温度植被干旱指数(TVDI)在草原干旱监测中的应用研究  

Application study of temperature vegetation drought index (TVDI) in grassland drought monitoring

作　　者：陈斌[1] 张学霞[1] 华开[1] 徐珂[1] 

机构地区：[1]北京林业大学水土保持学院,北京100083

出　　处：《干旱区地理》2013年第5期930-937,共8页Arid Land Geography

基　　金：国家973项目(2012CB955403;2010CB951102)

摘　　要：以内蒙古锡林郭勒盟地区为研究对象,选取2010年研究区旱情发生显著变化的9、10月份的MODIS植被指数和陆地表面温度数据,构建草原地区NDVI-LST和EVI-LST特征空间,进而由此构建了草原地区的温度植被干旱指数(TVDI),并结合当地气象数据和野外同步实地测量得到的土壤含水量数据对该指数进行定量验证。结果表明:(1)基于EVI-TS特征空间构建的TVDI,同样适用于旱情研究;且在研究区植被覆盖度不高的条件下,基于NDVI-TS特征空间的TVDI更适用于干旱监测;(2)构建的NDVI-TS和EVI-TS特征空间,其散点图符合三角形的关系,与前人研究成果相符;(3)TVDI可以很好地反映研究区的旱情变化情况,可以对研究区进行旱情动态监测;(4)基于NDVI-TS及EVI-TS空间构建的TVDI均与实地同步野外采集的土壤含水量数据结果显著负相关。且通过对基于TVDI的干旱监测结果与研究区实际情况对比分析发现,两者在旱区分布范围、旱情强度等级、干旱发展进程等方面基本吻合,说明TVDI可以在时间上很好监测旱情变化,TVDI可以用来评价草原干旱状况。MODIS （moderate-resolution imaging spectroradiometer）data are widely used in large-scale, long-time, and dynamic drought monitoring for their high time resolution, high spectral resolution, moderate spatial resolution and other characteristics. In this paper, MODIS MOD11C3 （monthly land surface temperature products）and MODIS MOD13C2 （monthly vegetation index products）were used to study drought conditions of September and October in 2010 in Xilingol League, Inner Mongolia, China. Based on NDVI （normalized difference vegetation index）, EVI （en- hanced vegetation index）and LST （land surface temperature） indexes, the NDVI-LST and EVI-LST feature spaces were established, and from which the equations were obtained of dry and wet edges, then TVDI （temperature vegetation dryness index）was calculated. According to the criterion of drought levels of the arid and semi-arid regions, the spatial distribution maps of drought degree evaluation were obtained, then the map of drought evaluation com- parison between September and October was produced, simultaneously, combining local meteorological information to take qualitative analysis of the drought monitoring result, and using surface soil moisture data with field survey method to take qualitative validation of TVDI. The results show as follows： （1）Even though the most study got TDVI from NDVI-LST feature space, TVDI calculated from EVI-LST feature space could also be suitable for drought monitoring. Under the condition of low vegetation cover, NDVI- LST feature space has better effects than that of EVI - LST , because TDVI obtained from NDVI - LST feature space has better correlation with surface soil moisture than that from EVI-LST feature space; （2） NDVI-LST and EVI-LST feature spaces established in this paper accorded with triangular relationship; （3）Previous studies have shown that TVDI can be used in one-day drought monitoring, but this study shows that TVDI is also fit for long-time drought monitoring. From the drought compariso

关 键 词：干旱监测 MODIS 温度植被干旱指数 

分 类 号：P426.616[天文地球—大气科学及气象学]