气候是植被变化的主要驱动因子, 研究全球增暖背景下中国区域植被变化及其对气候的响应对于国家开展重大生态恢复评估和未来植被保护政策制定具有重要意义。利用2000 -2016年MODIS植被指数(Normalized Difference Vegetation Index, NDVI)数据集, 运用统计分析方法, 从平均态、 线性趋势、 时间序列、 相关性等方面系统分析了2000年以来中国区域植被变化及其对气候变化的响应。结果表明: 中国区域NDVI在平均态上呈现从东南向西北递减的空间分布, 受降水生长季的影响, 东部地区植被指数明显较大; 我国大部分地区NDVI呈现增加的趋势, 其中湿润半湿润地区NDVI增长幅度为0.037·(10a)-1, 而在干旱半干旱地区变化较小[0.013·(10a)-1]。NDVI的变化与气候驱动因素的相关性存在一定的区域差异, 其中: NDVI与气温变化在东南沿海、 东北东部以及青藏高原北部等地区呈现出显著正相关, 而在青藏高原南部等地区呈现微弱的负相关; 除青藏高原、 塔里木盆地和东北北部等地区外, NDVI与降水量在全国大多数地区呈正相关。从全国平均来看, 温度和降水变化对NDVI的贡献分别为7.5%和9.1%, 其中温度对NDVI变化的贡献主要体现在湿润半湿润地区(9.3%), 而降水的贡献则在干旱半干旱地区(12.2%)。植被变化对气候要素驱动的响应也呈现出明显的区域差异性, 在我国东南沿海、 云贵高原东部、 四川盆地等南方地区以及黄河中下游、 东北东部等部分地区, NDVI变化对气温的敏感性最强; 而在中国北方干旱半干旱大部分地区, NDVI变化则是对降水驱动具有很显著的响应特征。总体而言, 气温是驱动南方地区植被变化的主导因子, 而降水则调控着北方地区植被生长变化。
Climate change is the main driving factor of vegetation change.It is of great significance to study the vegetation change and its response to climate change in China under the background of global warming for the country to carry out major ecological restoration assessment and future vegetation protection policy formulation.Using MODIS vegetation index NDVI dataset during 2000-2016 and statistical analysis method, this paper systematically analyzes the regional vegetation change and its response to climate change in China since 2000 from the aspects of average state, linear trend, time series, correlation, etc.The results show that the spatial distribution of climate mean of NDVI in China is decreasing from southeast to northwest, and vegetation index in the East is significantly larger due to the influence of precipitation during the growing season.NDVI shows an increasing trend in most areas of China, especially in humid and semi-humid areas, the growth rate of NDVI reaches 0.037·(10a)-1, while in arid and semi-arid areas, the change of NDVI is relatively small.In the southeast coastal areas, eastern northeast area, and northern Qinghai Tibet Plateau, NDVI has a significant positive correlation with the temperature, with the rising speed of 0.02 ℃-1, while NDVI has a weak negative correlation with the temperature in the south of Qinghai-Tibetan Plateau, with the decreasing speed of more than -0.015 ℃-1.Similarly, NDVI is positively correlated with precipitation in Sichuan Basin and Inner Mongolia, and NDVI increases by more than 0.03 for every 100 mm increase in precipitation, NDVI is weakly negatively correlated with precipitation in Qinghai-Tibetan Plateau, Tarim Basin and other areas, that is, the change of precipitation has no obvious impact on NDVI.For the NDVI variation associating to climate change, the response of vegetation change to climate driving factor also shows obvious regional difference.In the southeast coast of China, the east of Yunnan Guizhou Plateau, Sichuan Basin and other southern areas, as well as the middle and lower reaches of the Yellow River, the east of northeast and other parts of the region, the change of NDVI is the most sensitive to temperature.In the most arid and semi-arid areas of northern China, the change of NDVI has very significant response to precipitation.In general, the precipitation is the driver factor for most of southern China while the air temperature is the dominator for most of northern China.
[1]Eckert S, Husler F, Liniger H, et al, 2015.Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia[J].Journal of Arid Environments, 113: 16-28.
[2]Fensholt R, Langanke T, Rasmussen K, et al, 2012.Greenness in semi-arid areas across the globe 1981-2007-an Earth Observing Satellite based analysis of trends and drivers[J].Remote Sensing of Environment, 121: 144-158.
[3]Harris I, Jones P D, Osborn T J, et al, 2014.Updated high-resolution grids of monthly climatic observations-the CRU TS3.10 Dataset [J].International Journal of Climatology, 34: 623-642.
[4]Lanfredi M, Simoniello T, Macchiato M, 2003.Comment on “variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999”[J].Journal of Geophysical Research Atmospheres, 108(D12): 4346.
[5]Wang Q, Zhang Q P, Zhou W, 2012.Grassland Coverage Change and Analysis of the Driving Force in Maqu County[J].Physics Procedia, 38(4): 693-704.
