摘要
选用南京市SPOT 5 HRG图像的地物反射率（PAC）、表观反射率（TOA）和灰度值（DN）影像，提取了4种植被指数（VI），即归一化植被指数（NDVI）、转换植被指数（TVI）、土壤调节植被指数（sAw）和修正的土壤调节植被指数（MSAVI），与地面实测的植被覆盖度进行了回归分析，并建立了36个VI-VFC关系模型．结果表明：在所有模型中，基于PAC级影像提取的NDVI和刑的3次多项式模型最优；其次为基于DN级影像提取的SAVI和MSAVI的3次多项式模型，在VFC〉0．8时其精度略高于前两种模型．这4个模型在植被中等密集区域（VFC=0．4-0．8）的精度高于植被稀疏区域（VFC=0～0．4）．所建模型可通过中间模型的联结，进行推广使用．在基于VI-VFC关系建模过程中，基于遥感影像不同辐射校正水平提取植被指数，有利于充分挖掘遥感影像信息，进而提高VFC估算的精度．
The images of post atmospheric correction reflectance （PAC）, top of atmosphere reflectance （ TOA）, and digital number （DN） of a SPOT 5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices （ VIs ）, i. e. , normalized difference vegetation index （NDVI）, transformed vegetation index （TVI）, soil-adjusted vegetation index （SAVI）, and modified soil-adjusted vegetation index （MSAVI） , and 36 VI-VFC relationship models were established based on these VIs and the VFC data obtained from ground measurement. The results showed that a- mong the models established, the cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with the accuracy being slightly higher than that of the former two models when VFC 〉 0. 8. The accuracy of these four models was higher in middle-densely vegetated areas （ VFC = 0. 4-0. 8 ） than in sparsely vegetated areas （ VFC = 0-0. 4）. All the established models could be used in other places via the introduction of calibration models. In VI-VFC modeling, using Vls derived from different radiometric correction levels of remote sensing image could help mining valuable information from remote sensing image, and thus,improving the accuracy of VFC estimation.
出处
《应用生态学报》
CAS
CSCD
北大核心
2008年第6期1296-1302,共7页
Chinese Journal of Applied Ecology
基金
国家自然科学基金项目(40371053)
国家重点基础研究发展规划资助项目(2007CB407206)
关键词
辐射校正
植被指数
植被覆盖度
模型
radiometric correction
vegetation index
vegetation fractional coverage
model.
