沙地樟子松不同树高-胸径模型比较分析
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国家自然科学基金(31570709)


Comparing different height-diameter models of Pinus sylvestris var. mongolica in sandy land
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    摘要:

    目的 比较不同树高(H)-胸径(D)模型精度,确定适合章古台地区樟子松Pinus sylvestris var. mongolicaH-D模型。方法 以Sibbesen模型为基础模型,将优势木平均高(HT)、胸高断面积(AB)和平方平均胸径(DQM) 3个林分变量以不同组合加入基础模型中,分别建立了H-D的基础模型(1个)和广义模型(3个)及对应的基础混合模型(1个)和广义混合模型(3个)。对固定效应模型平均水平预测(FPA)、混合模型的总体平均响应预测(MPA)和主体响应预测(MPS)的精度进行比较。对混合模型在使用随机抽取样本木和抽取平均木(胸径接近平均值的样本)2种抽样方案计算随机参数时分析MPS精度与样本数量的关系。结果 表征樟子松H-D关系的4种固定效应模型中,含HTAB的广义模型拟合精度最高,Akaike信息量准则(AIC)=2 167.7,Bayesian信息量准则(BIC)=2 196.3。相同预测变量的各模型预测精度均表现为:MPS > FPA > MPA,仅含预测变量D的模型的3种预测精度差异最大。广义模型、广义混合模型、基础混合模型预测精度差异不大。使用验证数据检验模型精度时,每块标准地中随机抽取3株样本木计算基础混合模型随机参数时,该模型精度提升最为明显,MAE和RMSE分别降低了57.97%和57.63%;而广义混合模型精度随抽取样本木数量的增多未出现大的变化。结论 含有林分变量优势木平均高、胸高断面积的广义模型和基础混合模型均能较好地预测沙地樟子松人工林的单木树高。此外,利用混合模型预测树高时,推荐在标准地中随机抽取3株林木测量其树高,并依此来计算随机参数。

    Abstract:

    Objective To compare the accuracy of different height(H)-diameter (D) models to determine the optimal models for Pinus sylvestris var. mongolica in Zhanggutai area.Method Sibbesen model was used as the basic model. Dominant height (HT), stand basal area (AB), and quadratic mean diameter (DQM) with different combinations were added into Sibbesen model. We established one basic, three generalized, one basic mixed and three generalized mixed H-D models. The accuracies of population-averaged prediction (FPA) of fixed effects models, and mean response prediction (MPA) and specific-plot prediction (MPS) of mixed effects models were compared. For mixed models, two sampling designs, random sampling and medium-diameter tree sampling were used for random parameters estimation, and the relationship between MPS accuracy and sample size was analyzed.Result In four fixed H-D models, the generalized model with HT and AB has the highest prediction precision. Akaike’s information criterion (AIC) is 2 167.7. Bayesian information criterion (BIC) is 2 196.3. Models with the same predictor variables have precision in order of MPS > FPA > MPA, and models with D as the only variable have the largest variation among three types of prediction. There are little difference in prediction accuracy among generalized models, generalized mixed models and basic mixed model. Using three randomly selected sample trees per plot to estimate random parameters of basic mixed model results in the highest model precision based on the validation data, and MAE and RMSE decrease by 57.97% and 57.63% respectively. The accuracies of generalized mixed models do not change significantly with the increase of sample size.Conclusion Both generalized model including HT and AB and basic mixed model can well predict tree height for P. sylvestris var. mongolica. We recommend to randomly select three sample trees per plot measuring tree heights for parameters estimation of mixed models, and calculating random parameters.

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周晏平,雷泽勇,赵国军,韩艳刚.沙地樟子松不同树高-胸径模型比较分析[J].华南农业大学学报,2019,40(3):75-81

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  • 收稿日期:2018-06-26
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  • 在线发布日期: 2019-04-23