[6]Zhou L, Tucker C J, Kaufmann R K, et al, 2001.Variations in northern vegetation activity inferred form satellite data of vegetation index during 1981 to 1999[J].Journal of Geophysical Research: Atmospheres, 106(D17): 20069-20083.
[7]陈云浩, 李晓兵, 史培军, 2001.1983 -1992年中国陆地NDVI变化的气候驱动因子分析[J].植被生态学报, 25(6): 716-720.
[8]方精云, 朴世龙, 贺金生, 等, 2003.近20年来中国植被活动在增强[J].中国科学(生命科学), 33(6): 554-565.
[9]郭铌, 韩天虎, 王静, 等, 2010.玛曲退牧还草工程生态效果的遥感监测[J].中国沙漠, 30(1): 154-160.
[10]贾俊鹤, 刘会玉, 林振山, 2019.中国西北地区植被NPP多时间尺度变化及其对气候变化的响应[J].生态学报, 39(14): 5058-5069.
[11]焦珂伟, 高江波, 吴绍洪, 等, 2018.植被活动对气候变化的响应过程研究进展[J].生态学报, 38(6): 2229-2238.
[12]李晓兵, 史培军, 2000.中国典型植被类型NDVI动态变化与气温、 降水变化的敏感性分析[J].植被生态学报, 24(3): 379-382.
[13]刘斌, 孙艳玲, 王仲良, 等, 2015.华北地区植被覆盖变化及其影响因子的相对作用分析[J].自然资源学报, 30(1): 12-23.
[14]刘旻霞, 赵瑞东, 邵鹏, 等, 2018.近15 a黄土高原植被覆盖时空变化及驱动力分析[J].干旱区地理, 41(1): 99-108.
[15]刘宪锋, 朱秀芳, 潘耀忠, 等, 2015.1982 -2012年中国植被覆盖时空变化特征[J].生态学报, 35(16): 5331-5342.
[16]刘晓婉, 彭定志, 徐宗学, 2018.雅鲁藏布江流域NDVI对高程与降水的相依性研究[J].高原气象, 37(2): 349-357.DOI: 10. 7522/j.issn.1000-0534.2017.00048.
[17]马守存, 保广裕, 郭广, 等, 2018.1982-2013年黄河源区植被变化趋势及其对气候变化的相应[J].干旱气象, 36(2): 226-233.
[18]孟梦, 牛铮, 马超, 等, 2018a.青藏高原NDVI变化趋势及其对气候的响应[J].水土保持研究, 25(3): 360-365.
[19]孟梦, 牛铮, 2018b.近30a内蒙古NDVI演变特征及其对气候的响应[J].遥感技术与应用, 33(4): 676-685.
[20]南颖, 刘志锋, 董叶辉, 等, 2010.2000-2008年长白山地区植被覆盖变化对气候的响应研究[J].地理科学, 30(6): 921-928.
[21]朴世龙, 方精云, 2003.1982-1999年我国陆地植被活动对气候变化响应的季节差异[J].地理学报, 58(1): 119-125.
[22]孙艳玲, 郭鹏, 2012.1982-2006年华北植被覆盖变化及其与气候变化的关系[J].生态环境学报, 21(1): 7-12.
[23]孙艳玲, 延晓冬, 谢德体, 2007.基于因子分析方法的中国植被NDVI与气候关系研究[J].山地学报, 25(1): 54-63.
[24]王丹, 王爱慧, 2017.1901 -2013年GPCC和CRU降水资料在中国大陆的适用性评估[J].气候与环境研究, 22(4): 446-462.
[25]王丹云, 吕世华, 韩博, 等, 2018.黄土高原春季植被变化分布与变化特征及其对春旱的响应研究[J].高原气象, 37(5): 1208-1219.DOI: 10.7522/j.issn.1000-0534.2018.00033.
[26]武正丽, 贾文雄, 赵珍, 等, 2015.2000 -2012年祁连山植被覆盖变化及其与气候因子的相关性[J].干旱区地理, 38(6): 1241-1252.
[27]杨雪梅, 杨太保, 刘海猛, 等, 2016.气候变暖背景下近30a北半球植被变化研究综述[J].干旱区研究, 33(2): 379-391.
[28]朱文会, 毛飞, 徐影, 等, 2019.三江源去植被指数对气候变化的响应及预测分析[J].高原气象, 38(4): 693-704.DOI: 10.7522/j.issn.1000-0534.2018.00105.
[29]张佳琦, 张勃, 马彬, 等, 2019.三江平原NDVI时空变化及其对气候变化的响应[J].中国沙漠, 39(3): 206-213.
[30]赵天保, 陈亮, 马柱国, 2014.CMIP5多模式对全球典型干旱半干旱区气候变化的模拟与预估[J].科学通报, 59(12): 1148-1163.