森林林下植被生物量收获的样方选择和模型  

Selection of gaining quadrat for harvesting the undergrowth vegetation and its biomass estimation modeling in forest

作　　者：杨昆[1] 管东生[1] 

机构地区：[1]中山大学环境科学与工程学院,广州510275

出　　处：《生态学报》2007年第2期705-714,共10页Acta Ecologica Sinica

基　　金：国家自然科学基金资助项目(49571064);国家"985工程"GIS与遥感的地学应用科技创新平台资助项目(105203200400006);广东省自然科学基金资助项目(021740)~~

摘　　要：在控制误差内寻求样方的最小面积和最少样方数量是植被生态学野外研究的重要问题。在综合考虑取样的边界效应和时间、劳力消耗的基础上,研究了在控制误差内测定森林林下植被生物量时应选择的最佳样方大小和数量,并找出最佳的自变量拟合了估算林下植被生物量的预测方程。结果表明,利用Wiegert的方法测定研究区林下植被生物量取样方案,得出0.25m2的小样方为最佳取样面积。但小样方受边界效应的影响很大,会产生过高的生物量估计。通过分析了边界效应的影响和生物量相对平均值的变化,得出2m×1m是本研究的最佳样方面积,而10个2m×1m的样方能把标准误差控制在生物量平均值的10%以内。灌木生物量回归方程所选取的3个自变量D2H、CH和PH中,CH与灌木生物量的相关性和以CH为自变量的线性回归方程的拟合度较其他2个变量好。而以PH为自变量的灌木生物量预测方程在实际操作中能提高研究的简便性和效率。以PH为自变量的林下草本层单位面积生物量的预测方程分别为WU=11.65+4.25(PH)和WD=24.23+6.85(PH)。It is an important academic issue in field vegetation ecology that selecting the optimum as controlled by a standard error of mean. In this paper, based on time and labor consumed and selection of optimum size and number of the sampled quadrats in estimating forest undergrowth size and number of sample edge effect considered, the biomass was studied under the standard error of mean needed to control. An optimum independent variable was chosen to simulate prediction equation of estimating biomass of forest undergrowth vegetation. The results indicated that 0.25 ms was the optimum size for sampling the undergrowth vegetation biomass in this site by using Wiegert＇s method. However, the 0.25 m2 quadrat was too small and may induce a large edge effect to make an overestimate of undergrowth biomass. Furthermore, it is difficult to harvest the undergrowth vegetation by using the small simple size since lots of shrubs were too large to be encircled in it. Considering the impacts of edge effect and variation of the biomass relative mean value, we suggested the optimum size of 2 m ×1 m for undergrowth biomass sampling and 10 quadrats with the same size may control the standard error within 10% of the biomass average value. Of the three selected combination variables （D2H, PH, and CH） for establishing biomass estimation models, CH is the most suitable for undergrowth shrub biomass estimation than the others. However, a linear regression equation combined with the independent variable PH can enhance the efficiency and convenience of investigation. The optimum estimation equation with the independent variable PH for undergrowth herbage layer biomass in unit area were WU = 11.65 ＋ 4.25 （PH） for aboveground biomass and Wo = 24.23 ＋ 6.85 （PH） for the below ground biomass.

关 键 词：林下植被 生物量估算 样方大小 样方数量 

分 类 号：S718.5[农业科学—林学]