青藏高原植被指数最新变化特征及其与气候因子的关系

刘振元; 张杰; 陈立

doi:10.3878/j.issn.1006-9585.2017.14247

青藏高原植被指数最新变化特征及其与气候因子的关系

doi: 10.3878/j.issn.1006-9585.2017.14247

刘振元^{1, 2,},
张杰^1, ,,
陈立³

1.
南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害省部共建教育部重点实验室, 南京 210044
2.
天津市蓟州区气象局, 天津 300074
3.
福建省气候中心, 福州 350001

基金项目:

国家自然科学基金项目 91437107

国家杰出青年科学基金 41625019

详细信息

作者简介:
刘振元, 男, 1988年出生, 硕士, 主要从事气候模拟与陆气相互作用的研究。E-mail: 871523584@qq.com

通讯作者:
张杰, E-mail: gs-zhangjie@163.com, 365443382@qq.com

中图分类号: P436.22
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- 被引次数: 0
出版历程
- 收稿日期: 2014-11-20
- 网络出版日期: 2017-03-12
- 刊出日期: 2017-05-20

The Latest Change in the Qinghai-Tibetan Plateau Vegetation Index and Its Relationship with Climate Factors

Zhenyuan LIU^{1, 2
,},
Jie ZHANG^{1
, ,},
Li CHEN³

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044
2.
Jizhou District Meteorological office, Tianjin 300074
3.
Fujian Climate Center, Fuzhou 350001

Funds:

National Natural Science Foundation of China 91437107

National Science Foundations for Distinguished Young Scholars 41625019

摘要

摘要: 利用GIMMS/NDVI（全球库存模拟和影像研究/归一化植被指数，Global Inventory Modeling and MappingStudies，Normalized Difference Vegetation Index）和MODIS/NDVI遥感数据以及青藏高原6个气象代表站的站点数据，结合多种统计和计算方法，分析了青藏高原植被NDVI变化规律及其影响因子。结果表明：1982~2013年青藏高原多年平均植被NDVI的空间分布存在明显的区域差异，总体上呈从东南向西北递减的趋势，而且发现不同地区植被的时间变化规律也不尽相同。根据高原长势最好的6~9月植被NDVI进行经验正交分解，将青藏高原植被分为5个区，并进一步分析了不同分区内植被的变化规律，得出：青藏高原植被NDVI下降最明显的区域在二区的噶尔班公宽谷湖盆地地区和北羌塘高原地区，植被NDVI上升最明显的区域在四区的祁连山东部地区。为了探讨青藏高原不同分区内影响植被NDVI下降的因子，从青藏高原二区、四区、五区各选取NDVI处于下降趋势的两个代表站点。研究分析了各个站点植被NDVI与降水量、平均气温、平均最低气温、平均最高气温、日照百分率5个气象因子的关系，得出：在高原二区日照强度是其它分区的两倍左右，而降水量相对较少导致植被NDVI降低。高原四区由于降水量小、温度高、日照强，导致植被NDVI处于下降趋势；在青藏高原五区虽然降水充足，但日照较弱，限制了植被的正常成长导致NDVI处于下降趋势中；其结果为高原植被退化机制研究及高原植被对大气反馈等奠定了基础。
- 青藏高原 /
- 植被生长状况 /
- NDVI /
- 气候变化
Abstract: Based on the remote sensing data of GIMMS(Global Inventory Modeling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) and MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, as well as data collected at six meteorological stations in the Qinghai-Tibetan Plateau, the authors discussed the spatio-temporal variation of the vegetation in the Qinghai-Tibetan Plateau and its influencing factors using statistical and calculation methods. The results show some obvious differences in the spatial distribution of annual mean Qinghai-Tibetan Plateau vegetation NDVI from 1982 to 2013; the NDVI over all decreased from southeast to northwest. In addition, temporal variations of the vegetation index in different areas were not exactly the same. Using EOF (Empirical Orthogonal Function) analysis, the authors analyzed the regular changing patterns of vegetation coverage over five subregions, which were divided based on NDVI during the growing season from June to September. The results indicate that the largest decrease in NDVI over the Qinghai-Tibetan Plateau occurred in the area of Gar-Lake Bangong Gully and the northern Qiangtang Plateau, while increases in NDVI over the Qinghai-Tibetan Plateau was found in the eastern Qilian Mountain. In order to explore the factors affecting the decline of NDVI in different subregions, two representative stations were selected in subregions Ⅱ, Ⅳ, and Ⅴ of the Qinghai-Tibet Plateau, where the NDVI showed a declining trend. The authors also discussed the relationship between NDVI and total precipitation, average temperature, mean maximum temperature, and percentage of sunshine duration at each station. Preliminary results show that in subregion Ⅱ of the Qinghai-Tibet Plateau, the sunshine duration was double that in other subregions while precipitation was relatively low, resulting in vegetation degradation. The vegetation degradation in subregion Ⅳ was attributed to limited precipitation, high temperature, and strong sunshine. In subregion Ⅴ, precipitation was sufficient but sunshine was weak, which also caused the vegetation degradation. The above results lay a profound foundation for the mechanism study of vegetation degradation in the Qinghai-Tibetan Plateau and its feedback to the atmosphere.
- Qinghai-Tibetan Plateau /
- Vegetation growth status /
- Normalized difference vegetation index (NDVI) /
- Climate Change

