大兴安岭地区不同林分类型的广义加性生物量模型构建  

Construction of generalized additive biomass model of different typical stand types in the Greater Khingan mountains region

作　　者：赵阳[1,2] 贾炜玮[1,2] 李凤日[1,2] 李泽霖[1,2] 郭昊天 王帆 赵子鹏 ZHAO Yang;JIA Weiwei;LI Fengri;LI Zelin;GUO Haotian;WANG Fan;ZHAO Zipeng(College of Forestry,Ministry of Education,Northeast Forestry University,Harbin 150040,Heilongjiang,China;Key Laboratory of Sustainable Management of Forest Ecosystem,Ministry of Education,Northeast Forestry University,Harbin 150040,Heilongjiang,China)

机构地区：[1]东北林业大学林学院,黑龙江哈尔滨150040 [2]东北林业大学森林生态系统可持续经营教育部重点实验室,黑龙江哈尔滨150040

出　　处：《中南林业科技大学学报》2025年第4期52-64,共13页Journal of Central South University of Forestry & Technology

基　　金：国家重点研发计划子课题(2022YFD2201003-02);黑龙江省双一流学科协同创新成果项目(Zxkxt220100001)。

摘　　要：【目的】森林是最重要的自然资源之一,了解各因子对森林生物量的影响对优化森林空间结构和经营管理具有重要意义。对不同林分类型构建生物量模型,有助于为恢复和保护森林生态系统提供科学依据。【方法】以黑龙江省大兴安岭地区的7种典型林分类型为研究对象,利用2015年1 157块监测样地数据,结合Sentinel-2卫星影像和数字高程模型(DEM)数据,计算植被指数、纹理特征、坡度等变量。将遥感数据与实地调查数据、气候数据相结合,建立广义最小二乘生物量模型(GLS)和广义加性生物量模型(GAM)。采用十折交叉验证法,通过计算均方根误差(RMSE)、均方误差(MSE)和平均绝对误差(MAE)等评价指标对模型进行检验,并使用2020年复测的328块样地数据对模型进行辅助验证。【结果】在7种典型林分类型下,GAM的拟合效果优于GLS。其中,广义加性生物量模型的MAE较最小二乘生物量模型降低了1.99%~27.48%,RMSE降低了4.29%~20.87%,MSE降低了6.72%~35.43%。二次检验结果表明,每种林分类型的GAM预测精度均在80%以上。【结论】广义加性生物量模型是一种建立生物量模型的非参数方法,适用于大兴安岭地区不同林分类型下的生物量预测。【Objective】Forests are one of the most important natural resources.Understanding the impact of various factors on forest biomass is crucial for future forest spatial structure and management.Constructing biomass models for different forest types can provide scientific basis for the restoration and conservation of forest ecosystems.【Method】This study focuses on seven typical forest types in the Daxing’anling region of Heilongjiang Province,using data from 1157 monitoring plots in 2015.Sentinel-2 satellite images and digital elevation model(DEM)data provided by the European Space Agency were used to calculate vegetation indices,texture features,slope,and other variables.By integrating remote sensing data with field survey data and climate data,we established generalized least squares(GLS)biomass models and generalized additive models(GAM)for biomass.Ten-fold cross-validation was used,and the models were evaluated using root mean square error(RMSE),mean square error(MSE),and mean absolute error(MAE).Additionally,328 plots resurveyed in 2020 were used for model validation.【Result】The Generalized additive models(GAM)performed better than the Generalized Least Squares(GLS)models across the seven typical forest types.Specifically,the mean absolute error(MAE)of the GAM was reduced by 1.99%to 27.48%compared to the GLS models,the root mean square error(RMSE)was reduced by 4.29%to 20.87%,and the mean square error(MSE)was reduced by 6.72%to 35.43%.Secondary validation results showed that the prediction accuracy of the generalized additive model(GAM)for each forest type is above 80%.【Conclusion】Generalized additive models are a non-parametric method for constructing biomass models and are suitable for predicting biomass across different forest types in the Daxing’anling region.

关 键 词：生物量 林分类型 植被指数 广义加性模型 大兴安岭地区 

分 类 号：S758[农业科学—森林经理学]