首页 | 本学科首页   官方微博 | 高级检索  
     

不同树种组的热带森林植被生物量与遥感地学数据之间的相关性分析
引用本文:杨存建,张增祥,党承林,王宝荣. 不同树种组的热带森林植被生物量与遥感地学数据之间的相关性分析[J]. 遥感技术与应用, 2004, 19(4): 232-235. DOI: 10.11873/j.issn.1004-0323.2004.4.232
作者姓名:杨存建  张增祥  党承林  王宝荣
作者单位:1.四川师范大学资源环境学院遥感与GIS应用研究中心,四川成都 610068;2.中国科学院遥感应用研究所,北京 100101;3.云南大学生态与地植物学研究所,云南昆明 650018
基金项目:科学院知识创新项目(CX10G-E01-02-03),国家自然科学基金项目(40161007),云南省应用基金项目(2000d00020)的联合支持
摘    要:以我国云南省西双版纳的热带森林为例,分别对栎类林、其它阔叶林、其它硬阔叶林的生物量与其对应的Landsat TM数据及其派生数据、气象数据和地形数据进行了相关性分析。首先,利用森林资源连续清查的林业固定样地数据,通过各树种组的各器官生物量估算模型计算出各样地的森林植被的生物量。然后,根据样地的坐标来建立样地GIS数据库。其次,将同期的遥感影像、气象数据和地形数据与GIS数据进行配准,并从遥感影像中产生出一系列的派生数据。最后,在此基础上,分别对栎类林、其它阔叶林、其它硬阔叶林的样地生物量与其遥感数据和派生数据、气象数据和地形数据进行相关性分析。研究表明,栎类林的生物量与TM1、TM2、TM3、TM4、TM5、TM7、缨帽变换的亮度、绿度、湿度、VI3、DVI、PC1和PVI在0.05的水平上显著,而与其它因子在这个水平上相关都不够显著。其它阔叶林的生物量与降雨量在0.05的水平上显著,而与其它因子在这个水平上相关都不够显著。其它硬阔叶林的生物量与降雨量在0.05的水平上显著,而与其它因子在这个水平上,其相关都不够显著。

关 键 词:热带森林植被  生物量  遥感  相关性分析  
文章编号:1004-0323(2004)04-0232-04
修稿时间:2003-09-24

The Correlation Analysis of the Landsat TM Data,Its Derived Data,Meteorological Data and Topographic Data with the Biomass of the Tropical Forest Vegetation of Different Forest
YANG Cun-jian. The Correlation Analysis of the Landsat TM Data,Its Derived Data,Meteorological Data and Topographic Data with the Biomass of the Tropical Forest Vegetation of Different Forest[J]. Remote Sensing Technology and Application, 2004, 19(4): 232-235. DOI: 10.11873/j.issn.1004-0323.2004.4.232
Authors:YANG Cun-jian
Affiliation:1.Research Center of Remote Sensing and GIS Applications,Sichuan NormalUniversity,Chengdu610066,China; 2.Institute of Remote Sensing Application,Chinese Academy Sciences,Beijing100101,China; 3.Institute of Ecology and Ge-botany,Yunnan University,Kunming650018,China
Abstract:The correlation analysis of the Landsat TM data, its derived data, meteorological data and topographic data with the biomass of the tropical forest vegetation for the lithocarpus forest, the other broad leaf forest and the hard broad-leaf forest is explored here in Xishuangbanna, Yunnan province, P.R.of China. It includes four steps. Firstly, the biomass for each forest sample is calculated by using the field inventory data of each sample. Secondly, GIS Database is established according the coordinate of each forest sample. Secondly, Remote sensing image and its derived data, meteorological data, topographical data and the biomass of each sample are referenced to the same projection and coordination. Finally, the correlation between the Landsat TM and its derived data, meteorological data, topographical data and the biomass is analyzed respectively for the lithocarpus forest, the other broad leaf forest and the hard broad-leaf forest. It is shown as follows: (1) The correlations of the biomass of lithocarpus forest and Landsat TM1,TM2,TM2,TM4,TM5,TM7,Bright,Green,Wet,VI3,DVI,PC1 and PVI are obvious at 0.05 level. (2) The correlation between the biomass of the other broad leaf forest and average rainfall per year is obvious at level 0.05. (3)The correlation of the biomass of the hard broad-leaf forest and average rainfall per year is obvious at level 0.05.
Keywords:Tropical forest vegetation   Biomass   Remote sensing   Correlation analysis
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《遥感技术与应用》浏览原始摘要信息
点击此处可从《遥感技术与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号