Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data

doi:10.1007/s11442-014-1134-y

Abstract

Abstract:

Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations (temperature and precipitation) on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows: (1) Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons (i.e. spring and summer) exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only. (2) The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences. (3) Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator, and then, limits the plant growth of next spring.

Key words: Qinghai Lake Basin, EVI, precipitation, temperature, correlation analysis

Wei GUO, Xiangnan NI, Duanyang JING, Shuheng LI. Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data[J].Journal of Geographical Sciences, 2014, 24(6): 1009-1021.

Figures/Tables 9

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 7

Figure 6

Figure 8

Figure 9

References 41

1	Anderson R G, Canadell, J G, Randerson J Tet al., 2010. Biophysical considerations in forestry for climate protection.Frontiers in Ecology and the Environment, 9(3): 174-182.
2	Breshears D D and Barnes F J, 1999. Interrelationships between plant functional types and soil moisture heterogeneity for semiarid landscapes within the grassland/forest continuum: A unified conceptual model.Landscape Ecology, 14(5): 465-478.
3	Carpenter C, 2005. The environmental control of plant species density on a Himalayan elevation gradient.Journal of Biogeography, 32(6): 999-1018.
4	Chamaille-Jammes S, Fritz H, Murindagomo F, 2006. Spatial patterns of the NDVI-rainfall relationship at the seasonal and inter-annual time scales in an African savanna.International Journal of Remote Sensing, 27(23): 5185-5200.
5	Chen J, Jonsson P, Tamura Met al., 2004. A simple method for reconstructing a high-quality NDVI time-series dataset based on the Savitzky-Golay filter.Remote Sensing of Environment, 91: 332-344.
6	Chen X, Hu B, Yu R, 2005. Spatial and temporal variation of phonological growing season and climate change impacts in temperate eastern China.Global Change Biology, 11: 1118-1130.
7	Chen Xiaoguang, Li Jianping, Li Zhijunet al., 2007. Vegetation coverage and its relation with climate change in Qinghai Lake area.Journal of Desert Research, 27(5): 797-804. (in Chinese)
8	Cramer W, Bondeau A, Woodward F Iet al., 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models.Global Change Biology, 7: 357-373.
9	Fay P A, Kaufman D M, Nippert J Bet al., 2008. Changes in grassland ecosystem function due to extreme rainfall events: Implications for responses to climate change.Global Change Biology, 14: 1600-1608.
10	Feng S, Tang M C, Wang D M, 1998. New evidence for the Qinghai-Xizang (Tibet) Plateau as a pilot region of climatic fluctuation in China. Chinese Science Bulletin, 43(20): 1475-1479.
11	Frolking S, Palace M W, Clark D Bet al., 2009. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on above-ground biomass and canopy structure.Journal Geophysical Research, 114.
12	Gao X, Huete A R, Ni W Get al., 2000. Optical-biophysical relationships of vegetation spectra without background contamination.Remote Sensing of Environment, 74: 609-620.
13	Gaston K J, 2000. Global patterns in biodiversity,Nature, 405(6783): 220-227.
14	Guillevic P, Koster R D, Suarez M Jet al., 2002. Influence of the inter-annual variability of vegetation on the surface energy balance: A global sensitivity study. Journal of Hydrometeorology, 3(6): 617-629.
15	Holben B N, 1986. Characteristics of maximum value composite images from temporal AVHRR.International Journal of Remote Sensing, 7: 1417-1434.
16	Huete A R, Didan K, Miura Tet al., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213.
17	Huete A R, Justice C, Liu H, 1994. Development of vegetation and soil indexes for MODIS-EOS.Remote Sensing of Environment, 49: 224-234.