HTML全文

图 1 柴达木盆地NDVI的时间序列

Figure 1. Changes in NDVI from 1982 to 2013 in the Qaidam basin

下载: 全尺寸图片幻灯片

图 2 1982~2013年6~9月青藏高原平均NDVI分布

Figure 2. Average NDVI distribution over the Qinghai-Tibetan Plateau from Jun to Sep during 1982–2013

下载: 全尺寸图片幻灯片

图 3 1982~2013年（a）6月、（b）7月、（c）8月、（d）9月青藏高原植被NDVI趋势相关系数分布（相关系数大于0.32超过90%的信度检验，大于0.38超过95%的信度检验）

Figure 3. Distributions of trend correlation coefficient of NDVI over the Qinghai-Tibetan Plateau in (a) Jun, (b) Jul, (c)Aug, and (d) Sep during 1982–2013 (correlation coefficient greater than 0.32 exceeds the 90% confidence level, and that greater than 0.38 exceeds the 95% confidence level)

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图 4 青藏高原植被分区图青藏高原植被分区图

Figure 4. Subregions over the Qinghai-Tibetan Plateau based on NDVI

下载: 全尺寸图片幻灯片

图 5 青藏高原各分区内6~9月平均NDVI时间序列

Figure 5. Time sequences of average NDVI in each subregion over the Qinghai-Tibetan Plateau from Jun to Sep

下载: 全尺寸图片幻灯片

图 6 1982~2013年6~9月青藏高原植被NDVI（a）下降和（b）上升区域分布（阴影为趋势相关系数）

Figure 6. NDVI distributions in the regions with (a) decreasing NDVI and (b) increasing NDVI over the Qinghai-Tibetan Plateau from Jun to Sep during 1982–2013 (shadings denote trend correlation coefficients)

下载: 全尺寸图片幻灯片

表 1 青藏高原逐月GIMMS NDVI和MODIS NDVI之间的回归模型

Table 1. Regression model between monthly GIMMS NDVI and MODIS NDVI over the Qinghai-Tibetan Plateau

月份	回归模型	决定系数R²
5	y=0.724x+0.008	0.916*
6	y=0.828x+0.014	0.887*
7	y=0.836x+0.002	0.905*
8	y=0.849x+0.019	0.837*
9	y=0.801x+0.025	0.919*
10	y=0.764x+0.023	0.839*
注：x、y分别代表MODIS NDVI和GIMMS NDVI。 *表示显著性系数P＜0.05。

下载: 导出CSV

表 2 青藏高原各分区代表站植被NDVI和5个气象要素的相关系数

Table 2. Correlation coefficients between NDVI and five meteorological elements at the representative stations of each subregion over the Qinghai-Tibetan Plateau

