Changes in Vegetation Growth Dynamics and Relations with Climate over China's Landmass from 1982 to 2011
Changes in Vegetation Growth Dynamics and Relations with Climate over China's Landmass from 1982 to 2011
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1982-2011年中国陆地植被生长动态变化及其与气候的关系
DOI：
10.3390/rs6043263
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发表时间：
2014-04
期刊：
影响因子：
5
通讯作者：
Myneni, Ranga B.
中科院分区：
文献类型：
--
作者：
Yan, Jianwu;Zheng, Yonghong;Zhu, Zaichun;Myneni, Ranga B.
了解植被生长动态如何在不同的时间和空间尺度上对气候变化作出反应，对于预测未来生态系统动态和生态系统对全球变化的适应至关重要。在这项研究中，我们调查了植被的生长动态1982-2011年中国植被年生产力、季节性和最小植被覆盖量的变化及其与气候因子的关系，利用更新的全球清查、建模和制图研究（GIMMS）第三代全球卫星高级甚高分辨率辐射计（AVHRR）归一化植被指数（NDVI）数据集和气候数据从国家环境预测中心（NCEP）获得。主要调查结果如下：（1）1982 - 2011年，中国植被年平均NDVI以每年0.0006左右的速度显著增加;（2）中国植被面积中，超过33%的植被呈显著的正增长趋势，主要分布在中国中部和南部;约21%经历了显著的正增长季节性趋势，其中大部分发生在中国北方（>35 ° N）;（3）植被生长动态变化与气温和降水量显着相关（p 0.1）;（5）在植被覆盖面积中，约24%的植被覆盖面积年平均NDVI与气温呈显著相关年平均NDVI与年降水量呈显著正相关（93%为正相关，35%为负相关）的有12%（65%为正相关，35%为负相关）。植被生长动态的时空变化主要受温度控制，其次受降水控制。（6）中国西部、中部和北方地区月NDVI与前一个月的气温和降水量呈显著正相关。中国南方气候滞后超过两个月的影响可能主要是由于降水量丰富。这些研究结果表明，非常需要继续努力监测植被随时间的变化（现场和卫星观测），并在广泛的范围内，这对生态系统的管理和适应全球气候变化至关重要。如果不将这些观测结果与综合所有卫星和实地观测结果的陆地生态系统过程模型联系起来，同样很难很好地预测未来的植被生长。
Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover) in China and their relations with climatic factors during 1982-2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data acquired from the National Centers for Environmental Prediction (NCEP). Major findings are as follows: (1) annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2) of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35 degrees N); (3) changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p 0.1); (5) of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative), and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative). The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6) monthly NDVI was significantly correlated with the preceding month's temperature and precipitation in western, central and northern China. The effects of a climate lag of more than two months in southern China may be caused mainly by the abundance of precipitation. These findings suggest that continuing efforts to monitor vegetation changes (in situ and satellite observations) over time and at broad scales are greatly needed, and are critical for the management of ecosystems and adapting to global climatic changes. It is likewise difficult to predict well future vegetation growth without linking these observations to mechanistic terrestrial ecosystem processes models that integrate all the satellite and in situ observations.
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DOI：
10.1109/tgrs.2003.817274
发表时间：
2003-11
期刊：
IEEE Trans. Geosci. Remote. Sens.
影响因子：
--
作者：
M. Hall-Beyer
通讯作者：
M. Hall-Beyer
DOI：
10.1177/030913339902300207
发表时间：
1999-06
期刊：
Progress in Physical Geography
影响因子：
--
作者：
Daniel N.M. Donoghue
通讯作者：
Daniel N.M. Donoghue
DOI：
10.1007/978-0-387-35973-1_1114
发表时间：
2008
期刊：
2008 IEEE/OES 9th Working Conference on Current Measurement Technology
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.4
作者：
J. Sobrino;Y. Julien
通讯作者：
J. Sobrino;Y. Julien
影响因子：
3.4
作者：
Jue Wang;K. Price;P. Rich
通讯作者：
Jue Wang;K. Price;P. Rich