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吕梁山南段森林碳密度及其空间分布格局研究
引用本文:王琰,俞艳霞,张先平,王孟本.吕梁山南段森林碳密度及其空间分布格局研究[J].生态环境,2013(10):1658-1664.
作者姓名:王琰  俞艳霞  张先平  王孟本
作者单位:[1]山西大学黄土高原研究所,江西太原030006 [2]山西林业职业技术学院,江西太原030009
基金项目:山西省回国留学人员科研项目(2011-004);山两省自然科学基金项目(2007021032)
摘    要:基于2000年和2005年两期森林资源清查资料,利用双向指示种分析( two-way indicator species analysis, TWINSPAN)方法,对森林植被进行群系划分;采用生物量换算因子法,对森林样地碳密度进行估算,对碳密度及其动态变化特征进行研究;基于地统计学原理对森林碳密度的空间分布格局进行分析,并对其影响因子进行探讨。结果表明,(1)吕梁山南段森林植被可分为9个群系,即臭椿群系、柳树群系、辽东栎-油松混交群系、辽东栎群系、辽东栎-枫树混交群系、辽东栎-白桦-山杨混交群系、白皮松-辽东栎混交群系、白皮松-侧柏混交群系和槐树群系。2000年各群系的碳密度值介于23.53 Mg·hm^-2和75.65 Mg·hm^-2之间,平均值为54.90 Mg·hm^-2;2005年的碳密度值介于24.16 Mg·hm^-2和78.91 Mg·hm^-2之间,平均值为57.20 Mg·hm^-2,5年间9个森林群系的碳密度增加了2.30 Mg·hm^-2。(2)森林碳密度呈现出自南向北、自西向东增加的趋势;球状模型能很好地反映森林植被碳密度的空间结构特征;碳密度分布主要受结构性因子影响,空间依赖性较强,在小尺度上没有明显规律,而在中尺度上有群团状分布的特点。(3)随着海拔的升高,森林碳密度先增后降,1600~1800 m最大;坡上部森林碳密度最高,其次为坡下部,山脊部最低;阴坡半阴坡森林碳密度一般高于阳坡和半阳坡,无坡向处最低;斜坡和平坡碳密度值明显高于其他坡地,急坡地最小。

关 键 词:森林  吕梁山  双向指示种分析  碳密度  空间格局

Forest carbon density and its spatial distribution in the south Liiliang mountains
WANG Yan,YU Yanxia,ZHANG Xianping,WANG Mengben.Forest carbon density and its spatial distribution in the south Liiliang mountains[J].Ecology and Environmnet,2013(10):1658-1664.
Authors:WANG Yan  YU Yanxia  ZHANG Xianping  WANG Mengben
Institution:1. Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China; 2. Shanxi Forestry Technological College, Taiyuan 030009, China)
Abstract:Forest carbon storage, one of the major carbon pools of terrestrial ecosystems, plays an important role in terrestrial carbon cycle. However, little research has been conducted on the features of carbon storage and spatial distribution of forest vegetation in Shanxi Province, China. In the present study, based on the forest inventory data in 2000 and 2005, the authors characterized the carbon density and its spatial distribution patterns of the forests in the South Lüliang Mountains (Shanxi Province, China). The forests were classified using Two-Way Indicator Species Analysis (TWINSPAN) method;the carbon density of them was estimated using the variable BEF (Biomass Expansion Factor) method, and the spatial distribution patterns of carbon density for these forests were analyzed based on geo-statistics theory. The results showed that the forest vegetation was classified into 9 formations, i.e. Form. Ailanthus altissima, Form. Salix babylonica, Form. Quercus liaotungensis + Pinus tabulaeformis, Form. Quercus liaotungensis, Form. Quercusliaotungensis + Liquidamdar formosana, Form. Quercus liaotungensis+Betula platyphylla + Populus davidiana, Form. Pinus bungeana+Quercus liaotungensis, Form. Pinus bungeana+Platycladus orientalis,and Form. Sophora japonica. The average carbon density of these formations was 54.90 Mg·hm^-2 in 2000 and 57.20 Mg·hm^-2 in 2005, respectively, with a range of 23.53 Mg·hm^-2 to 75.65 Mg·hm^-2 in 2000, and 24.16 Mg·hm^-2 to 78.91 Mg·hm^-2 in 2005, and the average increase of forest carbon density was 2.30 Mg·hm^-2 during the study period. The spatial heterogeneity of carbon density could be well described by spherical model. The distribution of forest carbon density was mainly affected by structural factors. The forest carbon density had a strong spatial dependence, without obvious trend on a small scale, while with cluster-shaped feature on a middle scale. The forest carbon density showed increasing trends from south to north and from west to east across the study area. The spatial distribution of forest carbon density was also influenced by altitudinal gradient, slope position, slope aspect, and slope gradient. The forest carbon density first increased and then decreased with the altitude increase, with the maximum value at 1 600-1 800 m. It was the highest in slope top and the second in slope bottom, while it was the lowest in the ridge area. The forest carbon density was higher at shady and half-shady slopes than at sunny and half-sunny slopes, and it was significantly higher at slope and flat ground than other slopes, with the lowest at steep slope.
Keywords:forest  L/iliang mountains  two-way indicator species analysis  carbon density  spatial pattern
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