Assessing Vegetation Response to Meteorological Drought in Tibet Autonomous Region Using Vegetation Condition Index

Wang Yuanyuan; Zhaxi Yangzong

doi:10.11898/1001-7313.20160406

Vol.27, NO.4, 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(4): 435-444

Wang Yuanyuan, Zhaxi Yangzong. Assessing vegetation response to meteorological drought in Tibet autonomous region using vegetation condition index. J Appl Meteor Sci, 2016, 27(4): 435-444. DOI: 10.11898/1001-7313.20160406.

Citation:

Wang Yuanyuan, Zhaxi Yangzong. Assessing vegetation response to meteorological drought in Tibet autonomous region using vegetation condition index. J Appl Meteor Sci, 2016, 27(4): 435-444. DOI: 10.11898/1001-7313.20160406.

Citation:

PDF( 3105 KB)

Assessing Vegetation Response to Meteorological Drought in Tibet Autonomous Region Using Vegetation Condition Index

DOI: 10.11898/1001-7313.20160406

Wang Yuanyuan^{1
,
,},
Zhaxi Yangzong²

1.
National Satellite Meteorological Center, Beijing 100081
2.
Tibetan Institute of Atmospheric Environment and Science, Lhasa 850001

Received Date: 2015-11-18
Rev Recd Date: 2016-03-24
Publish Date: 2016-07-31

Abstract

Abstract

Tibetan plateau, as the third pole, is influenced by global climate change deeply. According to the 5th IPCC assessment report, temperature on the Tibetan Plateau is rising quickly, posing serious risks to agriculture, hydrological systems and so on. Drought is becoming a main hazard to agricultural production in Tibet, and therefore it's very essential to apply effective drought monitoring techniques in agriculture management in response to climate change. Although meteorological drought indices (such as standard precipitation index, SPI) are useful in drought measurement, they often have limited spatial resolution since they rely on in situ data. Satellites-based drought indices (such as vegetation condition index, VCI) can provide drought information over large areas at a higher spatial resolution, but in a different way from station-based meteorological drought indices. It has been recognized that the existing satellite-based drought indices are more associated with agricultural drought (e.g., vegetation health, crop yield, soil moisture, etc.), and the response of vegetation to meteorological drought (precipitation deficits) varies depending on the seasonal timing, land cover type, climate, soil properties, irrigation, and other factors.Correlation coefficients between VCI and SPI at different time scales for 30 meteorological stations in Tibet during 2000-2014 are calculated. First, the time scale of SPI that is most correlated with VCI is determined. Then, climatic and environmental factors are investigated to explain the spatial variation of this correlation. With considerations of inter-correlations among environmental factors, two preconditions are recognized as favorable for a strong correlation between VCI and SPI, and regions where vegetation responds to meteorological drought obviously are identified. Results are as follows. Firstly, correlations between VCI and SPI are time scale dependent, and the lag between the occurrence of precipitation and the vegetation response is about 12 weeks in Tibet. Secondly, there are obvious spatial variations in terms of the vegetation response to meteorological drought. Insensitive vegetation response is often associated with extremely dry or wet climate, forested land cover, low annual NDVI value, low multi-annual NDVI fluctuations, and water sources other than precipitation (e.g., snowmelt, irrigation). Thirdly, according to the climatic and environmental factors, vegetation in the middle southern part of Tibet responds to meteorological drought obviously, including Lhasa region, the northern part of Shannan, the eastern part of Rikaze, the middle and southwestern part of Naqu, and the southeastern part of Ali.
- vegetation condition index;
- standard precipitation index;
- Tibet Autonomous Region;
- drought

FullText(HTML)

References(38)

Figures and Tables

Fig. 1 Administrative division, land cover type and locations of meteorological stations in Tibet Autonomous Region

DownLoad: Full-Size Img PowerPoint

Fig. 2 Illustration of the NDVI time series before and after data smoothing with the weighted least-square method (window size is 5)

