地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (4): 500-504.doi: 10.3724/SP.J.1047.2015.00500

• • 上一篇    

基于TAVI的山区毛竹林LAI反演分析

江洪1(), 张兆明2, 汪小钦1, 何国金2   

  1. 1. 福州大学空间数据挖掘与信息共享教育部重点实验室 福建省空间信息工程研究中心, 福州 350002
    2. 中国科学院遥感与数字地球研究所,北京 100094
  • 收稿日期:2014-02-20 修回日期:2014-04-13 出版日期:2015-04-10 发布日期:2015-04-10
  • 作者简介:

    作者简介:江 洪(1975-),男,福建永安人,博士,副研究员。研究方向为遥感技术与应用及电子政务等。E-mail:jh910@fzu.edu.cn

  • 基金资助:
    国家科技支撑计划项目“南方红壤水土流失综合监测”(2013BAC08B01);福建省自然科学基金项目“基于地形调节植被指数的毛竹林叶面积指数遥感反演研究”(2011J01267)

Bamboo Forest LAI Retrieval and Analysis in Mountainous Area Based on TAVI

JIANG Hong1,*(), ZHANG Zhaoming2, WANG Xiaoqin1, HE Guojin2   

  1. 1. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou 350002, China
    2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2014-02-20 Revised:2014-04-13 Online:2015-04-10 Published:2015-04-10
  • Contact: JIANG Hong E-mail:jh910@fzu.edu.cn
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要:

本文采用地形调节植被指数(TAVI),以RapidEye高分辨率多光谱遥感影像为数据源,对福建省永安市毛竹林山区进行了叶面积指数(LAI)地面实测、遥感建模及反演分析。通过TAVI与归一化植被指数(NDVI)、比值植被指数(RVI)的对比研究,结果表明:(1)毛竹林实测LAI与TAVI、NDVI和RVI线性回归的决定系数(R2)分别为0.6085、0.3156和0.4092,最佳非线性回归的R2分别提高到0.6624、0.5280和0.6497。LAI与NDVI或RVI非线性(U曲线)模型可以很好地解释LAI-VI的散点分布规律,但难以解决LAI-VI间因地形影响导致的“同物异谱”和“异物同谱”问题,因此,在山区大面积推广应用需慎重。(2)通过实测LAI的验证表明,LAI-TAVI回归模型可有效避免因地形影响导致的“同物异谱”和“异物同谱”问题。TAVI具有良好的削减地形影响作用,可用于山区植被LAI的遥感反演。

关键词: 叶面积指数, 地形调节植被指数, 同物异谱

Abstract:

As one of the key biophysical parameters in the bamboo forest evaluation, the leaf area index (LAI) retrieval from remote sensing data has always been challenged by the topographic effect in mountainous area. In this paper, the topographic-adjusted vegetation index (TAVI) was proposed to eliminate the topographic influence for the bamboo forest LAI derivation in Yongan city, Fujian, based on the Rapideye high spatial resolution satellite imagery. Normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were also utilized in the statistical analysis with respect to LAI for comparison with TAVI. The regression results indicate: (1) LAI is more linearly correlated with TAVI than NDVI or RVI. R2 (coefficient of determination) of the linear regression between LAI and TAVI, NDVI, RVI are 0.6085, 0.3156, and 0.4092 respectively. For the optimal non-linear fitting model, the corresponding R2 had increased to 0.6624, 0.5280 and 0.6497 respectively. Although the quadratic polynomial regression model can well explain the relationship between LAI and NDVI or RVI, it can hardly illustrate the typical phenomenon of "same object with different spectra" and "different objects with same spectrum" that resulted from topographic effect. (2) Both the LAI-TAVI regression models and the in-situ measurement demonstrate that the proposed method can effectively avoid the above problems with a correlation coefficient (r) of 0.7674 between in-situ and the simulated LAI, and a RMSE of 0.3403. In conclusion, TAVI shows good capability to alleviate the topographic effect and can be effectively applied to the LAI retrieval of the bamboo forest in mountainous area.

Key words: leaf area index (LAI), topographic-adjusted vegetation index (TAVI), same object with different spectra