Analysis of vegetation changes and dominant factors on the Qinghai-Tibet Plateau, China

doi:10.3724/SP.J.1226.2019.00150.

Abstract

Abstract:

This research was undertaken to clarify the characteristics of vegetation change and its main influencing factors on the Qinghai-Tibet Plateau. Using the greenness rate of change (GRC) and correlation factors, we analyzed the trend of vegetation change and its dominant factors from 2000 to 2015. The results indicate that the vegetation tended to improve from 2000 to 2015 on the Qinghai-Tibet Plateau, with the improved area accounting for 39.93% of the total; and the degraded area accounting for 19.32%. The areas of degraded vegetation are mainly concentrated in the low-relief and intermediate-relief mountains of the high-altitude and extremely high-altitude areas on the Qinghai-Tibet Plateau, as the vegetation characteristics are impacted by the terrain. Temperature and precipitation have obvious response mechanisms to vegetation growth, but the effects of precipitation and temperature on vegetation degradation are not significant over a short time frame. Overgrazing and population growth are the dominant factors of vegetation degradation on the Qinghai-Tibet Plateau.

Key words: Qinghai-Tibet Plateau, remote sensing, vegetation activity, degraded, dominant factors

HongWei Wang,Yuan Qi,ChunLin Huang,XiaoYing Li,XiaoHong Deng,JinLong Zhang. Analysis of vegetation changes and dominant factors on the Qinghai-Tibet Plateau, China[J].Sciences in Cold and Arid Regions, 2019, 11(2): 150-158.

Figures/Tables 9

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Table 1

Comparison of MODIS NDVI trend on the Qinghai-Tibet Plateau (2000?2005, 2005?2010, 2010?2015, 2000?2015)"

NDVI trends	Degree	Statistical indicators	2000?2005	2005?2010	2010?2015	2000?2015
$Θ$ _slope $≤$ ?0.0091	Greatly degraded	Area (km²)	221,859	297,241	661,191	19,835
$Θ$ _slope $≤$ ?0.0091	Greatly degraded	Area percentage	7.17%	9.60%	21.36%	0.64%
?0.0090 $≤ Θ$ _slope $≤$ ?0.0046	Slightly degraded	Area (km²)	189,560	296,979	452,265	85,238
?0.0090 $≤ Θ$ _slope $≤$ ?0.0046	Slightly degraded	Area percentage	6.12%	9.59%	14.61%	2.75%
?0.0045 $≤$ Θ _slope $≤$ ?0.0010	Significantly degraded	Area (km²)	345,325	608,095	603,892	492,964
?0.0045 $≤$ Θ _slope $≤$ ?0.0010	Significantly degraded	Area percentage	11.16%	19.64%	19.51%	15.93%
?0.0009 $≤$ Θ _slope $≤$ 0.0009	Basically unchanged	Area (km²)	478,519	706,871	559,710	1,261,121
?0.0009 $≤$ Θ _slope $≤$ 0.0009	Basically unchanged	Area percentage	15.46%	22.84%	18.08%	40.74%
0.0010 $≤$ Θ _slope $≤$ 0.0045	Significantly improved	Area (km²)	747,150	601,379	457,228	867,716
0.0010 $≤$ Θ _slope $≤$ 0.0045	Significantly improved	Area percentage	24.14%	19.43%	14.77%	28.03%
0.0046 $≤$ Θ _slope $≤$ 0.0090	Slightly improved	Area (km²)	433,155	304,178	183,245	239,382
0.0046 $≤$ Θ _slope $≤$ 0.0090	Slightly improved	Area percentage	13.99%	9.83%	5.92%	7.73%
0.0091 $≤$ Θ _slope	Greatly improved	Area (km²)	679,854	280,679	177,891	129,166
0.0091 $≤$ Θ _slope	Greatly improved	Area percentage	21.96%	9.07%	5.75%	4.17%