月份	地区	与NDVI的相关系数
月份	地区	降水量	平均气温	平均最高气温	平均最低气温	日照百分率
6月	二区	–0.3731^*	–0.2876	–0.3976^*	0.2841	0.2962
	四区	0.5042^**	–0.4657^**	–0.4355^*	–0.4651^*	–0.3524
	五区	–0.4326^*	0.4468^*	0.5037^**	0.4238^*	0.5106^**
7月	二区	–0.2184	–0.2126	–0.3640^*	–0.1641	0.2796
	四区	0.2327	–0.4683^**	–0.3768^*	–0.3801^*	0.4973^**
	五区	0.4233^*	–0.3587	0.2001	–0.4564^*	–0.4522*
8月	二区	–0.1517	0.1908	0.1513	0.3591	–0.2657
	四区	0.4711^**	–0.4136*	–0.4055^*	–0.1294	0.3893^*
	五区	0.4113^*	–0.2014	–0.1055	–0.3257	–0.1175
9月	二区	–0.3649^*	0.2435	–0.3296	–0.3054	0.3578
	四区	0.4956^**	–0.4377^*	–0.3921^*	0.1324	–0.5067^**
	五区	0.4632^**	0.3489	0.4793^**	0.3068	–0.4762^**
表示P≤0.05具有统计学意义。 *表示P≤0.01具有高度的统计学意义。

下载: 导出CSV

表 3 青藏高原各分区代表站植被NDVI和5个气象要素去趋势以后的相关系数

Table 3. Correlation coefficient between detrended NDVI and five meteorological factors at the representative stations over the Qinghai-Tibetan Plateau

月份	地区	去趋势后的相关系数
月份	地区	降水量	平均气温	平均最高气温	平均最低气温	日照百分率
6月	二区	–0.3671^*	–0.2851	–0.3014	0.1093	0.0314
	四区	0.3324	–0.4127^*	–0.3830^*	–0.3498	–0.3147
	五区	–0.2036	0.4537^*	0.4762^**	0.2163	0.5011^**
7月	二区	–0.1576	–0.0479	–0.0772	–0.1532	0.2234
	四区	0.1225	–0.3749*	–0.2396	–0.3128	0.4067^*
	五区	0.3985	–0.3174	0.2051	–0.4019^*	–0.3952^*
8月	二区	–0.0840	0.1538	0.1414	0.3425	–0.2575
	四区	0.4001^*	–0.2835	–0.3961^*	–0.0654	0.3628^*
	五区	0.3936^*	–0.0864	–0.0756	–0.1958	–0.3279
9月	二区	–0.3739^*	0.1615	–0.4123	–0.3174	0.3624^*
	四区	0.4639^**	–0.4128^*	–0.3629^*	0.2061	–0.4934^**
	五区	0.4529^*	0.3185	0.4826^**	0.4965^**	–0.4625^**
表示P≤0.05具有统计学意义。 *表示P≤0.01具有高度的统计学意义。

下载: 导出CSV

表 4 青藏高原各分区代表站气象要素的月平均值

Table 4. Meteorological elements on average at the representative stations over the Qinghai-Tibetan Plateau

月份	地区	降水量/mm	平均气温/℃	平均最高气温/℃	平均最低气温/℃	日照百分率
6月	二区	13.1	10.09	17.87	2.35	76.96%
	四区	27.6	12.14	20.31	7.23	61.46%
	五区	119.8	17.66	25.10	12.39	38.66%
7月	二区	36.3	13.45	20.23	6.63	74.68%
	四区	28.5	16.45	23.87	10.56	62.84%
	五区	140.5	17.79	24.82	13.16	38.32%
8月	二区	40.3	12.59	19.43	6.34	77.04%
	四区	15.5	15.51	25.26	8.45	67.76%
	五区	104.8	19.18	25.38	12.59	40.1%
9月	二区	13.4	8.94	15.98	1.69	80.28%
	四区	9.0	10.78	18.68	3.95	71.44%
	五区	95.2	15.22	22.71	10.64	43.02%

下载: 导出CSV

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