草地植被盖度的多尺度遥感与实地测量方法综述

植被盖度作为一个重要的生态学参数被用在许多气候模型和生态模型中。地表实测和遥感测量是获取植被盖度的两种基本途径。以草地植被盖度的测量为研究对象,综合讨论了目前地表实测和遥感测量常用的方法,分析了它们的优缺点,并对如何提高草地植被盖度的测量精度做出展望。数码相机、高光谱遥感以及多尺度遥感数据的综合使用可能是未来草地植被盖度测量发展的趋势。

OVERVIEW OF FIELD AND MULTI-SCALE REMOTE SENSING MEASUREMENT APPROACHES TO GRASSLAND VEGETATION COVERAGE

     Vegetation coverage is an important ecology parameter and used in many climatic and ecological models. Field measurement and remote sensing measurement are rudimental approaches to get vegetation coverage. As far as field measurement, currently common methods include sampling, instruments and visible estimating. Field measurement plays a crucial role in ground vegetation investigation, which provides a background for interpreting and quantifying remote sensing data. However, field measurement has much self-limitation, which does not satisfy exhibiting vegetation features and variation in a large area.  Referring to remote sensing measurement, experiential models, sub-pixel models and vegetation indices approaches are three prime methods used for vegetation coverage estimation, which are restricted by some factors such as ground measurement precision and image spatial resolution. Vegetation indices mostly used in estimating vegetation coverage involve DVI,ARVI,ASVI,GEMI,SAVI,MSAVI and SAVI, which have various suitable conditions.  Corresponding to different spatial scales, actual remote sensing imagines can be divided into low spatial resolution images such as NOAA/AVHRR and MODIS, middling spatial resolution images such as TM,MSS and SPOT, and high spatial resolution images such as aerial photograph and IKONOS. Remote sensing measurement in grass vegetation coverage has close relation with field measurement data, so consummate design for field measurement is very essential for improving measuring precision of grassland vegetation coverage. Only fast combining these two kinds of data, we are having chances to get perfect grass vegetation coverage measuring results. 
    This article aims at analyzing and discussing measurement of grassland vegetation coverage, synthetically studying methods of filed measurement and remote sensing measurement, and prospecting possible methods to improve measuring precision of grassland vegetation coverage. Undoubtedly, with the development and mature of sensor technology and various mathematics models, it is possible for us to get remote sensing imagine of high spatial resolution, great spatial scales and credible performance.  Digital camera, hyperspectral remote sensing and comprehensive use of multi-scale remote sensing data are possible development trends for improving measurement precision to grassland vegetation coverage. It is true that remote sensing imagine will play more and more important role in studying vegetation characters in the future.