DownLoad: Full-Size Img PowerPoint

Fig. 3 Spatial pattern of correlation coefficients between VCI and 12-week SPI

DownLoad: Full-Size Img PowerPoint

图 4 气候干燥度对VCI和12周时间尺度SPI之间相关性的影响

(已去除气候干燥度不大于10的2个站)

Fig. 4 The impact of climatic aridity index on the strength of the relationship between VCI and 12-week SPI

(only considering stations with aridity index greater than 10)

DownLoad: Full-Size Img PowerPoint

图 5 雨水利用效率与VCI和12周时间尺度SPI相关系数散点图

(仅考虑气候干燥度在30~50之间且周围主要土地覆盖为非森林的站点)

Fig. 5 The impact of rain use efficiency on the strength of the relationship between VCI and 12-week SPI

(only considering non-forested stations with aridity index greater than 30 and less than 50)

DownLoad: Full-Size Img PowerPoint

图 6 NDVI多年变化标准差与VCI和12周时间尺度SPI相关系数散点图

(已去除气候过度干燥或过度湿润、土地覆盖为森林类型或雨水利用效率高的站点)

Fig. 6 The impact of NDVI standard deviations on the strength of the relationship between VCI and 12-week SPI

(those stations are not considered if the aridity index is high or low, rain use efficiency is high or land cover is forest)

DownLoad: Full-Size Img PowerPoint

Fig. 7 The flowchart of identifying regions where vegetation responds to meteorological drought significantly

DownLoad: Full-Size Img PowerPoint

Fig. 8 Regions where vegetation responds to meteorological drought significantly

DownLoad: Full-Size Img PowerPoint

Table 1 Correlation coefficients between VCI and SPI at different time scales

时间尺度/周	站点平均相关系数
4	0.24
8	0.32
12	0.34
16	0.33
24	0.32
32	0.33
40	0.32

DownLoad: Download CSV

Table 2 Correlation coefficients between VCI and 12-week SPI and environmental factors extracted for each meteorological station

站点	区域	相关系数	气候干燥度	站点周围主要土地覆盖	NDVI多年变化标准差	NDVI多年平均值	雨水利用效率
改则	阿里	0.41	16.39	裸地/荒漠	0.01	0.10	5.29
普兰	阿里	0.24	10.02	草地	0.00	0.14	8.03
狮泉河	阿里	0.04	8.56	裸地/荒漠	0.00	0.06	6.52
泽当	山南	0.46	20.18	农田	0.02	0.41	7.64
错那	山南	-0.05	36.51	草地	0.02	0.47	13.53
贡嘎	山南	0.39	21.10	草地	0.01	0.32	5.27
加查	山南	0.70	26.74	灌丛	0.04	0.43	6.26
帕里	日喀则	0.17	37.05	草地	0.02	0.39	9.16
江孜	日喀则	0.29	18.33	农田	0.01	0.41	10.16
定日	日喀则	0.42	22.00	灌丛	0.02	0.23	5.75
南木林	日喀则	0.34	28.25	草地	0.01	0.32	4.52
聂拉木	日喀则	0.09	40.41	草地	0.02	0.49	8.75
那曲	那曲	0.37	47.10	草地	0.04	0.28	4.99
申扎	那曲	0.39	27.38	草地	0.01	0.16	4.47
安多	那曲	0.16	52.85	草地	0.01	0.28	5.36
嘉黎	那曲	-0.01	66.72	草地	0.02	0.44	5.74
比如	那曲	0.35	43.44	草地	0.02	0.51	7.75
索县	那曲	0.28	48.74	草地	0.01	0.39	6.31
察隅	林芝	0.11	34.25	森林	0.03	0.74	9.03
波密	林芝	-0.09	44.84	森林	0.05	0.59	11.00
拉萨	拉萨	0.37	23.30	城市	0.02	0.24	4.53
当雄	拉萨	0.63	38.75	草地	0.04	0.39	7.83
尼木	拉萨	0.45	19.97	草地	0.01	0.30	6.52
丁青	昌都	0.39	42.74	草地	0.02	0.39	4.90
昌都	昌都	0.51	26.98	城市	0.02	0.42	7.92
类乌齐	昌都	0.40	45.50	草地	0.02	0.48	7.34
洛隆	昌都	0.59	26.65	草地	0.03	0.36	7.65
八宿	昌都	0.48	12.26	灌丛	0.02	0.32	11.20
左贡	昌都	0.42	30.33	灌丛	0.01	0.42	6.47
芒康	昌都	0.48	40.84	草地	0.02	0.39	5.36

