Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China
Climatic extremes such as drought have becoming a severe climate-related problem in many regions all over the world that can induce anomalies in vegetation condition. Growth and CO2 uptake by plants are constrained to a large extent by drought. Therefore, it is important to understand the spatial and temporal responses of vegetation to drought across the various land cover types and different regions. Leaf area index (LAI) derived from Global Land Surface Satellite (GLASS) data was used to evaluate the response of vegetation to drought occurrence across Yunnan Province, China (2001–2010). The meteorological drought was assessed based on Standardized Precipitation Index (SPI) values. Pearson’s correlation coefficients between LAI and SPI were examined across several timescales within six sub-regions of the Yunnan. Further, the drought-prone area was identified based on LAI anomaly values. Lag and cumulative effects of lack of precipitation on vegetation were evident, with significant correlations found using 3-, 6-, 9- and 12-month timescale. We found 9-month timescale has higher correlations compared to another timescale. Approximately 29.4% of Yunnan’s area was classified as drought-prone area, based on the LAI anomaly values. Most of this drought-prone area was distributed in the mountainous region of Yunnan. From the research, it is evident that GLASS LAI can be effectively used as an indicator for assessing drought conditions and it provide valuable information for drought risk defense and preparedness.
KeywordsMODIS Leaf area index distribution Standardized Precipitation Index (SPI) Drought Yunnan
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This study was a part of the Project on “Building Effective Water Governance in the Asian Highlands” supported by Canada’s International Development Research Centre (IDRC), National Science Foundation of China, Grant No. 31270524 and the CGIAR research programs on ‘Climate change adaptation and mitigation’ (CRP6.4). We greatly thank the Center for Global Change Data Processing and Analysis in China for providing the satellite remote sensing data.
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