中文摘要：

豫西山地是秦岭山系在河南境内的余脉，处于亚热带向暖温带的过渡区域，是气候变化的敏感区。利用S-G滤波算法重构2000—2013年MODIS．NDVI时序影像，结合DEM、气温和降水数据，运用趋势分析、相关性分析等方法探讨豫西山地NDVI及其气候响应的多维变化。结果表明：①14年来豫西山地NDVI呈增长态势，增速为0．041／10a。NDVI值随山地海拔升高先增后降，随坡度增加而增大，在各坡向的分布相差不大。②植被在〈1100m海拔区恢复概率最高，在〉1700m区域退化概率最高；在10°-20°坡度区域恢复概率最高，在0°～5°区域退化概率最高；坡向对植被变化的分异作用不明显。③不同海拔、坡度、坡向上的植被所受影响因素不同，高海拔区植被动态主要受降水控制；不同坡度上的植被NDVI与气温的相关性均大于与降水的；在不同坡向上差异不明显。④崤山、熊耳山、伏牛山三大山脉北坡NDVI增速均大于南坡；北坡植被对降水变化较敏感，而南坡植被对气温变化较敏感。这些都是在全球变化背景下该区生态环境响应的重要信号，反映了过渡带生态响应因子对山地生态系统的重要性。

英文摘要：

Western Henan Mountains, the extent of Qinling Mountains in Henan province and the transition from subtropical to warm temperate zone, are sensitive to climate change. This study sought to analyze vegetation NDVI change and its response to climate change in this sensitive area in multi-dimensions because the multi-dimensional ecological unit analysis is conducive to vegetation protection and ecology restoration in mountain ecosystems. We firstly used S-G filtering algorithm to reconstruct the MODIS-NDVI time-series data from 2000 to 2013 and combined DEM, temperature and precipitation data in the study area; then we used statistical analyses （i.e., linear regression, correlation analysis, and so on） to study vegetation NDVI change and its response to climate variables （temperature and precipitation） in different terrain factors （elevation, slope, and aspect）. The results showed that： （1） in 2000-2013, there was a significant growth of vegetation NDVI in the study area, and the growth rate was 0.041/ 10a. The finding suggested that, in general, the vegetation in the Western Henan Mountains was positively developed in this area. Meanwhile, the mean NDVI value increased with the increase of elevation, and then the trend became decreased; while it gradually increased as the slope increased. The mean NDVI value, however, had no significant differences in each aspect. （2） The recovery probability of vegetation in 〈1100 m regions was the highest, whereas the degradation probability in 〉1700 m regions was the highest. Regarding the slope, the recovery probability of vegetation in 10°-20° regions was the highest, while the degradation probability in 0°-5° regions was the highest. The variation of recovery （or degradation） probability of the aspect was not obvious somehow. （3） Vegetation in different terrains was affected by distinctive climate factors. Specifically, vegetation NDVI change at high elevations had stronger correlation with precipitation than with temperature, which indic