干旱区研究

干旱区研究 >

2016 , Vol. 33 >Issue 3: 511 - 518

DOI: https://doi.org/10.13866/j.azr.2016.03.09

植物及植物生理

阿尔泰山小东沟乔木生物量空间分布规律

  • 井学辉 ,
  • 曹磊 ,
  • 刘云生 ,
  • 郭仲军 ,
  • 黄继红 ,
  • 臧润国
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  • 1.承德市环境保护局,河北 承德 067000;
    2.中国林业科学研究院森林生态环境与保护研究所,国家林业局森林生态环境国家重点实验室,北京 100091;
    3.包钢绿化有限公司,内蒙古 包头 014010;
    4.新疆林业科学研究院森林生态研究所,新疆 乌鲁木齐 830000
井学辉(1979-),女,博士研究生,主要从事环境生态和生物多样性保育方面的研究与管理工作. E-mail: jingxuehui19@126.com

收稿日期: 2014-09-05

  修回日期: 2015-03-20

  网络出版日期: 2016-05-31

基金资助

国家十二五科技支撑课题(2012BAD22B0301-2)资助

收起

Spatial Distribution Pattern of Biomass of Arbor Species in Xiaodonggou in the Altay Mountains,China

  • JING Xue-hui ,
  • CAO Lei ,
  • LIU Yun-sheng ,
  • GUO Zhong-jun ,
  • HUANG Ji-hong ,
  • ZANG Run-guo
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  • 1. Chengde Environmental Protection Agency,Chengde 067000,Hebei,China;
    2. National Key Laboratory of Forest Ecology and Environment of State Forestry Administration,Institute of Forest Ecology and Environmental Protection,Chinese Academy of Forestry,Beijing 100091,China;
    3. Baotou Steel Greening Co. Ltd,Baotou 014010,Inner Mongolia,China;
    4. Institute of Forest Ecology,Xinjiang Academy of Forestry,Urumqi 830000,Xinjiang,China

Received date: 2014-09-05

  Revised date: 2015-03-20

  Online published: 2016-05-31

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摘要

以中国境内阿尔泰山小东沟林区ETM+遥感影像数据和林分乔木生物量抽样调查数据为基础,选取比值植被指数、归一化差异植被指数、土壤调节植被指数、差值植被指数和近红外光百分比植被指数,分析了该林区植被指数与乔木地上生物量之间的相关性,并对生物量相关性最高的植被指数建立了植被指数与乔木地上生物量的线性回归预测模型。以预测模型为基础,利用ArcGIS 9.1软件的空间分析功能生成了小东沟林区乔木地上生物量空间分布图。生物量残差图中较强、中等和较低预测面积分别为66.60%、30.31%和3.09%,表明小东沟林区生物量空间分布的预测效果较好。将生物量空间分布预测图分别与坡度、坡向和海拔图叠加分析表明:小东沟林区乔木地上部分的生物量以斜陡坡(15°～35°)的最高(200～250 t·hm-2),平缓坡(0°～15°)次之(150～200 t·hm-2),急险坡(＞35°)的最低(100～150 t·hm-2)。东南、南坡向的生物量较低,而其余坡向的生物量较高。较低海拔(1 042～1 400 m)的生物量最低(100～150 t·hm-2),中海拔(1 400～1 900 m)的最高(200～250 t·hm-2),高海拔(＞1 900 m)生物量居中(150～200 t·hm-2)。说明利用遥感影像提取的植被指数可以很好地预测小东沟林区乔木的地上生物量,生物量的空间变化与地形因子有着密切的关系。

关键词： 植被指数; 乔木; 生物量; 回归模型; 小东沟; 阿尔泰山

本文引用格式

井学辉 , 曹磊 , 刘云生 , 郭仲军 , 黄继红 , 臧润国 . 阿尔泰山小东沟乔木生物量空间分布规律[J]. 干旱区研究, 2016 , 33(3) : 511 -518 . DOI: 10.13866/j.azr.2016.03.09

Abstract

It is an important approach for understanding the spatial variation of biomass at large scale to predict the spatial distribution of biomass based on remote sensing data and typical field investigation data. The Pearson correlations between RVI,NDVI,SAVI,DVI and IPVI and plot biomass were made separately using the Landsat 7 Enhanced Thematic Mapper data and the biomass investigation in the typical sample plots,the linear regression models between biomass and vegetation indexes were established for the Xiaodonggou forest region in the Altay Mountains. The spatial distribution map of aboveground biomass of arbor species in the Xiaodonggou was produced by the regression model and by using the spatial analysis function in ArcGIS 9.1 software. The residual type analysis showed that the strongly predicting area,moderately predicting area and lowly predicting area occupied 66.60%,30.31% and 3.09% respectively,which revealed that the predicted results of the spatial distribution of biomass in the study area were ideal. The results of overlay analysis of spatial distribution of aboveground biomass of arbor species with the slope,aspect and elevation revealed that the aboveground biomass of arbor species was in an order of steep slope (15°-35°,200-250 t·hm-2) > gentle slope (0°-15°,150-200 t·hm-2) > very steep slope (>35°,100-150 t·hm-2). In slope aspect,the biomass on the southeast slope and south slope was lower than that on other slope aspects. In elevation,the biomass was in an order of low elevation (1 042-1 400 m,100-150 t·hm-2) > high elevation (>1 900 m,150-200 t·hm-2) > moderate elevation (1 400-1 900 m,200-250 t·hm-2). This study showed that the aboveground biomass of arbor species can be well predicted with the vegetation indexes derived from remote sensing images,and the spatial distribution of biomass is significantly related to the topographical factors.

Key words： vegetation index; arbor species; biomass; regression model; Xiaodonggou; the Altay Mountains

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