DownLoad: Download CSV

References

[1]	Kang S, Xu Y, You Q, et al.Review of climate and cryospheric change in the Tibetan Plateau.Environ Res Lett, 2010, 5:1-8. http://www.doc88.com/p-574468440887.html
[2]	徐祥德, 陈联寿.青藏高原大气科学试验研究进展.应用气象学报, 2006, 17(6):756-772. doi: 10.11898/1001-7313.20060613
[3]	万玮, 肖鹏峰, 冯学智, 等.卫星遥感监测近30年来青藏高原湖泊变化.科学通报, 2014, 59(8):701-714. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201408008.htm
[4]	Parry M L, Canziani O F, Palutikof J P, et al.Climate Change 2007:Impacts, Adaptation and Vulnerability:Working GroupⅡ Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press, 2007.
[5]	高懋芳, 邱建军.青藏高原主要自然灾害特点及分布规律研究.干旱区资源与环境, 2011, 25(8):101-106. http://www.cnki.com.cn/Article/CJFDTOTAL-GHZH201108019.htm
[6]	柳锦宝, 何政伟, 段英杰.MODIS数据支持下的西藏干旱遥感监测.干旱区资源与环境, 2013, 27(6):134-139. http://www.cnki.com.cn/Article/CJFDTOTAL-DIQU201211033.htm
[7]	扎西央宗, 陈军, 李林, 等.基于MODIS数据的西藏干旱自动监测系统实现的关键技术研究.冰川冻土, 2014, 36(5):1245-1250. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201405023.htm
[8]	于敏, 程明虎.基于NDVI-Ts特征空间的黑龙江省干旱监测.应用气象学报, 2010, 21(2):221-228. doi: 10.11898/1001-7313.20100212
[9]	孙灏, 陈云浩, 孙洪泉.典型农业干旱遥感监测指数的比较及分类体系.农业工程学报, 2012, 28(14):147-154. doi: 10.3969/j.issn.1002-6819.2012.14.023
[10]	Quiring S M, Ganesh S.Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas.Agricultural and Forest Meteorology, 2010, 150:330-339. doi: 10.1016/j.agrformet.2009.11.015
[11]	Keyantash J, Dracup J A.The Quantification of drought:An evaluation of drought indices.BAMS, 2002, 8:1167-1180. http://www.southwestclimatechange.org/node/464
[12]	杨绍锷, 闫娜娜, 吴炳方.农业干旱遥感监测研究进展.遥感信息, 2010(1):103-109. http://www.cnki.com.cn/Article/CJFDTOTAL-CXYY201632006.htm
[13]	陈怀亮, 李颖, 张红卫.农作物长势遥感监测业务化应用与研究进展.气象与环境科学, 2015, 38(1):95-102. http://www.cnki.com.cn/Article/CJFDTOTAL-HNQX201501013.htm
[14]	张强, 张良, 崔显成, 等.干旱监测与评价技术的发展及其科学挑战.地球科学进展, 2011, 26(7):763-778. http://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201107013.htm
[15]	Vicente-Serrano S M.Evaluating the impact of drought using remote sensing in a Mediterranean, semi-arid region.Natural Hazards, 2007, 40:173-208. doi: 10.1007/s11069-006-0009-7
[16]	Bhuiyan C, Singh R P, Kogan F N.Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data.International Journal of Applied Earth Observation and Geoinformation, 2006, 8:289-302. doi: 10.1016/j.jag.2006.03.002
[17]	Ji L, Peters A.Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices.Remote Sens Environ, 2003, 87:85-89. doi: 10.1016/S0034-4257(03)00174-3
[18]	沙莎, 郭铌, 李耀辉, 等.植被状态指数VCI与几种气象干旱指数的对比——以河南省为例.冰川冻土, 2013, 35(4):990-998. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201304023.htm
[19]	Bayarjargal Y, Karnieli A, Bayasgalan M, et al.A comparative study of NOAA AVHRR derived drought indices using change vector analysis.Remote Sens Environ, 2006, 105:9-22. doi: 10.1016/j.rse.2006.06.003
