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Alpine cold vegetation response to climate change in the western Nyainqentanglha range in 1972-2009.

Wang X, Sun Z, Zhou AG - ScientificWorldJournal (2014)

Bottom Line: This may be the result of the mountain effect.The variation appears to be associated with an increase in mean temperature of 0.05 °C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades.The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.

View Article: PubMed Central - PubMed

Affiliation: School of Environmental Studies, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China ; Laboratory of Basin Hydrology and Wetland Eco-Restoration, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China.

ABSTRACT
The Tibetan Plateau is regarded as one of the most climatic-sensitive regions all over the world. Long-term remote sensing data enable us to monitor spatial-temporal change in this area. The vegetation changes of the western Nyainqentanglha region were detected by using RS and GIS techniques. And the vegetation coverage was derived by the NDVI-SMA (spectral mixture analysis) methods. An incensement of vegetation was observed in the mountain areas during 1972-2009 with a mean vegetation coverage of 24.87%, 35.89%, and 42.88% in 30/09/1972, 14/09/1991, and 30/08/2009, respectively. The vegetation fraction increased by 18% in the period of 1972-2009. The bin with the elevation between 4400 and 5200 m had the highest vegetation coverage. This may be the result of the mountain effect. Alpine vegetation had a trend to increase and expand to higher altitudes with the climate change in the past 40 years. The variation appears to be associated with an increase in mean temperature of 0.05 °C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades. The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.

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The estimation of actual fractional green vegetation cover (FV) with the 5 × 5 window surrounding each sample TM pixel from the high resolution image. (a) is the 5 × 5 pixel of TM subset with 5-4-1 band combination; (b) is the high resolution subset corresponding to the 5 × 5 pixel; (c) is the classified results of the high resolution subset. GV is the abbreviation for green vegetation, and NGV represents no green vegetation.
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fig4: The estimation of actual fractional green vegetation cover (FV) with the 5 × 5 window surrounding each sample TM pixel from the high resolution image. (a) is the 5 × 5 pixel of TM subset with 5-4-1 band combination; (b) is the high resolution subset corresponding to the 5 × 5 pixel; (c) is the classified results of the high resolution subset. GV is the abbreviation for green vegetation, and NGV represents no green vegetation.

Mentions: We randomly selected 60 plots (5 × 5 pixels) from the TM images and calculated the average FV of each plot (Figure 4). The high resolution GeoEye-1 was used to estimate the actual FV of these sample TM plots and then compared with their predicted vegetation fractions for evaluation of FV calculated by NDVI-SMA. Each high resolution plot was classified into green vegetation (GV) and no green vegetation (NGV) using maximum likelihood classification. Correlation coefficients R2 and overall RMSE were 0.89 and 0.054, respectively. This approach was also used to estimate the FV in 1991 and 1972.


Alpine cold vegetation response to climate change in the western Nyainqentanglha range in 1972-2009.

Wang X, Sun Z, Zhou AG - ScientificWorldJournal (2014)

The estimation of actual fractional green vegetation cover (FV) with the 5 × 5 window surrounding each sample TM pixel from the high resolution image. (a) is the 5 × 5 pixel of TM subset with 5-4-1 band combination; (b) is the high resolution subset corresponding to the 5 × 5 pixel; (c) is the classified results of the high resolution subset. GV is the abbreviation for green vegetation, and NGV represents no green vegetation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4150475&req=5

fig4: The estimation of actual fractional green vegetation cover (FV) with the 5 × 5 window surrounding each sample TM pixel from the high resolution image. (a) is the 5 × 5 pixel of TM subset with 5-4-1 band combination; (b) is the high resolution subset corresponding to the 5 × 5 pixel; (c) is the classified results of the high resolution subset. GV is the abbreviation for green vegetation, and NGV represents no green vegetation.
Mentions: We randomly selected 60 plots (5 × 5 pixels) from the TM images and calculated the average FV of each plot (Figure 4). The high resolution GeoEye-1 was used to estimate the actual FV of these sample TM plots and then compared with their predicted vegetation fractions for evaluation of FV calculated by NDVI-SMA. Each high resolution plot was classified into green vegetation (GV) and no green vegetation (NGV) using maximum likelihood classification. Correlation coefficients R2 and overall RMSE were 0.89 and 0.054, respectively. This approach was also used to estimate the FV in 1991 and 1972.

Bottom Line: This may be the result of the mountain effect.The variation appears to be associated with an increase in mean temperature of 0.05 °C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades.The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.

View Article: PubMed Central - PubMed

Affiliation: School of Environmental Studies, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China ; Laboratory of Basin Hydrology and Wetland Eco-Restoration, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China.

ABSTRACT
The Tibetan Plateau is regarded as one of the most climatic-sensitive regions all over the world. Long-term remote sensing data enable us to monitor spatial-temporal change in this area. The vegetation changes of the western Nyainqentanglha region were detected by using RS and GIS techniques. And the vegetation coverage was derived by the NDVI-SMA (spectral mixture analysis) methods. An incensement of vegetation was observed in the mountain areas during 1972-2009 with a mean vegetation coverage of 24.87%, 35.89%, and 42.88% in 30/09/1972, 14/09/1991, and 30/08/2009, respectively. The vegetation fraction increased by 18% in the period of 1972-2009. The bin with the elevation between 4400 and 5200 m had the highest vegetation coverage. This may be the result of the mountain effect. Alpine vegetation had a trend to increase and expand to higher altitudes with the climate change in the past 40 years. The variation appears to be associated with an increase in mean temperature of 0.05 °C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades. The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.

Show MeSH
Related in: MedlinePlus