Spatial-temporal variation of vegetation and its correlation with climate change in Central Asia during the period of 1982-2012

Gang, 殷刚 Yin; Xianyong, 孟现勇 Meng; Hao, 王浩 Wang; Zengyun, 胡增运 Hu; Zhiqun, 孙志群 Sun

doi:10.5846/stxb201601240164

Cited by 8 publications

(4 citation statements)

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“…Areas with positive correlation between NDVI and precipitation reached as high as 80.4%, indicating that precipitation is the major influencing factor of vegetation conditions in CA. Yin et al [112] investigated the correlation between spatial-temporal changes of vegetation and climatic changes in CA from 1982 to 2012. They concluded that NDVI had a weak negative correlation with annual temperature changes, but a positive correlation with precipitation, which is consistent with the results of this study.…”

Section: Effects Of Climatic Factors On Vegetation Changesmentioning

confidence: 99%

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

Qian

Wang

et al. 2022

Remote Sensing

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The uncertainties in soil erosion (SE) are further intensified by various factors, such as global warming, regional warming and humidification, and vegetation cover changes. Moreover, quantitative evaluations of SE in major basins of Central Asia (CA) under changing environments have rarely been conducted. This study conducted quantitative evaluation of SE in four major basins (Syr Darya Basin (SDB), Amu Darya Basin (ADB), Ili River Basin (IRB) and Tarim River Basin (TRB) using the Revised Universal Soil Loss Equation (RUSLE) and analyzed the main driving factors. SE quantities in the basins presented relatively consistent upward fluctuating trends from 1982 to 2017. Vegetation cover variation fluctuated significantly from 1982 to 2017. Specifically, vegetation cover decreased continuously in SDB, ADB, and IRB, but increased gradually in TRB. Pixels with positive spatial variation of vegetation mainly occurred around lakes and oases near rivers. The Normalized Difference Vegetation Index (NDVI) showed higher correlation with precipitation (80.5%) than with temperature (48.3%). During the study period, the area of arable land (AL) exhibited the largest change among all land use types in CA. Under long-term human activities, the proportion of NDVI of other land types converting to AL was the highest. In the structural equation model (SEM), precipitation, temperature, Shannon Diversity Index (SHDI), and NDVI strongly influenced SE. Overall, the major basins in CA were jointly affected by climate, human activities, and vegetation. Specifically, climatic factors exerted the strongest influence, followed by SHDI (human activities). SE was found to be relatively serious in ADB, SDB, and IRB, with SE in SDB even approaching that in the Loess Plateau. Under the background of global changes, appropriate water and land resource management and optimization configurations should be implemented in CA with reference to TRB in order to relieve local SE problems.

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Section: Effects Of Climatic Factors On Vegetation Changesmentioning

confidence: 99%

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

Qian

Wang

et al. 2022

Remote Sensing

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“…We postulate that the factors driving grassland decline can be attributed to two main factors: climate change and human activities. Research has demonstrated five Central Asia countries witnessing an increase in aridity, marked by the severest drought in the northwestern part of Kazakhstan and a gradual reduction in overall precipitation [4,32,48]. This shift in climate patterns was likely a primary driver behind the declining grassland NDVI in northern Kazakhstan.…”

Section: Discussionmentioning

confidence: 99%

“…The western part (the five central Asia countries) holds an annual precipitation of 300-500 mm, and this region witnesses cold winters, hot summers, and substantial day-to-day temperature variations [29,31]. Under arid and semiarid climatic conditions [32], the main land use type is grassland, with sparse vegetation and extensive desert areas in the central region.…”

Section: Study Areamentioning

confidence: 99%

Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands

Miao,

Zhang,

Agathokleous

et al. 2024

Remote Sensing

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Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model’s performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.

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“…The normalized difference vegetation index (NDVI), as an index to characterize the vegetation coverage, is widely used in the study of the laws and driving forces of long-timeseries vegetation evolution [6][7][8][9][10]. In recent years, using wavelet analysis and empirical mode decomposition (EMD) methods, combined with NDVI data, to extract the periodic characteristics of time series and study vegetation cover change has attracted extensive attention from domestic scholars [11][12][13][14][15][16][17][18][19][20]. Wavelet analysis is a method of processing signals and images, which is adaptive and can decompose complex time series composed of different frequency components interwoven into sub sequences with different frequencies [21].…”

Section: Introductionmentioning

confidence: 99%

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

Zhang

Tian

et al. 2023

Atmosphere

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Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement.

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Spatial-temporal variation of vegetation and its correlation with climate change in Central Asia during the period of 1982-2012

Cited by 8 publications

References 0 publications

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

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