[20]	Wang J, Price K P, Rich P M.Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains.Int J Remote Sens, 2001, 22:3827-3844. doi: 10.1080/01431160010007033
[21]	Singh R P, Roy S, Kogan F N.Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India.Int J Remote Sens, 2003, 24:4393-4402. doi: 10.1080/0143116031000084323
[22]	Kogan F N.Global drought watch from space.Bull Amer Meteor Soc, 1997, 78(4):621-636. doi: 10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
[23]	Gitelson A A, Kogan F N, Zakarin E, et al.Using AVHRR data for quantitative estimation of vegetation conditions:Calibration and validation.Advances in Space Research, 1998, 22:673-676. doi: 10.1016/S0273-1177(97)01129-0
[24]	Kogan F N.Remote sensing of weather impacts on vegetation in nonhomgeneous areas.Int J Remote Sens, 1990, 11:1405-1419. doi: 10.1080/01431169008955102
[25]	陈怀亮, 徐祥德, 杜子璇, 等.黄淮海地区植被活动对气候变化的响应特征.应用气象学报, 2009, 20(5):513-520. doi: 10.11898/1001-7313.20090501
[26]	Piao S, Fang J, Zhou L, et al.Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999.J Geophys Res, 2003, 108 (D14), 4401. doi: 10.1029/2002JD002848
[27]	古格·其美多吉.西藏地理.北京:北京师范大学出版社, 2013.
[28]	McKee T B, Doesken N J, Kleist J.The Relationship of Drought Frequency and Duration to Time Scales.Eighth Conference on Applied Climatology, 1993. http://www.southwestclimatechange.org/node/910
[29]	谢五三, 王胜, 唐为安, 等.干旱指数在淮河流域的适用性对比.应用气象学报, 2014, 25(2):176-184. doi: 10.11898/1001-7313.20140207
[30]	GB/T20481-2006.气象干旱等级.北京:中国标准出版社, 2006.
[31]	Swets D L, Reed B C, Rowland J R, et al.A Weighted Least-Squares Approach to Temporal Smoothing of NDVI. Proceedings of the 1999 ASPRS Annual Conference, 1999. http://www.statsref.com/HTML/index.html?least_squares.html
[32]	孟猛, 倪健, 张治国.地理生态学的干燥度指数及其应用评述.植物生态学报, 2004, 28(6):853-861. doi: 10.17521/cjpe.2004.0111
[33]	成林, 方文松.气候变化对雨养冬小麦水分利用效率的影响估算.应用气象学报, 2015, 26(3):300-310. doi: 10.11898/1001-7313.20150305
[34]	Hijmans R J, Cameron S E, Parra J L, et al.Very high resolution interpolated climate surfaces for global land areas.International Journal of Climatology, 2005, 25:1965-1978, doi: 10.1002/joc.1276.
[35]	Fensholt R, Rasmussen K.Analysis of trends in the Sahelian rain-use efficiency using GIMMS NDVI, RFE and GPCP rainfall data.Remote Sens Environ, 2011, 115(2):438-451. doi: 10.1016/j.rse.2010.09.014
[36]	于敏, 王春丽.不同卫星遥感干旱指数在黑龙江的对比应.应用气象学报, 2011, 22(2):221-231. doi: 10.11898/1001-7313.20110211
[37]	Price J.On the analysis of thermal infrared imagery:The limited utility of apparent thermal inertia.Remote Sens Environ, 1985, 18(1):59-73. doi: 10.1016/0034-4257(85)90038-0
[38]	Wagle P, Xiao X M, Torn M S, et al.Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought.Remote Sens Environ, 2014, 152:1-14. doi: 10.1016/j.rse.2014.05.010