TM影像VI提取植被信息技术研究

应用规一化植被指数（ＮＤＶＩ）、土壤调节植被指数（ＳＡＶＩ）和修正后的土壤调节植被指数（ＭＳＡＶＩ），研究了ＴＭ影像植被信息的提取技术。结果表明，ＳＡＶＩ较之ＮＤＶＩ和ＭＳＡＶＩ，消除土壤背景影响和适应植被密度变化的能力更强，是植被信息提取效果较好的植被指数。使用ＳＡＶＩ，关键是要确定一个合适的土壤调节参数Ｌ，以最大限度地消除土壤背景的影响，ＳＡＶＩ提出时推荐的最佳Ｌ值（０．５）应用于卫片处理有很大局限性；基于研究结果，本文给出了适于不同植被条件卫片处理的最佳Ｌ取值。

METHODS FOR SUBTRACTING VEGETATION INFORMATION USING VEGETATION INDEX(VI) FROM TM IMAGES

The methods for subtracting the vegetation information from TM images by using the normalized difference vegetation index(NDVI),soil-adjusted vegetation index(SAVI)and modified soil-adjusted vegetation index(MSAVI)are studied.The study results show that,compared with NDVI and MSAVI,SAVI can minimize the effect of soil background and has a greater ability to reflect changes of vegetation density.When using SAVI it is important that a proper soil-adjusted factor(L)is incorporated into the SAVI formula to minimize the soil effects over the whole image.The optimal L value(0.5) recommended by a researcher based on oberservation for single vegetation densities with two soil types could not be applied to satellite images with varied soil and varied vegetation conditions.This paper describes the identification of optimal L values that can be applied to different vegetation conditions. \ \