18	Huete A R, Liu H Q, Batchily Ket al., 1997. A comparison of vegetation indices global set of TM images for EOS-MODIS.Remote Sensing of Environment, 59(3): 440-451.
19	Jackson R B, Randerson J T, Canadell J Get al., 2008. Protecting climate with forests.Environmental Research Letters, 3, 044006.
20	Justice C, Vermote E, Townshend Jet al., 1998. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research.IEEE Transactions on Geoscience and Remote Sensing, 36, 1228-1249.
21	Li Yungang, He Daming2009. The spatial and temporal variation of NDVI and its relationships to the climatic factors in Red River Basin.Journal of Mountain Science, 27(3): 333-340.
22	Li D, Lewis J, Rowland Jet al., 2004. Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series.Journal of Arid Environments, 59(3): 463-480.
23	Loik M E, Breshears D D, Lauenroth W Ket al., 2004. A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA.Oecologia, 141(2): 269-281.
24	Mao F, Tang S H, Sun Het al., 2008. A study of dynamic change of dry and wet climate regions in the Tibetan Plateau over the last 46 years.Chin. J. Atmos. Sci., 32: 499-507. (in Chinese)
25	McCulley R L, Burke I C, Nelson J Aet al., 2005. Regional patterns in carbon cycling across the Great Plains of North America.Ecosystems, 8: 106-121.
26	Mu Shaojie, Yang Hongfei, Li Jianlonget al., 2013. Spatio-temporal dynamics of vegetation coverage and its relationship with climate factors in Inner Mongolia, China.Journal of Geographical Science, 23(2): 231-246.
27	Nemani R, Hashimoto H, Votava Pet al., 2009. Monitoring and forecasting protected area ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS).Remote Sensing of Environment, 113, 1497-1509.
28	Nemani R R, Keeling C D, Hashimoto Het al., 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999.Science, 300(5625): 1560-1563.
29	Piao S, Friedlingstein P, Ciais Pet al., 2007. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochemical Cycles, 21(3): GB3018.
30	Piao S L, Cui M D, Chen A Pet al., 2011. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau.Agricultural and Forest Meteorology, 151, 1599-1608.
31	Piao S L, Fang J Y, Ji Wet al., 2004. Variation in a satellite-based vegetation index in relation to climate in China.Journal of Vegetation Science, 15(2): 219-226.
32	Sims D A, Rahman A F, Cordova V Det al., 2006. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems,Journal of Geophysical Research, 111: G04015.
33	Theurillat J, Guisan A, 2001. Potential impact of climate change on vegetation in the European Alps: A Review.Climatic Change, 50: 77-109.
34	Verbesselt J, Hyndman R, Newnham Get al., 2010. Detecting trend and seasonal changes in satellite image time series.Remote Sensing of Environment, 114: 106-115.
35	Wang J, Rich P M, Price K P, 2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA.International Journal of Remote Sensing, 24(11): 2345-2364.
36	Wang Junbang, Tao Jian, Li Guicaiet al., 2010. Monitoring inter-annual vegetation variation in middle Inner Mongolia through MODIS NDVI.Journal of Geo-information Science, 12(6): 835-842.
37	White A B, Kumar P, Tcheng D, 2005. A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States,Remote Sensing of Environment, 98: 1-20.
38	Xu Jianhua, 2002. Mathematical Methods in Contemporary Geography. Beijing: High Education Press. (in Chinese)
39	Xu X Q, Chen H, LEVY Jason K, 2008. Plateau vegetation cover change and its spatial and temporal characteristics of the Causes of climate warming.Chinese Science Bulletin, 54(3): 456-462.
40	Zhang Geli, Xu Xingliang, Zhou Caipinget al., 2011. Responses of grassland vegetation to climatic variations on different temporal scales in Hulun Buir Grassland in the past 30 years.Journal of Geographical Sciences, 21(4): 634-650.
41	Zhou L, Tucker C J, Kaufmann R Ket al., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999.Journal of Geophysical Research, 106(D17): 20069-20083.

