Detection and attribution of vegetation greening trend in China over the last 30 years
By Shilong Piao, Guodong Yin, J. Tan, Lei Cheng, Mengtian Huang, Yue Li, Ronggao Liu, J. Mao, Ranga B. Myneni, S. Peng and 6 more. Global Change Biology, 2015
755 citations recorded by the source corpus.
The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index (LAI) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr−1, ranging from 0.0035 yr−1 to 0.0127 yr−1), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.
Subfields: Atmospheric Science, Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/547dc12a7ca44f650c73a1274b9ab62b4308d117
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Citations in the corpus
- The Palaearctic steppe biome: a new synthesis (2016, background): “It is unclear if this may offset effects of rising CO2 levels and thus increased water use efficiency (Piao et al. 2015; Soussana and Luscher 2007).”
- Factors Affecting Long-Term Trends in Global NDVI (2019, methodology): “Secondly, some studies used LAI [6,16,18,58], Fracti nal Vegetation Cov r (FVC) [59], Enhanced Vegetation Index (EVI) [60], and even growth season data [16] to characterize vegetation.”
- Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal (2018, background): “As mentioned above, the result is more consistent with previous research carried out in the Northern Hemisphere, China [1,84,96] and Tibetan plateau [21], where the NDVI is reportedly positive and mostly related to temperature and precipitation.”