中文摘要：

基于福建省长汀县马尾松标准地的调查和全国8个不同省区常用的马尾松单株生物量估测模型,以胸径（D）、树高（H）、株数（T）等为自变量,计算和比较马尾松地上生物量（W）.结果表明：8个计算模型以江西的生物量模型所算出的生物总量值最大,且各样区内生物量的离散系数最高;安徽的生物量模型所算出的生物量总值最小,且各样区内生物量的离散系数最小.具体表现为：在生物量的总值比较上来说,对于不同的模型,以江西的模型计算出的生物量值最高,其次是广东的模型,贵州、福建、广西、湖北、湖南的模型计算出的生物量值较为接近,安徽模型计算出的生物量值最低;对于不同的样方,以样方3的各模型计算值差异性最大,其次是样方1、样方2和样方10,样方11和样方12的差异性最小.而通过对各模型计算出的生物量的离散系数进行比较后发现,江西模型的离散系数始终是最高的,福建、贵州、湖北、湖南和广西的模型的离散系数相对较为集中,其次是广东的生物量计算模型,离散系数最小的仍是安徽的生物量计算模型.因此在对生物量的估算研究当中,我们需根据研究区域的具体生态环境,建立最合适的模型,才能估算出最精确的生物量的值.

英文摘要：

Based module of biomass and compared the on the investigation of Pinusmassoniana Lamb sample plot in Changting County and the estimate per plant of Pinusmassoniana Lamb frequently used in eight provinces of China, it was calculated ground Pinusmassoniana Lamb biomass （W）, taking the diameter at breast height （D）, tree height （H） and tree numbers （T） as arguments. The results show that, the total biomass amount of Jiangxi Province is highest in eight estimate modules, with the highest coefficient of variation of biomass. While the total biomass amount of Anhui Province is lowest in eight estimate modules, with the lowest coefficient of variation of biomass. From the com- parison of total amount of biomass, among the different modules, the biomass calculated by the module of Jiangxi Province is highest followed by Guangdong Province. The biomass amounts calculated by the modules of Guizhou, Fujian, Guangxi, Hubei and Hunan Provinces are similar while the biomass amount calculated by the module of Anhui Province is lowest. For the different quadrats , the calculation difference reached highest by using quadrat 3 modules, followed by quadrat 1, 2 and 10. The differences calculated by quadrat 11 and 12 modules were lowest. After the comparison of the biomass coefficient of variation calculated by the different modules, it is found that the coefficient of variation calculated by the module of Jiangxi Province is always highest, the coefficient of variation calculated by the modules of Fujian, Guizhou, Hubei, Hunan and Guangxi Provinces are relatively concentrated, followed by the Guangdong Province. The coefficient of variation calculated by the biomass module of Anhui Province is still lowest. Therefore, during the study of biomass estimate, it is necessary to establish the most suitable module based on the actual eco - environment of study areas so as to get the most accurate biomass value.