[1]	MA Bin, ZHANG Bo, JIA Lige. Spatio-temporal variation in China’s climatic seasons from 1951 to 2017 [J]. Journal of Geographical Sciences, 2020, 30(9): 1387-1400.
[2]	HOU Bingfei, JIANG Chao, SUN Osbert Jianxin. Differential changes in precipitation and runoff discharge during 1958-2017 in the headwater region of Yellow River of China [J]. Journal of Geographical Sciences, 2020, 30(9): 1401-1418.
[3]	LIU Ruiqing, XU Hao, LI Jialin, PU Ruiliang, SUN Chao, CAO Luodan, JIANG Yimei, TIAN Peng, WANG Lijia, GONG Hongbo. Ecosystem service valuation of bays in East China Sea and its response to sea reclamation activities [J]. Journal of Geographical Sciences, 2020, 30(7): 1095-1116.
[4]	ZHAO Ting, BAI Hongying, YUAN Yuan, DENG Chenhui, QI Guizeng, ZHAI Danping. Spatio-temporal differentiation of climate warming (1959-2016) in the middle Qinling Mountains of China [J]. Journal of Geographical Sciences, 2020, 30(4): 657-668.
[5]	LIU Yansui, ZANG Yuzhu, YANG Yuanyuan. China’s rural revitalization and development: Theory, technology and management [J]. Journal of Geographical Sciences, 2020, 30(12): 1923-1942.
[6]	CHENG Mingyang, LI Linna, ZHOU Yang. Exploring the urban-rural development differences and influencing factors in the Huang-Huai-Hai Plain of China [J]. Journal of Geographical Sciences, 2020, 30(10): 1603-1616.
[7]	LIU Haolong, DAI Junhu, YAN Junhui, HE Fanneng, GE Quansheng, MU Chongxing. Temperature variations evidenced by records on the latest spring snowing dates in Hangzhou of eastern China during 1131-1270AD [J]. Journal of Geographical Sciences, 2020, 30(10): 1664-1680.
[8]	HAO Zhixin, WU Maowei, LIU Yang, ZHANG Xuezhen, ZHENG Jingyun. Multi-scale temperature variations and their regional differences in China during the Medieval Climate Anomaly [J]. Journal of Geographical Sciences, 2020, 30(1): 119-130.
[9]	FU Shiwen, NIE Suping, LUO Yong, CHEN Xin. Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data [J]. Journal of Geographical Sciences, 2020, 30(1): 18-36.
[10]	CHEN Shaodan, ZHANG Liping, ZHANG Yanjun, GUO Mengyao, LIU Xin. Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin, China [J]. Journal of Geographical Sciences, 2020, 30(1): 53-67.
[11]	YANG Jing, SU Kai, YE Sijing. Stability and long-range correlation of air temperature in the Heihe River Basin [J]. Journal of Geographical Sciences, 2019, 29(9): 1462-1474.
[12]	NING Lixin, CHENG Changxiu, SHEN Shi. Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China [J]. Journal of Geographical Sciences, 2019, 29(9): 1475-1490.
[13]	LI Wei, LI Xiaoyan, HUANG Yongmei, WANG Pei, ZHANG Cicheng. Spatial patch structure and adaptive strategy for desert shrub of Reaumuria soongorica in arid ecosystem of the Heihe River Basin [J]. Journal of Geographical Sciences, 2019, 29(9): 1507-1526.
[14]	TAN Xuelan, OUYANG Qiaoling, AN Yue, MI Shengyuan, JIANG Lingxiao, ZHOU Guohua. Evolution and driving forces of rural functions in urban agglomeration: A case study of the Chang-Zhu-Tan region [J]. Journal of Geographical Sciences, 2019, 29(8): 1381-1395.
[15]	Lifang CHEN, Qun DOU, Zhiming ZHANG, Zehao SHEN. Moisture content variations in soil and plant of post-fire regenerating forests in central Yunnan Plateau, Southwest China [J]. Journal of Geographical Sciences, 2019, 29(7): 1179-1192.