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西南地区植被覆盖动态及其与气候因子的关系 被引量:31

Vegetation Dynamics and Its Relationship with Climatic Factors in Southwestern China
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摘要 基于2001至2013年16d合成的MODIS植被指数数据集以及西南地区121个气象站点月平均气温和降水数据,进行季节合成植被指数(SINDVI)的趋势模拟、偏差统计、简单相关分析和偏相关分析,探讨西南地区植被覆盖动态变化特征及其与气候因子的响应机制。结果表明:四川盆地西部、广西大瑶山和云贵高原部分地区植被退化趋势明显;而四川盆地南部、大巴山脉、贵州西部和广西南部沿海的植被呈改善趋势。中国西南大部分地区植被覆盖变化与温度因子的相关性更为显著,而其与降水因子在不同响应周期下的相关系数空间差异较大。 The response of the vegetation dynamics to climate variability in the southwestern China was analyzed based on the dataset from remotely sensed data and ground observation data,i.e.,MODIS NDVI data with the spatial resolution of 250 meters and temporal resolution of 16-day,and meteorological data composed of monthly temperature and precipitation data collected from 121 weather stations spanning periods of 2001-2013,respectively.Seasonally Integrated Normalized Difference Vegetation Index(SINDVI)utilized linear regression to characterize the trends in vegetation shifts.Anomaly analysis was applied to characterize the yearly average fluctuation.Furthermore,the monthly maximum Normalized Difference Vegetation Index(MNDVI)and meteorological data were employed to calculate the correlation coefficient at different time scales.The results indicate that vegetation coverage has extensive decreased trends in the west of Sichuan Basin,the Dayao Mountain in Guangxi and part of the Yunnan-Guizhou Plateau.Conversely,it shows an increased trend in the south of Sichuan Basin,the Daba Mountains,the west of Guizhou and the coastal areas in Guangxi.In more than half part of southwestern China,vegetation conditions are positively correlated with accumulated temperatures.But the partial correlation coefficient between vegetation condition and total precipitation has evident differences in regions.
出处 《遥感技术与应用》 CSCD 北大核心 2017年第5期966-972,共7页 Remote Sensing Technology and Application
基金 中央高校基本科研业务费专项资金资助(XDJK2015B021) 西南大学国家级大学生创新创业训练计划(NO.201610635075) 中国科学院大学生创新实践训练计划(Y5Y01110QM)
关键词 西南地区 NDVI 植被动态变化 气候 遥感 The southwest China NDVI Dynamic vegetation change Climate Remote sensing
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