| 1982—2015年中国北方生长季NDVI变化及其对气温极值的响应 |
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| 引用本文： | 何航,张勃,候启,李帅,马彬,马尚谦.1982—2015年中国北方生长季NDVI变化及其对气温极值的响应[J].干旱区研究,2020(1):244-253. |
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| 作者姓名： | 何航 张勃 候启 李帅 马彬 马尚谦 |
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| 作者单位： | 西北师范大学地理与环境科学学院 |
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| 基金项目： | 国家自然科学基金项目(41561024)资助 |
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| 摘 要： | 基于GIMMS NDVI 3g v1. 0数据集和日值气象数据,结合极端气温指数,辅以极点对称模态分解、趋势分析、Mann-Kendall趋势检验、相关分析等方法,探讨中国北方生长季植被覆盖及极端气温的变化特征,研究植被覆盖对气温极值的响应状况。结果表明:①1982-2015年中国北方生长季NDVI以0. 002·(10a)^-1的速率上升(P <0. 05),ESMD(极点对称模态分解方法)显示生长季NDVI波动上升;针叶林、灌丛、荒漠植被、草地以及栽培植被呈增长趋势,栽培植被增速最快,针阔混交林、落叶阔叶林和高山植被呈不显著减少趋势。②空间上,NDVI显著增加区域超过全区的33%,主要分布在天山、塔里木盆地北部、祁连山、陇南山区、黄土高原、河套平原、吕梁山和太行山、大别山以及辽西丘陵地区;显著下降区域仅占12%,主要分布在大兴安岭、小兴安岭和长白山区。③极端气温指数中,除TNmean(日最低气温平均值)和TNn(日最低气温极低值)呈上升趋势外,其余冷极值指数均呈下降趋势;所有暖极值指数均呈上升趋势;其他指数中,DTR(气温日较差)呈减小趋势,GSL(生长季日数)呈增加趋势。④中国北方NDVI与极端气温指数的相关性表明,冷极值指数中NDVI与FD0(霜冻日数)、TN10p(冷夜日数)、TX10p(冷昼日数)呈显著负相关(P <0.05),与TNmean呈显著正相关(P <0.01);NDVI与所有暖极值指数呈正相关,与TR20(热夜日数)、TXmean(日最高气温平均值)、TX90p(暖昼日数)以及TN90p(暖夜日数)存在显著相关性(P <0. 05);NDVI与GSL呈显著正相关(P <0.05)。⑤天山、塔里木盆地北缘、祁连山区、河套平原、黄土高原、太行山和吕梁山区等NDVI显著增加区域对极端气温指数的响应强烈。NDVI显著增加区主要对FD0、TNmean、TN90p、GSL等指数响应较强。NDVI显著减少区域对指数的响应各异,主要与SU25(夏季日数)呈显著负相关(P <0.05)。
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| 关 键 词： | 生长季 GIMMS NDVI 3g v1.0数据集 时空变化 极端气温指数 响应 中国北方 |
Spatiotemporal Change of NDVI and Its Response to Extreme Temperature Indices in North China from 1982 to 2015 |
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| HE Hang,ZHANG Bo,HOU Qi,LI Shuai,MA Bin,MA Shang-qian.Spatiotemporal Change of NDVI and Its Response to Extreme Temperature Indices in North China from 1982 to 2015[J].Arid Zone Research,2020(1):244-253. |
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| Authors: | HE Hang ZHANG Bo HOU Qi LI Shuai MA Bin MA Shang-qian |
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| Institution: | (College of Geographical and Environmental Sciences,Northwest Normal University,Lanzhou 730070,Gansu,China) |
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| Abstract: | Climate warming is conducive to enhancing vegetation activities. Here,the interannual and spatial variations of vegetation cover in north China in growing season were analyzed based on the satellite-derived normalized difference vegetation index (NDVI),and its responses to the change of extreme temperature indices were studied by using GIMMS NDVI 3g V1. 0 datasets,daily temperature and precipitation data. Across the whole study area,the trends calculated by linear regression showed that the NDVI value in growing season increased at a rate of 0.002·(10 a)^-1 from 1982 to 2015. The results of extreme-point symmetric mode decomposition showed that the NDVI increased gradually until 1992,decreased slightly until 2005,and then increased gradually. The NDVI values of coniferous forest,shrubbery,desert vegetation,grassland and cultivated vegetation were all in an increase trend,and those of mixed forest,broadleaved deciduous forest and alpine vegetation were in a decrease trend. Spatially,the NDVI was in a decrease trend from the southeast to the northwest,and the area of the regions where the vegetation was significantly improved accounted for 33% of north China. The regions where the NDVI increased significantly were mainly distributed in the Tianshan Mountains and north Tarim Basin in north Xinjiang,Qilian Mountains,mountainous area in south Gansu Province,Loess Plateau,Hetao Plain,Lvliang Mountain,Taihang Mountain,and hilly region in west Liaoning Province. The area of the regions where the NDVI decreased significantly were mainly distributed in the Great Khingan Range,Lesser Khingan Mountains and Changbai Mountain. Among the 18 extreme temperature indices,except the mean daily minimum air temperature and the lowest minimum air temperature were in an increase trend,all others of cold extreme temperature indices were in a decreased trend;the warm extreme temperature indices were all in an increase trend. The NDVI was negatively correlated with FD0,TN10p and TX10p (P <0. 05),but positively correlated with TNmean (P <0.01). The NDVI was positively correlated with all warm extreme temperature indices,and was significantly correlated with TR20,TXmean,TX90p and TN90p (P <0.05).There was also a significant positive correlation between NDVI and GSL (P < 0. 05). |
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| Keywords: | growing season GIMMS NDVI 3g V1 0 spatiotemporal variation extreme temperature index response North China |
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