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吉林金沟岭林场不同密度天然云冷杉林林下主要灌木生物量模型
引用本文:季蕾,亢新刚,张青,郭韦韦,周梦丽. 吉林金沟岭林场不同密度天然云冷杉林林下主要灌木生物量模型[J]. 浙江农林大学学报, 2016, 33(3): 394-402. DOI: 10.11833/j.issn.2095-0756.2016.03.004
作者姓名:季蕾  亢新刚  张青  郭韦韦  周梦丽
作者单位:1.北京林业大学 林学院,北京 1000832.北京林业大学 理学院,北京 100083
基金项目:国家林业局引进国际先进农业科学技术计划(“948”计划)项目2013-4-66“十二五”国家科技支撑计划项目2012BAD22B02-3
摘    要:为了研究吉林省汪清县金沟岭林场林下灌木的生物量,以该林场3种不同郁闭度(0.6,0.8,1.0)的天然红皮云杉Picea koraiensis,鱼鳞云杉Picea jezoensis和冷杉Abies nephrolepis林为研究对象,以灌木生物量实测数据为基础,用R软件拟合了灌木层出现频率较高的13个物种单一物种生物量最优模型和各物种不同器官的最优模型,挑选判定系数R2和方差分析F值较大,剩余标准差ESEE和平均相对误差E值较小的作为生物量最优模型,以及探索了不同主林层密度下各物种生物量的差异与分配。结果表明:各物种不同器官最优模型除了青楷槭Acer tegmentosum叶和根,花楷槭Acer ukurunduense干为幂函数外,其他多为一元二次方程或二元一次方程;单一物种混合模型多为一元二次方程或二元一次方程。枝、干最优模型的自变量多为D2H(D为地径,H为株高)和CH(C为冠幅,H为株高);根系多采用因子D2H。林下灌木生物量(W)随着林分密度的减小,出现先减小后增大的趋势,即W(0.6)>W(1.0)>W(0.8)。图2表4参27

关 键 词:森林生态学   灌木   生物量模型   云杉林   冷杉林   混交林
收稿时间:2015-06-01

Shrub models in a spruce-fir forest of different densities in Jingouling Plantation,Jilin Province
JI Lei,KANG Xingang,ZHANG Qing,GUO Weiwei,ZHOU Mengli. Shrub models in a spruce-fir forest of different densities in Jingouling Plantation,Jilin Province[J]. Journal of Zhejiang A&F University, 2016, 33(3): 394-402. DOI: 10.11833/j.issn.2095-0756.2016.03.004
Authors:JI Lei  KANG Xingang  ZHANG Qing  GUO Weiwei  ZHOU Mengli
Affiliation:1.College of Forestry, Beijing Forestry University, Beijing 100083, China2.School of Science, Beijing Forestry University, Beijing 100083, China
Abstract:In order to study the shrub biomass in the forest in Jingouling Plantation and there wasn’t a comprehensive shrub models study in this area. Three different forest densities (0.6, 0.8, 1.0) of a natural spruce-fir Picea koraiensis-Picea jezoensis-Abies nephrolepis forest in Jingouling Plantation, Jilin Province were used for this research. Based on measured data for shrub biomass under the forest canopy, 13 mixed biomass models for 13 different species and biomass models of the different shrubs’ organs were respectively constructed by R software. Then the optimal model was selected using the largest R2 and F and the smallest ESEE and E. Also differences in shrub biomass for forests of different densities and for different species distribution with the same forest density were studied. Shrub quadrat 5 m × 5 m were setted in the center and four corner at each sample plot (20 m × 20 m), then measured the diameter D, crown width C1, C2, and height H. According to the results of the shrub species, 13 species, were selected from the sample plots. Results showed that the optimal biomass model (R2=0.626 2-0.997 9)for different organs in each species as well as for leaves and roots of Acer tegmentosum, and the trunk of Acer ukurunduense was a power function; whereas the optimal model of the other species was a quadratic equation or binary linear equation. The independent variable of the model for stem and branch biomass was AC(AC=πC1C2/4) with the other species being D2H and CH(crown width multiply height). The independent variables in the optimal model of leaf biomass for species Spiraea pubescens, Acanthopanax senticosus and A. tegmentosum used factors of AC and CH, but other species used variables D and D2H. The optimal model for root biomass was D2H. The accuracy of the models are relatively high, and they meets the requirements of biomass model estimation, which provides the basis for the measurement of shrub biomass in this region. The research is only suitable for this area and the shrubs’ ground diameter must in a certain range, for other areas to be further studied[Ch, 2 fig. 4 tab. 27 ref.]
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