气候对云南松林分生物量的影响研究

The Effect of Climate on the Biomass of Pinus yunnanensis Standing Forest

基于云南省森林资源连续清查数据和气象站点数据,以为云南松林为研究对象,利用13种基础模型对林分生物量生长进行拟合,选取最优模型引入气象因子,分析林分生物量与不同气象因子的相关性。结果表明:年平均降水量(MAP)和年平均日照时数(MASD)与云南松林分生物量的相关性不显著;年平均温度(MAT)和年平均生物学温度(BT)与云南松林分生物量存在极显著的负相关关系(P<0.01);以决定系数(R2)和均方根误差(RMSE)为指标,选出最优模型为Gompertz模型,R2达到了0.461,RMSE为23.184 t/hm2;分别将MAT、WI、BT、HI气象因子引入最优模型后,模型RMSE和绝对平均相对误差(RMA)均有所降低,R2分别达到0.524、0.532、0.520、0.521;预估精度(P)分别达到73.349%、71.792%、72.863%、62.354%。因此引入气象因子将提高云南松林分生物量生长模型的拟合精度。

Under the background of global climate warming, climate change would affect the structure and function of forest ecosystem. Pinus yunnanensis was the main constructive species and the study of biomass of P. yunnanensis forest play an important role in the southwest region. The study analyzed the correlation between the bio- mass of P. yunnanensis forest and the meteorological factors that based on the data of the Continuous Forest Inventories （CFI） and combined with the data of meteorological stations in Yunnan. The 13 kinds of basic model were used to select the optimal model, and the meteorological factors were introduced into the optimal model to analyze the effect of climate on the biomass of P. yunnanensis. The result showed that： There was no significant correlation between mean annual precipitation （MAP） and mean annual sunshine duration （MASD） and biomass of P. yunnanensis. There was a sig- nificant negative correlation between themean annual temperature （MAT） and the biological temperature （BT） and biomass of P. yunnanensis. The optimal model was Gompertz model with the coefficient R2 and root mean square er- ror （RMSE） as index. The coefficient R2 and RMSE were 0. 461 and 23. 184 t/hm2. The determination coefficient was reached 0. 524, 0. 532, 0. 520, 0. 521 and the precision inspection was reached 73. 349%, 71. 792%, 72. 863%, 62. 354% after introducing into the meteorological factors of MAT, WI, BT and HI. The minimum root mean square error and the absolute average relative error were both reduced. Therefore, introduction the meteoro- logical factors would improve the fitting precision of the biomass growth model of P. yunnanensis.