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三江平原湿地植被叶面积指数遥感估算模型

靳华安1,2;刘殿伟1;王宗明1;宋开山1;李方1;杨飞1,2;杜嘉1,2;李凤秀1,2   

  1. 1中国科学院东北地理与农业生态研究所, 长春130012;2中国科学院研究生院, 北京100039
  • 收稿日期:2007-10-11 修回日期:1900-01-01 出版日期:2008-05-10 发布日期:2008-05-10

Remote sensing estimation models of wetland vegetation LAI in Sanjiang Plain.

JIN Hua-an1,2; LIU Dian-wei1;WANG Zong-ming1;SONG Kai-shan1;LI Fang1; YANG Fei1,2;DU Jia1,2;LI Feng-xiu1,2    

  1. 1Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China; 2Graduate University of Chinese Academy of Sciences, Beijing 100039, China
  • Received:2007-10-11 Revised:1900-01-01 Online:2008-05-10 Published:2008-05-10

摘要: 利用中巴资源卫星CBERS-02影像提取的归一化植被指数(NDVI)和同期野外实测的叶面积指数(LAI)数据,分析了三江平原洪河自然保护区草甸、沼泽植被、灌丛和岛状林4种湿地植被及样本总体的NDVI与LAI之间的相关关系,建立了NDVI与不同湿地植被类型叶面积指数间的线性和非线性回归模型,并制作完成洪河自然保护区LAI空间分布图。结果表明,整个研究区样本总体的LAI估算效果不太理想,其NDVI与LAI的相关性仅为0.523;将研究区分为草甸、沼泽、灌丛和岛状林4种湿地植被类型,NDVI与各植被型LAI的相关性和估算效果均有很大程度的提高,所建立的LAI遥感反演模型以三次曲线回归方程拟合精度最高,R2分别达到0.723、0.588、0.837、0.720。以上结果表明,结合地面实测数据并基于遥感植被分类的基础上,CBERS-02遥感影像可用于较大区域内湿地植被生理参数的反演研究。

关键词: 科尔沁沙地, 沙层水分, 空间异质性, 植物群落格局

Abstract: By using the normalized difference vegetation index (NDVI) extracted from CBERS-02 data and the leaf area index (LAI) data obtained from field measurement, this paper analyzed the relationships between the NDVI and the LAI of total samples of meadow, marsh vegetation, shrubs, and islanded forests of the wetland vegetation in Honghe Nature Reserve of Sanjiang Plain. The linear and non-linear regression models between NDVI and LAI of different wetland vegetation types were established, and the spatial distribution of LAI in the Honghe Nature Reserve was mapped. The results showed that it was unsatisfactory for the whole samples to estimate the LAI, with the correlation coefficient being only 0.523. After the total samples were divided into four vegetation types, i.e., meadow vegetation, marsh vegetation, shrub and islanded forest, the correlation coefficients and the estimation accuracy were improved evidently. Cubic-equations-were found to be the best in the different forms of the regression models for retrieving the LAI of wetland vegetation by using CBERS data, with the R2 value being 0.723, 0.588, 0.837, and 0.720, respectively. It was indicated that with the combination of field-measured data and remote sensing-based vegetation classification, CBERS-02 data could be used for the larger scale estimation of the physiological parameters of wetland vegetation.

Key words: Korqin sandy land, Spatial heterogeneity, Water content in sand layer, Pattern of plant community