学术
摘 要:
印度遥感卫星IRS—P6的LISS3数据由于其较高的空间分辨率和相对较低的数据价格而受到广泛关注，而利用LISS3数据估测森林生物量的研究报道较少。以高黎贡山自然保护区常绿阔叶林为研究对象，以2006年印度卫星IRS—P6的LISS3影像为主要数据源，利用地面样地胸径每木调查数据，结合生物量相对生长式，得出样地生物量。通过遥感数据提取4个波段的光谱值、6种植被指数，从DEM获取的海拔、坡度、坡向，共13个遥感及地学因子。在此基础上，提取13个因子的主成分，以前5个主成分值作自变量，建立主成分与地面生物量的回归模型，模型经方差分析及相关性检验，达到显著相关水平，相关系数R=0．7129。[著者文摘]
文章出处:
《中南林业调查规划》-2008年27卷1期 -42-45页
栏目信息:
文献标识码:
A
文章编号:
1003-6075（2008）01-0042-04
Study on Forest Biomass Estimation Model Based on the Satellite Data of IRS-P6
XU Tian-shu,YUE Cai-rong （Resources Faculty, Soulhwes! Forest College, Kunming 650224, China）
Abstract:
Remotely sensed data LISS3 of India satellite IRS-P6 are paying more attention recently because of its higher spatial resolution and lower price. Taking the GaoLiGongShan nature reserve as a research site, a study on forest biomass estimation model in this paper was conducted based on the forest biomass on the field of sampling plot and relative factors such as the factors of remotely sensed data, vegetation index and topographic data. Firstly the forest biomass of the plots in the evergreen broadleaved forest was calculated based on the relative growth equations and the diameter of all the trees in the plot. Secondly the principal components （PCs） for the 13 factors, which included 4 multi-spectral band value of IRS;6 kinds of vegetation index derived from them;elevation, slope and aspect coming from DEM was analyzed by principal components analysis （PCA）. Finally, a forest biomass model was set up based on the first 5 principal components. F test examination showed that the forest biomass was correlated significantly to these first 5 principal components with coefficient R being 0.7129.[著者文摘]
Key words:
forest biomass; IRS; remote-sensing information model; GaoLiGongShan Nature Reserve
基金资助:
云南省重点建设专业--西南林学院林学专业资助（2005）.
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