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基于分级的福建将乐地区栲树树高曲线模型研究
李 杰1
中关村绿色碳汇研究院
摘要:
【目的】研究栲树树高曲线模型,为编制福建省将乐地区亚热带常绿阔叶林森林经营数表、指导当地森林经营提供依据。【方法】以将乐地区常绿阔叶林中的栲树为研究对象,以17个栲树林样地中427株栲树胸径、树高实测数据为建模数据,通过分析坡度、坡向和坡位3个地形因子与树高生长之间的相关关系,以Richards方程为基本方程,分别构建基于树高分级和地形分级的栲树树高曲线模型,并与经典树高曲线模型进行精度对比。【结果】基于树高分级的栲树树高曲线模型的决定系数(R2)最大,为0.630;均方根误差(RMSE)最小,为2.283,表明模型拟合精度较高。基于地形分级的栲树树高曲线模型的平均绝对误差(MAE)和平均相对误差(MRE)分别为1.707和0.163,均为最小,表明该模型预估精度最高。【结论】结合将乐地区实际情况,基于地形分级的树高曲线模型能较为精确地预估栲树树高。
关键词:  栲树  树高曲线  树高分级  地形分级  Richards方程  福建将乐地区
DOI:
分类号:
基金项目:“十二五”国家科技支撑计划项目“南方集体林区生态公益林可持续经营技术研究与示范”(2012BAD22B05)
Classification based height-diameter model for Castanopsis fargesii in Jiangle,Fujian
LI Jie
Abstract:
【Objective】The study established the height-diameter model of Castanopsis fargesii to provide basis for compiling forest management tables of subtropical evergreen broad leaved forest and guiding local forest management.【Method】Height-diameter model based on height classification and height-diameter model based on topographic factors classification ware built to estimate C. fargesii tree height based on correlations between height growth and topographic factors,including slope,aspect and slope position.Four hundred and twenty-seven sample trees,with measured DBH and tree height,in 17 plots of C. fargesii forest were used to validate models.Richards equation was used as basic equation.Fitting parameters and prediction accuracy of two models ware compared with classical Richards model.【Result】The height-diameter model based on height classification model had the highest R2 of 0.630 and lowest RMSE of 2.283,indicating the highest goodness of fitting.The height diameter model based on topographic factors classification had the lowest MAE and MRE of 1.707 and 0.163,indicating the highest accuracy of prediction.【Conclusion】The height-diameter model based on topographic factors classification had higher feasibility and practicality in Jiangle.
Key words:  Castanopsis fargesii  height diameter model  height classification  topographic factors classification  Richards equation  Jiangle,Fujian