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生态学杂志 ›› 2012, Vol. 31 ›› Issue (01): 222-226.

• 方法与技术 • 上一篇    下一篇

基于垂直植被指数的干旱区荒漠环境人工杨树林生物量模型

姚远1,2,丁建丽1,2**,倪绍忠1,2,王刚1,2   

  1. 1新疆大学资源与环境科学学院, 乌鲁木齐 830046;2绿洲生态教育部重点实验室, 乌鲁木齐 830046
  • 出版日期:2012-01-08 发布日期:2012-01-08

Biomass models of poplar plantations in arid desert environment of China based on perpendicular vegetation index. 

YAO Yuan1,2, DING Jian-li1,2**, NI Shao-zhong1,2, WANG Gang1,2   

  1. 1College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China; 2Key Laboratory of Oasis Ecosystem of Education Ministry, Xinjiang University, Urumqi 830046, China
  • Online:2012-01-08 Published:2012-01-08

摘要: 为了对中国干旱区荒漠环境下人工林生态系统生物量进行准确评估,本文以新疆克拉玛依地区人工杨树林为例,利用野外48个实测样地的人工林生物量数据和陆地卫星TM影像数据分析了遥感信息和实测人工林生物量的相关关系,分别建立了基于垂直植被指数(PVI)的地上生物量的线性和指数回归模型,并用归一化植被指数(NDVI)和比值植被指数(RVI)的地上生物量的线性和指数回归模型与PVI作比较。结果表明:3种植被指数PVI、NDVI、RVI与人工林生物量之间均具有极显著的相关关系;基于PVI的地上生物量的指数模型y=13.783e0.0257x为监测干旱区人工林地生物量的最优化关系模型(复相关系数为0.761)。

关键词: 菜地, 生物炭, 硝化抑制剂, 综合温室效应, 温室气体强度

Abstract: Taking the poplar plantations in Karamay City of Xinjiang as a case, and by using the actual poplar plantation biomass data of 48 sampling plots as well as the Landsat TM data,this paper analyzed the correlations between remote sensing information and actual poplar plantation biomass data,aimed to accurately estimate the biomass of poplar plantation ecosystem in arid desert environment of China. The linear and exponential models of poplar plantation aboveground biomass were established on the basis of perpendicular vegetation index (PVI), and applied to compare the normalize difference vegetation index (NDVI) and ratio vegetation index (RVI). There existed significant correlations between the three vegetation indices (PVI, NDVI, and RVI) and poplar plantation aboveground biomass, and the exponential model (y=13.783e0.0257x) based on PVI was the best one (with multiple correlation coefficient of 0.761) for estimating the poplar plantation biomass in arid desert environment.

Key words: vegetable field, biochar, nitrification inhibitor, global warming potential (GWP), greenhouse gas intensity (GHGI).