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Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011

(2015) REMOTE SENSING LETTERS. 6(1). p.69-77
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Organization
Abstract
Changes in precipitation patterns were expected to have strong impacts on temperate ecosystem dynamics. North China has experienced opposite trends of precipitation change (increased in the west and decreased in the east) in the past several decades. Under such a background, we analysed mean growing season (GS) (April-October) grass Normalized Difference Vegetation Index (NDVI) changes using combined dataset of Global Inventory, Monitoring, and Modelling Studies and Moderate Resolution Imaging Spectroradiometer NDVI in North China during 1982-2011. The results showed that in mean GS NDVI increased for grasslands in both Northeastern China (NE) and Northwestern China (NW). Increase in NDVI in NW was mainly due to the increase in precipitation (r = 0.50, p < 0.01). However, the decrease in precipitation did not cause a decrease in grass NDVI in NE, suggesting that precipitation is still higher than the most sensitive value and NDVI changes were significantly correlated with the increased temperature (r = 0.43, p < 0.05).
Keywords
NET PRIMARY PRODUCTION, VEGETATION, RESPONSES, TEMPERATURE, GROWTH, FOREST

Citation

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MLA
Yuan, Xiuliang, Longhui Li, and Xi Chen. “Increased Grass NDVI Under Contrasting Trends of Precipitation Change over North China During 1982–2011.” REMOTE SENSING LETTERS 6.1 (2015): 69–77. Print.
APA
Yuan, X., Li, L., & Chen, X. (2015). Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011. REMOTE SENSING LETTERS, 6(1), 69–77.
Chicago author-date
Yuan, Xiuliang, Longhui Li, and Xi Chen. 2015. “Increased Grass NDVI Under Contrasting Trends of Precipitation Change over North China During 1982–2011.” Remote Sensing Letters 6 (1): 69–77.
Chicago author-date (all authors)
Yuan, Xiuliang, Longhui Li, and Xi Chen. 2015. “Increased Grass NDVI Under Contrasting Trends of Precipitation Change over North China During 1982–2011.” Remote Sensing Letters 6 (1): 69–77.
Vancouver
1.
Yuan X, Li L, Chen X. Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011. REMOTE SENSING LETTERS. 2015;6(1):69–77.
IEEE
[1]
X. Yuan, L. Li, and X. Chen, “Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011,” REMOTE SENSING LETTERS, vol. 6, no. 1, pp. 69–77, 2015.
@article{8534487,
  abstract     = {Changes in precipitation patterns were expected to have strong impacts on temperate ecosystem dynamics. North China has experienced opposite trends of precipitation change (increased in the west and decreased in the east) in the past several decades. Under such a background, we analysed mean growing season (GS) (April-October) grass Normalized Difference Vegetation Index (NDVI) changes using combined dataset of Global Inventory, Monitoring, and Modelling Studies and Moderate Resolution Imaging Spectroradiometer NDVI in North China during 1982-2011. The results showed that in mean GS NDVI increased for grasslands in both Northeastern China (NE) and Northwestern China (NW). Increase in NDVI in NW was mainly due to the increase in precipitation (r = 0.50, p < 0.01). However, the decrease in precipitation did not cause a decrease in grass NDVI in NE, suggesting that precipitation is still higher than the most sensitive value and NDVI changes were significantly correlated with the increased temperature (r = 0.43, p < 0.05).},
  author       = {Yuan, Xiuliang and Li, Longhui and Chen, Xi},
  issn         = {2150-704X},
  journal      = {REMOTE SENSING LETTERS},
  keywords     = {NET PRIMARY PRODUCTION,VEGETATION,RESPONSES,TEMPERATURE,GROWTH,FOREST},
  language     = {eng},
  number       = {1},
  pages        = {69--77},
  title        = {Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011},
  url          = {http://dx.doi.org/10.1080/2150704x.2014.1002944},
  volume       = {6},
  year         = {2015},
}

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