基于随机森林的兴安落叶松天然林单木年龄预估模型  

Age estimation model for individual tree in natural Larix gmelinii forest based on random forest model

作　　者：王晓楠 苏文浩 董灵波[1] WANG Xiaonan;SU Wenhao;DONG Lingbo(Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management,School of Forestry,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学林学院,森林生态系统可持续经营教育部重点实验室,哈尔滨150040

出　　处：《应用生态学报》2024年第4期1055-1063,共9页Chinese Journal of Applied Ecology

基　　金：“十四五”国家重点研发计划项目(2022YFD2200502);中央高校基本科研业务费(2572022CG07);黑龙江省头雁创新团队计划项目(森林资源高效培育技术研发团队)资助。

摘　　要：为准确预估天然兴安落叶松单木的年龄,实现大兴安岭地区兴安落叶松的全周期可持续经营,本研究基于大兴安岭地区盘古林场44块固定样地数据和280个标准木树芯,采用逐步回归和随机森林算法构建单木年龄预测模型,分析林分结构、立地条件和竞争指标等因素对年龄预测精度的影响,采用决定系数(R^(2))、均方根误差(RMSE)和平均绝对误差(MAE)对模型进行评价和检验。结果表明:当决策树的数量为1500、节点竞争变量数目为8时,随机森林模型的预测精度最高,据此建立的单木年龄随机森林模型相比逐步回归模型具有更好的准确性和预测能力,其R^(2)、RMSE和MAE分别为0.5882、9.9259 a、8.1155 a;胸径是影响年龄预测最重要的指标(83.8%),其次为树高(34.4%)、海拔(17.9%)和每公顷断面积(17.5%)。随机森林算法在兴安落叶松天然林的单木年龄预测模型构建中,具有较好的适应性和建模效果。本研究结果有助于提高兴安落叶松生长与收获的预估精度,并可为其他与林龄相关的科学研究提供参考。To accurately estimate the age of individual tree and to achieve full-cycle sustainable management of natural Larix gmelini forest in Great Xing'an Mountains of northeastern China,we constructed individual tree age prediction model using stepwise regression and random forest algorithms based on 44 fixed plots data and 280 standard tree cores obtained from the Pangu Forest Farm.We analyzed the influence of stand structure,site conditions,and competition index on the accuracy of model prediction.The model was evaluated by the coefficient of determination(R^(2)),root mean square error(RMSE),and mean absolute error(MAE).The results showed that the random forest model had the highest prediction accuracy when number of decision trees was 1500 and number of node contention variables was 8.The random forest model had better accuracy and prediction ability than the stepwise regression model,with R^(2),RMSE and MAE of 0.5882,9.9259 a,8.1155 a.Diameter at breast height was the most important factor affecting age prediction(83.8%),followed by tree height(34.4%),elevation(17.9%),and basal area per hectare(17.5%).The random forest algorithm exhibited better adaptability and modeling effect on constructing a predictive model for individual tree age.This research contributed to improving the accuracy of growth and harvest estimation for L.gmelinii,and could provide a reference for other scientific studies related to tree age estimation in forests.

关 键 词：兴安落叶松 年龄预测 逐步回归 机器学习 

分 类 号：S791.222[农业科学—林木遗传育种]