Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Luo, Nana; Mao, Dehua; Wen, Bo; Liu, Xingtu

doi:10.3390/land9030090

Cited by 57 publications

(44 citation statements)

References 45 publications

(49 reference statements)

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“…These results confirm that the climate gradually developed a warmer and more humid pattern in Xinjiang, which confirms previous claims [ 24 , 39 , 53 , 54 , 55 ]. Similar studies have reported a significant wetting tendency in northern Xinjiang [ 4 , 56 ], which is consistent with the trend observed in this study. However, unlike some studies revealed a trend of dryness in Xinjiang from 2000–2015 [ 32 ].…”

Section: Discussionsupporting

confidence: 93%

“…The vegetation in arid areas could be more sensitive to climate change, which might influence the eco-environment in the countries and regions of the Belt and Road [ 4 ]. The work could enrich the understanding of the effects of climate change on land cover change and vegetation dynamics, laying the basis for its sustainable management.…”

Section: Discussionmentioning

confidence: 99%

“…Arid and semi-arid regions encompass nearly 40% of the Earth’s land surface, where about 20% of the human population of the world live [ 1 ]. As one of the largest arid and semi-arid regions in the Northern hemisphere, Central Asia is particularly influenced by drought [ 2 , 3 , 4 ]. Vegetation dynamics could reflect the changes in the ecological environment to some extent [ 5 , 6 , 7 ], and it might be extremely sensitive to climate change in fragile eco-environment [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

Bian

Shen

et al. 2020

IJERPH

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Since the Silk-road Economic belt initiatives were proposed, Xinjiang has provided a vital strategic link between China and Central Asia and even Eurasia. However, owing to the weak and vulnerable ecosystem in this arid region, even a slight climate change would probably disrupt vegetation dynamics and land cover change. Thus, there is an urgent need to determine the Normalized Difference Vegetation Index (NDVI) and Land-use/Land-cover (LULC) responses to climate change. Here, the extreme-point symmetric mode decomposition (ESMD) method and linear regression method (LRM) were applied to recognize the variation trends of the NDVI, temperature, and precipitation between the growing season and other seasons. Combining the transfer matrix of LULC, the Pearson correlation analysis was utilized to reveal the response of NDVI to climate change and climate extremes. The results showed that: (1) Extreme temperature showed greater variation than extreme precipitation. Both the ESMD and the LRM exhibited an increased volatility trend for the NDVI, with the significant improvement regions mainly located in the margin of basins. (2) Since climate change had a warming trend, the permanent snow has been reduced by 20,436 km2. The NDVI has a higher correlation to precipitation than temperature. Furthermore, the humid trend could provide more suitable conditions for vegetation growth, but the warm trend might prevent vegetation growth. Spatially, the response of the NDVI in North Xinjiang (NXC) was more sensitive to precipitation than that in South Xinjiang (SXC). Seasonally, the NDVI has a greater correlation to precipitation in spring and summer, but the opposite occurs in autumn. (3) The response of the NDVI to extreme precipitation was stronger than the response to extreme temperature. The reduction in diurnal temperature variation was beneficial to vegetation growth. Therefore, continuous concentrated precipitation and higher night-time-temperatures could enhance vegetation growth in Xinjiang. This study could enrich the understanding of the response of land cover change and vegetation dynamics to climate extremes and provide scientific support for eco-environment sustainable management in the arid regions.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

Bian

Shen

et al. 2020

IJERPH

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“…Since the indices time series for the NDVI, global circulations, precipitation and temperatures meet the basic assumptions of parametric linear analysis, we decided to use the Pearson correlation coefficient (PCC) (Luo et al, 2020) to give a clear picture of the strength of the relationships among them. The preliminary correlation analysis showed that the NDVI vegetation of both March and April, averaged over the entire region, have the highest correlation (0.5 ≤ PCC < 0.8) to the same cumulative amounts of October-March period total precipitation and January-March period mean temperatures according to the seven climate datasets.…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

“…The normalized difference vegetation index (NDVI) (Tucker, 1979), specifically, is the most popular and has been used successfully to detect vegetation and climate conditions interactions worldwide at various temporal and spatial scales (Wu et al, 2015;Xu, Yang & Chen, 2016;Yuan et al, 2019;Luo, Mao, Wen & Liu, 2020). In the study of Luo et al (2020), the dynamic characteristics of drought characterized using the standardized precipitation evapotranspiration index (SPEI) and the NDVI were investigated and evaluated on an interannual scale from 1998 to 2015. Two NDVI datasets were used in the work of Xu et al (2016) to study vegetation growth and its response to climate change reflected by the precipitation, minimum, maximum, and mean temperatures at yearly and monthly time scales from 1982 to 2013.…”

Section: Introductionmentioning

confidence: 99%

The sensitivity of vegetation in the lower Tigris basin landscapes to regional and global climate variability

Alhumaima

Abdullaev

2021

srees

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This study investigates the lower Tigris basin’s the normalized difference vegetation index (NDVI) sensitivity in 2000–2016 to regional climate variability reflected by the monthly precipitation and temperature time series of seven global datasets as well as to four global circulation indices. To examine the effect of climate variability on the different ecosystems, the study area has been classified into 10 smaller natural and anthropogenic landscapes based on landforms and land cover patterns. The preliminary analysis showed that the maximum biological productivity reflected by the NDVI of March and April has the highest correlation (0.5–0.8) to the same cumulative amounts of October–March period total precipitation and January–March period mean temperatures according to all datasets. In addition, this article showed there is a correlation between landscapes’ NDVI and global modulation represented by the September–February state of El Nińo-Southern Oscillation (ENSO) (0.55–0.70) and December state of the dipole mode index (DMI) (0.35–0.72). The significant differences in the original precipitation and temperature levels according to the different datasets have urged the use of normalized time series: z-score of temperatures and analogous six-months the standardized precipitation index (SPI). However, the multiple correlation analysis showed that using ERA-

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Relationships between NDVI, river discharge and climate in the Okavango River Basin region

Moses

Blamey

Reason

2021

Intl Journal of Climatology

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The Okavango River Basin (ORB) is a highly sensitive and biodiverse region in southern Africa whose climate, vegetation and river discharge characteristics are not well understood. This study investigated relationships between rainfall, temperature, Normalized Difference Vegetation Index (NDVI) and river discharge over the region as well as their trends and interannual variability. It is found that spatial patterns of NDVI are closely related to those of rainfall, but less so with temperature at monthly and seasonal time scales. The relationships between NDVI and rainfall/temperature differ north of 18.9°S where rainfall is higher than to its south. Typically, there are lags of 1–2‐months between NDVI and either rainfall or temperature. Also, there are large areas across the region that show significant warming trends in all seasons as well as wetting (mainly in the north). This increasing trend in surface temperature may act to worsen the impacts of extreme events such as severe drought and fire in the region. There is also pronounced interannual variability with significant correlations found with El Niño‐Southern Oscillation (ENSO), the subtropical South Indian Ocean Dipole and the Botswana High for rainfall and temperature, and for NDVI with ENSO. For the Southern Annular Mode, significant correlations were found with rainfall and temperature only in December and April. On longer time scales, focus was also placed on the wet 2006–2013 period relative to much drier 1999–2005 epoch for October–December. The wetter conditions during 2006–2013 appear related to La Niña Modoki conditions and warmer sea surface temperature near Angola as well as regional circulation differences.

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Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Cited by 57 publications

References 45 publications

Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

The sensitivity of vegetation in the lower Tigris basin landscapes to regional and global climate variability

Relationships between NDVI, river discharge and climate in the Okavango River Basin region

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