Table 1

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References 0

	Anyamba A , Tucker CJ , 2012. Historical Perspective of AVHRR NDVI and Vegetation Drought Monitoring. Remote Sens Drought: Innovative Monitoring Approaches. Boca Raton: CRC Press, pp. 23−49.
	Croux C , Dehon C , 2010. Influence functions of the Spearman and Kendall correlation measures. Statistical Methods & Applications，. 19(4):497−515. DOI: 10.1007/s10260-010-0142-z. doi: 10.1007/s10260-010-0142-z
	Dardel C , Kergoat L , Hiernaux P , et al . , 2014. Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sensing of Environment, 140: 350−364. DOI: 10.1016/j.rse.2013.09.011. doi: 10.1016/j.rse.2013.09.011
	Fensholt R , Rasmussen K , Nielsen TT , et al . , 2009. Evaluation of earth observation based long term vegetation trends-Intercom paring NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9): 1886−1898. DOI: 10.1016/j.rse.2009.04.004. doi: 10.1016/j.rse.2009.04.004
	Godinez-Alvarez H , Herrick JE , Mattocks M , et al . , 2009. Comparison of three vegetation monitoring methods: their relative utility for ecological assessment and monitoring. Ecological Indicators, 9(5): 1001−1008. DOI: 10.1016/j.ecolind.2008.11.011. doi: 10.1016/j.ecolind.2008.11.011
	Hu YF , Jiang SL , Yuan S , et al . , 2017. Changes in soil organic carbon and its active fractions in different desertification stages of alpine-cold grassland in the eastern Qinghai−Tibet Plateau. Environmental Earth Sciences, 76(9): 348. DOI: 10.1007/s12665-017-6684-8. doi: 10.1007/s12665-017-6684-8
	Huete A , Didan K , Miura T , et al . , 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1−2): 195−213. DOI: 10.1016/S0034-4257(02)00096-2. doi: 10.1016/S0034-4257(02)00096-2
	Jamali S , Seaquist J , Eklundh L , et al . , 2014. Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote Sensing of Environment, 141: 79−89. DOI: 10.1016/j.rse.2013.10.019. doi: 10.1016/j.rse.2013.10.019
	Li F , Zeng Y , Li XS , et al . , 2014. Remote sensing based monitoring of interannual variations in vegetation activity in China from 1982 to 2009. Science China Earth Sciences, 57(8): 1800−1806. DOI: 10.1007/s11430-014-4883-7. doi: 10.1007/s11430-014-4883-7
	Li G , Sun WL , Zhang H , et al . , 2014. Balance between actual number of livestock and livestock carrying capacity of grassland after added forage of straw based on remote sensing in Tibetan Plateau. Transaction of the Chinese Society of Agricultural Engineering, 30(17): 200−211. DOI:10.39 69/j.issn.1002-6819.2014.17.026. doi: 10.39 69/j.issn.1002-6819.2014.17.026
	Moss RH , Edmonds JA , Hibbard KA , et al . , 2010. The next generation of scenarios for climate change research and assessment. Nature, 463(7282): 747. DOI: 10.1038/nature 08823. doi: 10.1038/nature 08823
	Nan X , Li AN , Chen Y , et al . , 2016. Design and compilation of Digital Mountain map of china (1:6700000) in vertical layout. Remote Sensing Technology and Application, 31(3): 451−458. DOI: 10.11873/j.issn.1004-0323.2016.3.0454. doi: 10.11873/j.issn.1004-0323.2016.3.0454
	Otto M , Höpfner C , Curio J , et al . , 2016. Assessing vegetation response to precipitation in northwest Morocco during the last decade: an application of MODIS NDVI and high resolution reanalysis data. Theoretical and Applied Climatology, 123(1−2): 23−41. DOI: 10.1007/s00704-014-1344-3. doi: 10.1007/s00704-014-1344-3
	Peng J , Liu ZH , Liu YH , et al . , 2012. Trend analysis of vegetation dynamics in Qinghai−Tibet Plateau using Hurst Exponent. Ecological Indicators, 14(1): 28−39. DOI: 10.1016/j.ecolind.2011.08.011. doi: 10.1016/j.ecolind.2011.08.011
	Pinzon JE , Tucker CJ , 2014. A non-stationary 1981−2012 AVHRR NDVI3g time series. Remote Sensing, 6(8): 6929−6960. DOI: 10.3390/rs6086929. doi: 10.3390/rs6086929
