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LandsatTM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。
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.
出处
《遥感技术与应用》
CSCD
2004年第4期232-235,共4页
Remote Sensing Technology and Application
基金
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
关键词
热带森林植被
生物量
遥感
相关性分析
Tropical forest vegetation, Biomass, Remote sensing, Correlation analysis