树高的预测精度。[方法]利用福建省将乐国有林场34块杉木样地的2898组树高-胸径调查数据,基于传统回归建立10个广义树高-胸径模型,筛选出精度最高的模型作为对照。同时基于H2O平台的深度学习算法,建立70个不同结构的树高-胸径DLA模型,通过分析比较,确定最适宜预测杉木树高的模型结构,与传统最优模型进行比较。[结果]建立的树高-胸径DLA模型均能较好地描述杉木的树高-胸径间关系,R^(2)都在0.84以上,大于最优传统模型,RMSE和MAE小于传统模型。精度最高的DLA模型结构包含6个隐藏层,每层各340个神经元。[结论]本研究基于深度学习建立的杉木树高-胸径DLA模型,其拟合精度与预测精度略高于传统的广义树高-胸径模型,尤其在预测较高的林木时,更为明显,能够用于研究区杉木树高的预测。[Objective]To explore a more efficient and low-biased tree height prediction method,improve the prediction accuracy of tree height,and to establish a multi-hidden layer neural network model of height-diameter is based on deep learning algorithm.[Method]Using a set of 2898 groups of tree height and diameter data from 34 Chinese Fir(Cunninghamia lanceolata)sample plots in Jiangle National Forest Farm of Fujian Province,10 generalized height-diameter models were established based on traditional regression,and the model with the highest accuracy was selected to compare.At the same time,based on the deep learning algorithm of the H2O platform,70 DLA models with different structures of tree height-diameter at breast height were established.Through analysis and comparison,the most suitable model structure was determined and compared with the traditional optimal model.[Result]The different height-diameter DLA models can describe the relationship between height and diameter of Chinese Fir well,whose R^(2) is above 0.84,which is higher than that of the best traditional model,and the RMSE and MAE are smaller than that of the traditional model.The most accurate DLA model structure contains 6 hidden layers,each with 340 neurons.[Conclusion]The height-diameter DLA model established based on deep learning has higher fitting accuracy and prediction accuracy than the traditional models,especially when predicting higher trees.It can be used to predict the height of Chinese Fir in study area.

关 键 词：深度学习 树高-胸径模型 非线性回归 杉木 

分 类 号：S758.5[农业科学—森林经理学]