摘要
森林蓄积量的研究对了解森林生态系统、林分生产力、森林生物量具有重要意义,探究影响4种树种(组)林分蓄积量变化的因子,为后期建立天然林生长模型构建提供理论支撑。以福建省最新一次的森林资源连续清查中的天然马尾松、阔叶林树种(组)、针阔混交树种(组)、针叶混交树种(组)的蓄积量为研究对象,气象、地貌等环境因子为自变量,利用决策树回归、随机森林回归、adaboost回归、梯度提升树回归(GBDT)、CatBoost回归、ExtraTrees回归、XGBoost回归、LightGBM回归方法分析环境因子对4种天然林树种(组)蓄积量的影响情况开展探讨。结果表明:梯度提升树回归(GBDT)能较好地拟合各环境因子与4种树种(组)蓄积量的关系,4种树种(组)蓄积量R2均为0.999,MSE、RMSE、MAE、MAPE均在0.1范围内;林分年龄与蓄积量的密切关系,重要性达0.50以上;气象和地貌因子对4种树种(组)蓄积量的重要性存在差异,建议在具体建模过程中应进行剥离分析。
The study of forest volume is of great significance for understanding forest ecosystems,stand productivity,and forest biomass.It explores the factors that affect the changes in forest volume of four tree species(groups)and provides theoretical support for establishing natural forest growth models in the future.The study focuses on the stock volume of natural Pinus massoniana,broad-leaved forest species(groups),mixed coniferous and broad-leaved tree species(groups),and mixed coniferous tree species(groups)in the latest continuous inventory of forest resources in Fujian Province.Environmental factors such as meteorology and geomorphology are used as independent variables,and decision tree regression,random forest regression,adaboost regression,gradient lifting tree regression(GBDT),CatBoost regression,ExtraTrees regression,XGBoost regression are used The LightGBM regression method is used to analyze the impact of environmental factors on the stock volume of four natural forest tree species(groups).The results showed that Gradient Ascending Tree Regression(GBDT)could better fit the relationship between environmental factors and the stock volume of four tree species(groups).The stock volume R2 of all four tree species(groups)was 0.999,and MSE,RMSE,MAE,and MAPE were all within the range of 0.1;The close relationship between stand age and stock volume,with an importance of over 0.50;There are differences in the importance of meteorological and geomorphological factors on the accumulation of four tree species(groups),and it is recommended to conduct stripping analysis during the specific modeling process.
作者
廖佩莹
王雅楠
丘甜
华伟平
郑士超
周艳
饶贵川
LIAO Peiying;WANG Yanan;QIU Tian;HUA Weiping;ZHENG Shichao;ZHOU Yan;RAO Guichuan(School of Ecology and Resource Engineering;School of Business,Wuyi University,Wuyishan,Fujian 354300;School of Forestry,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002;Research Monitoring Center,Wuyishan National Park,Beijing 354399)
出处
《武夷学院学报》
2024年第3期20-26,共7页
Journal of Wuyi University
基金
福建省自然科学基金项目(2021J011141)
南平市资源化学产业科技创新联合项目(N2021Z010)
福建省哲学社会科学规划项目成果(FJ2022X019)
大学生创新训练项目(S202210397048)。
关键词
林分蓄积量
环境因子
林分年龄
机器学习
stand volume
environmental factors
stand age
machine learning
