| 摘要: |
| 【目的】探索云南松不同区域相容性生物量模型的构建方法，为云南松生物量建模工作提供技术支撑。【方法】以四川、西藏和云南150株云南松地上生物量实测数据为基础，选取基础生物量模型（一元、二元模型），引入以地理区域为特征的哑变量模型，建立不同省（自治区）云南松的地上总生物量及树干、干材、干皮、树冠、树枝和树叶各项生物量的通用模型；然后采用非线性度量误差联立方程组法，建立地上总生物量与各分项生物量的相容性生物量模型，根据方程构成的不同，该方法又分为比例总量直接控制及代数和控制2种方案；对上述模型的拟合效果进行评价。【结果】基础模型中，各项生物量的二元模型的拟合效果较一元模型明显提高。比例总量直接控制及代数和控制2种方案都能有效解决地上总生物量与各分项生物量间不相容的问题，其中二元模型优于一元模型，比例总量直接控制方案及代数和控制方案的拟合效果基本相当；引入哑变量可以有效地将不同地域的生物量模型融为一体。【结论】引入哑变量可减少工作量、增强模型稳定性；综合考虑模型精度和建模工作量，建议采用非线性度量误差联立方程组代数和控制方案构建相容性生物量模型。 |
| 关键词: 云南松 生物量模型 相容性 非线性度量误差联立方程组 |
| DOI： |
| 分类号: |
| 基金项目:北京市教育委员会科学研究与科研基地建设项目（省部共建重点实验室）；国家林业公益性行业科研专项（201204510）；国家林业公益性行业科研专项重大项目(201504303) |
|
| Establishment of compatible biomass models for Pinus yunnanensis in different regions |
|
LIU Weiyi,DENG Huafeng,HUANG Guosheng,et al
|
| Abstract: |
| 【Objective】This study explored the method of constructing compatible biomass models for different regions to provide technical support for the biomass modeling of Pinus yunnanensis.【Method】Based on the measurement data of aboveground biomass of 150 P.yunnanensis trees in Sichuan,Xizang and Yunnan,the general models for total above ground biomass as well as stem,wood,bark,crown,branch and leaf biomasses for trees from different regions were established with basic biomass models (one or two predictor variables) and dummy variables characterized by geographic regions.Then,the compatible biomass models were built using the method of nonlinear simultaneous equations with measurement error.According to different equations,the method was divided into direct control under total tree biomass by proportions and sum control.At last,the fitting effect was evaluated and analyzed.【Result】In basic models,the fitting effect of models with two predictor variables was significantly higher than models with one predictor variable.Both the direct control under total tree biomass by proportions and sum control could efficiently ensure that the total biomass was equal to the summary of its components with high accuracy.The models with two predictor variables were better than models with one predictor variable and the direct control method was as effective as sum control method.The introduction of dummy variable could integrate different regional biomass models effectively.【Conclusion】Introducing dummy variable was helpful to reduce workload and enhance model stability.For balancing prediction accuracy and workload,it is suggested to adapt the nonlinear simultaneous equations with measurement error of sum control to build compatible biomass models. |
| Key words: Pinus yunnanensis biomass model compatibility nonlinear simultaneous equations with measurement error |
