Replication Data for: Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China

This dataset was moved to: https://doi.org/10.34725/DVN/XYOU4QClimatic 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.

本数据集已迁移至：https://doi.org/10.34725/DVN/XYOU4Q。气候极端事件（如干旱）已成为全球诸多地区愈发严重的气候相关问题，可引发植被状况异常。植物的生长与二氧化碳吸收能力在很大程度上受干旱制约，因此理解不同土地覆盖类型与不同区域的植被对干旱的时空响应具有重要意义。本研究采用基于全球陆面卫星（Global Land Surface Satellite, GLASS）数据反演得到的叶面积指数（Leaf Area Index, LAI），评估2001-2010年中国云南省范围内植被对干旱发生的响应。气象干旱通过标准化降水指数（Standardized Precipitation Index, SPI）进行评估。针对云南省六个分区内的多个时间尺度，计算并分析了LAI与SPI间的皮尔逊相关系数。此外，基于LAI距平值识别了研究区内的易干旱区域。研究发现，降水亏缺对植被存在显著的滞后与累积效应，在3、6、9及12个月时间尺度上均检测到显著相关关系，且9个月时间尺度的相关系数高于其余时间尺度。基于LAI距平值，约29.4%的云南省域被划分为易干旱区域，该类区域大多分布于云南省山地丘陵区。本研究结果表明，GLASS反演的LAI可有效作为评估干旱状况的指标，可为干旱风险防控与应急准备提供重要参考依据。