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Satellite-based studies on large-scale vegetation changes in China.
Xia Zhao1, Daojing Zhou, Jingyun Fang
1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China.
Journal of Integrative Plant Biology
|September 15, 2012
Summary
Satellite remote sensing reveals significant increases in China
Area of Science:
- Ecological monitoring
- Environmental science
- Remote sensing applications
Background:
- Remotely-sensed vegetation indices are crucial for monitoring vegetation dynamics.
- Satellite data have been extensively used to study vegetation cover, biomass, productivity, phenology, desertification, and grassland degradation.
- China's terrestrial ecosystems have undergone significant changes over the past 2-3 decades.
Purpose of the Study:
- To review satellite-based studies on vegetation dynamics in China over the past 2-3 decades.
- To analyze changes in vegetation cover, biomass, productivity, phenology, desertification, and grassland degradation.
- To assess the reliability of remote sensing for monitoring vegetation.
Main Methods:
- Review of satellite-based studies focusing on vegetation indices like the Normalized Difference Vegetation Index (NDVI).
- Analysis of vegetation cover changes, biomass, productivity, and phenological dynamics.
- Examination of desertification and grassland degradation trends.
Main Results:
- Significant increases observed in growing season NDVI and net primary productivity across major terrestrial ecosystems.
- Substantial decline in fresh lake numbers and urban vegetation coverage.
- Advancing growing season start in temperate regions until the 1990s; increased vegetation activity in arid/semi-arid grasslands and oases.
- Declining coverage of sparsely-vegetated areas.
Conclusions:
- Remote sensing data indicate widespread vegetation greening and productivity increases in China, alongside urban vegetation loss.
- Vegetation dynamics show complex spatial heterogeneity and regional variations.
- Caution is advised when interpreting remote sensing data due to dependencies on data sources, methods, and time periods.
