基于多源遥感数据集的近30a西北地区植被动态变化研究

摘要/Abstract

摘要： 基于重构后的AVHRR GIMMS NDVI、MODIS NDVI、GLC 2000数据产品和研究区的128个气象站点的气温、降水数据, 利用回归分析、相关性分析法, 研究了西北地区近30a(1984-2013年)以来不同植被NDVI的时空变化及其与气候的相关性. 结果表明: (1)研究时段内, 西北地区植被NDVI变化整体上呈上升趋势, 将整个研究分为两个时段, 1984-1997年呈小幅上升趋势, 且波动起伏较大, 最大值在1993年, 最小值出现在1995年; 1997-2013年也呈波动上升趋势, 且上升趋势大于前一阶段. (2)空间上, 西北地区植被NDVI变化存在明显的区域差异, 大部分区域植被NDVI变化显著性较弱, 昆仑山、塔里木盆地北部、祁连山、青海的中东西部、甘肃东部、陕西北部等地区植被NDVI显著增加; 阿尔泰山、天山、伊犁哈萨克自治州等干旱地区植被NDVI下降趋势明显. (3)除甘肃的祁连山、青海东南部、陕西的秦岭等地植被NDVI的变化主要受气温的驱动外, 西北地区植被NDVI变化与气温整体上呈弱相关, 干旱半干旱地区植被与气温呈负相关; 除甘肃南部、祁连山西段和陕西中部等一些年降水量较多以及灌溉农业区或草地以外, 西北地区植被与降水呈较强正相关, 降水是影响植被变化的主要自然因素; (4)不同植被类型NDVI的变化具有时空差异性, 且与气温和降水的相关性不尽相同, 与气温由强到弱: 耕地>灌丛>草地>沼泽湿地>林地; 与降水由强到弱: 耕地>草地>灌丛>林地>沼泽湿地.

关键词: 植被变化, NDVI, 气温, 降水, 相关性, 分段线性拟合

Abstract: Remote sensing technology provides long time series of NDVI datasets in studying vegetation changes and has good advantages. At present, relatively in-depth change of vegetation NDVI are rarely studied by using long time series data over 30 years and in larger scale. The paper built the regression of different sourced remote sensing data, GIMMS and MODIS with overlapping year, and then reconstructed a long time series NDVI dataset. Combined the reconstructed NDVI, GLC2000(Global Land Cover 2000)product and temperature and precipitation data of 128 meteorological stations in the study area over 30 years, this paper analyzed spatial and temporal vegetation NDVI changes of different vegetation types in northwest China in recent 30 years(from 1984 to 2013)by using regression analysis and correlation analysis methods. The results are as follows: (1)Reconstructed long time series NDVI dataset has good timing characteristics and can be effectively used in monitoring dynamic changes of vegetation in northwest China in temporal and spatial scale.(2)Within the whole study period, vegetation change in northwest China showed an upward trend. Specifically, vegetation change showed a slight upward trend from 1984 to 1993, a slow decline trend from 1993 to 2002 and an upward trend again from 2002 to 2013.(3)Except for Qilian Mountains in Gansu, southeastern Qinghai, and Qinling in Shaanxi where NDVI change was driven mainly by temperature, vegetation NDVI and temperature in northwest China were weakly correlated and vegetation NDVI in arid and semi-arid regions was negatively correlated with temperature. In northwest, vegetation NDVI and precipitation showed a strong positive correlation, except for some areas with more precipitation, irrigated agricultural or meadows such as south Gansu, west Qilian Mountains and central Shaanxi. Precipitation was the main natural factor that affected the natural vegetation changes.(4)In spatial scale, vegetation NDVI change showed obvious regional differences that NDVI increased obviously in Kunlun Mountains, northern Tarim Basin, Qilian Mountains, western and middle eastern of Qinghai Province, northern Shaanxi and Gansu province, and decreased obviously in arid areas such as Altai Mountains and Ili Kazak Autonomous Prefecture.( 5)NDVI changes of different vegetation types showed various spatial and temporal variations and the correlation of different vegetation types between temperature and precipitation were also various. For temperature, from strong to week, the correlation ranked as: farmland > shrub > meadow > swamp > woodland; for precipitation, the correlation ranked as: farmland > grassland > shrub> woodland > swamp.(6)Based on remote sensing and GIS technology, the article discussed the dynamics changes of vegetation and its relationship between temperature and precipitation via NDVI, which provided relative scientific basis for further study of the response mechanism of vegetation and climate.

Key words: vegetation changes, NDVI, temperature, precipitation, correlation, piecewise linear fitting

中图分类号:

TP79

李净, 刘红兵, 李龙, 李彩云. 基于多源遥感数据集的近30a西北地区植被动态变化研究[J]. 干旱区地理, 2016, 39(2): 387-394.

LI Jing, LIU Hong-bing, LI Long, LI Cai-yun. Vegetation dynamic changes in northwest China based on multi-source remote sensing datasets in recent 30 years[J]. , 2016, 39(2): 387-394.

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