Response of vegetation activity dynamic to climatic change and ecological restoration programs in Inner Mongolia from 2000 to 2012

Tian, Hao; Cao, Chunxiang; Chen, Wei; Bao, Shanning; Yang, Bin; Myneni, Ranga B.

doi:10.1016/j.ecoleng.2015.04.098

Cited by 140 publications

(75 citation statements)

References 42 publications

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“…This approach has been widely applied to analyze the relationship between NDVI and climatic factors [14,[81][82][83]. The r indicates the linear relationship and the p values indicate the significant correlation level between two variables.…”

Section: Correlation Between Ndvi and Climatic Variablesmentioning

confidence: 99%

“…If the time series data present a linear trend, the true slope (change per unit time) of a trend could be estimated by the non-parametric index developed by Sen [75], which is based on the assumption of a linear trend. In the last three decades, it has been highly applied in the studies of vegetation dynamics [14,[76][77][78]]. Sen's slope was computed as…”

Section: Sen's Non-parametric Slopementioning

confidence: 99%

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Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Baniya

Tang

Huang

et al. 2018

Forests

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Abstract:Nepal is a country of contrast, with varying altitude, climate and vegetation from the top of high mountains to the tropical forest in low lands. The terrestrial vegetation has rapidly been altered by climate change in Nepal. The spatial and temporal evolution of vegetation and its linkage to climatic variables were analyzed using the Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer (AVHRR) sensors. A linear regression model and Sen's slope method were used to estimate NDVI trends and the Pearson correlation between NDVI and climatic variable, i.e., temperature and precipitation were calculated to identify the role of climate in vegetation changes. The carbon dynamics were also measured using a biomass carbon density estimation model. Results showed that NDVI experienced an overall increasing trend in Nepal from 1982-2015. The NDVI significantly increased at the rate of 0.0008 year −1 (p < 0.05) with seasonal variation of 0.0004 year −1 , p > 0.05; 0.0007 year −1 , p < 0.05; 0.0008 year −1 , p < 0.05 and 0.0007 year −1 , p > 0.05 in winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The NDVI relative change ratio (RCR) was 6.29% during last 34 years in Nepal. The correlation between NDVI and temperature was significantly positive (r = 0.36, p = 0.03), but there was a negative correlation with precipitation (r = −0.21, p = 0.28). Altogether, 82.20% of the study areas showed a positive correlation with temperature in which 34.97% was significant and 69.23% of the area had a negative correlation (16.35% significant, p < 0.05) with precipitation. In addition, NDVI-based carbon estimation showed that Nepal's forest total carbon stock is 685.45 × 10 6 t C (i.e., an average of 115.392 t C/ha) with an annual carbon sequestration rate of 0.10 t C/ha from 1982-2015. The results suggest that NDVI variation is more sensitive to temperature than precipitation and it is valuable to measure carbon dynamics in Nepal.

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Section: Correlation Between Ndvi and Climatic Variablesmentioning

confidence: 99%

Section: Sen's Non-parametric Slopementioning

confidence: 99%

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Baniya

Tang

Huang

et al. 2018

Forests

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“…Moreover, vegetation condition is also a crucial factor influencing E T (Tian et al, 2015;Piao et al, 2006;Mackay et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Monitoring the variations of evapotranspiration due to land use/cover change in a semiarid shrubland

Gong

Lei

Yang

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration (E T ) is an important process in the hydrological cycle, and vegetation change is a primary factor that affects E T . In this study, we analyzed the annual and inter-annual characteristics of E T using continuous observation data from eddy covariance (EC) measurement over 4 years (1 July 2011 to 30 June 2015) in a semiarid shrubland of Mu Us Sandy Land, China. The Normalized Difference Vegetation Index (NDVI) was demonstrated as the predominant factor that influences the seasonal variations in E T . Additionally, during the land degradation and vegetation rehabilitation processes, E T and normalized E T both increased due to the integrated effects of the changes in vegetation type, topography, and soil surface characteristics. This study could improve our understanding of the effects of land use/cover change on E T in the fragile ecosystem of semiarid regions and provide a scientific reference for the sustainable management of regional land and water resources.

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“…In contrast with evidence showing that vegetation resources are threatened by human population growth [6], a larger population might use vegetation resources more efficiently. A close connection exists between vegetation and human activity [34,35]. Furthermore, the impact of the percentage of human activity on NPP did not increase human-activity density such as in Lhasa.…”

Section: Relationship Between Npp and Climate/humanmentioning

confidence: 98%

The Impact of Climate Change and Human Activity on Net Primary Production in Tibet

Qin¹,

Sun²,

Liu³

et al. 2016

Pol. J. Environ. Stud.

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Response of vegetation activity dynamic to climatic change and ecological restoration programs in Inner Mongolia from 2000 to 2012

Cited by 140 publications

References 42 publications

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Monitoring the variations of evapotranspiration due to land use/cover change in a semiarid shrubland

The Impact of Climate Change and Human Activity on Net Primary Production in Tibet

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