	Qian S , Mao LX , Hou YY , et al . , 2007. Livestock carrying capacity and balance between carrying capacity of grassland with added forage and actual livestock in the Qinghai-Tibet Plateau. Journal of Natural Resources, 22(3): 389−397. DOI: 1000-3037(2007)03-0389-10. doi: 1000-3037(2007)03-0389-10
	Smith AMS , Kolden CA , Tinkham WT , et al . , 2014. Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems. Remote Sensing of Environment, 154: 322−337. DOI: 10.1016/j.rse.2014.03.038. doi: 10.1016/j.rse.2014.03.038
	Stow D , Daeschner S , Hope A , et al . , 2003. Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s. International Journal of Remote Sensing, 24(5): 1111−1117. DOI: 10.1080/0143116021000020144. doi: 10.1080/0143116021000020144
	Stow DA , Hope A , McGuire D , et al . , 2004. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sensing of Environment, 89(3): 281−308. DOI: 10.1016/j.rse.2003.10.018. doi: 10.1016/j.rse.2003.10.018
	Sun HL , Zheng D , Yao TD , et al . , 2012. Protection and construction of the national ecological security shelter zone on Tibetan Plateau. Acta Geographica Sinica, 67(1): 3−12.
	Tian F , Brandt M , Liu YY , et al . , 2016. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment, 177: 265−276. DOI: 10.1016/j.rse.2016.02.056. doi: 10.1016/j.rse.2016.02.056
	Tian F , Fensholt R , Verbesselt J , et al . , 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment, 163: 326−340. DOI: 10.1016/j.rse.2015.03.031. doi: 10.1016/j.rse.2015.03.031
	Wang GX , Cheng GD , Shen YP ， 2002. Soil organic carbon pool of grasslands on the Tibetan Plateau and its global implication. Journal of Glaciology and Geocryology, 24(6): 693−700. DOI: 1000-0240(2002)06-0693-08. doi: 1000-0240(2002)06-0693-08
	Wang YS , Lou ZP , Sun CC , et al . , 2008. Ecological environment changes in Daya Bay, China, from 1982 to 2004. Marine Pollution Bulletin, 56(11): 1871−1879. DOI: 10.1016/j.marpolbul.2008.07.017. doi: 10.1016/j.marpolbul.2008.07.017
	Wang ZQ , Zhang YZ , Yang Y , et al . , 2016. Quantitative assess the driving forces on the grassland degradation in the Qinghai−Tibet Plateau, in China. Ecological Informatics, 33: 32−44. DOI: 10.1016/j.ecoinf.2016.03.006. doi: 10.1016/j.ecoinf.2016.03.006
	Waylen P , Southworth J , Gibbes C , et al . , 2014. Time series analysis of land cover change: Developing statistical tools to determine significance of land cover changes in persistence analyses. Remote Sensing, 6(5): 4473−4497. DOI: 10.3390/rs6054473. doi: 10.3390/rs6054473
	Xu G , Zhang HF , Chen BZ , et al . , 2014. Changes in vegetation growth dynamics and relations with climate over China’s landmass from 1982 to 2011. Remote Sensing, 6(4): 3263−3283. DOI: 10.3390/rs6043263. doi: 10.3390/rs6043263
	Yao TD , Zhu LP , 2006. The response of environmental changes on Tibetan Plateau to global changes and adaptation strategy. Advances in Earth Science, 21(5): 459−464. DOI: 1001-8166(2006)05-0459-06. doi: 1001-8166(2006)05-0459-06
	Zhang L , Guo HD , Wang CZ , et al . , 2014. The long-term trends (1982−2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau. Environmental Earth Sciences, 72(6): 1827−1841. DOI: 10.1007/s12665-014-3092-1. doi: 10.1007/s12665-014-3092-1
	Zhang TT , Zeng SL , Gao Y , et al . , 2011. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecological Indicators, 11(6): 1552−1562. DOI: 10.1016/j.eco lind.2011.03. 025. doi: 10.1016/j.eco lind.2011.03. 025
	Zhu JF , Zhou Y , Wang SX , et al . , 2015. Multicriteria decision analysis for monitoring ecosystem service function of the Three-River Headwaters region of the Qinghai-Tibet Plateau, China. Environmental Monitoring and Assessment, 187(6): 355. DOI: 10.1007/s10661-015-4523-5. doi: 10.1007/s10661-015-4523-5

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