The implications of serial correlation and time-lag effects for the impact study of climate change on vegetation dynamics - a case study with Hulunber meadow steppe, Inner Mongolia

文献类型： 外文期刊

第一作者： Lin, Yong

作者： Lin, Yong;Xin, Xiaoping;Zhang, Hongbin;Wang, Xu;Lin, Yong

作者机构：

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.151； 五年影响因子：3.266 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Ecological time series data are widely used in ecological research thanks to the development of remote-sensing technologies and fixed ecological research stations. However, the serial correlation issue with time series, which violates the fundamental assumption of independence for traditional statistical models or analysis, is rarely considered by ecologists in vegetation-climate relationship research. In addition, the issue of time lags between climate change and vegetation response is also often ignored. Inadequate consideration of these issues produces misleading results in some cases. In this article, we propose an approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric test to address serial correlation issue and distribution requirements for the valid statistical analysis of time series data. With Hulunber meadow steppe as a case, we applied this approach to analyse the role of climate factors in vegetation dynamics based on leaf area index (LAI) data and climatic data. The results showed that the LAI dynamics of Hulunber meadow steppe were mainly related to temperature with the time lag of zero, whereas the impact of precipitation on LAI dynamics was not statistically obvious. The comparison of regression models that deal with serial correlation and residual normality to different extents showed that ignoring the serial correlation issue with time series data likely produces misleading results, highlighting the importance of serial correlation removal. The combination of nonparametric correlation tests with ARIMA-based cross-correlation analysis also proved quite useful in reducing the chance of spurious correlation and time lags resulting from outlier values in ARIMA-based cross-correlation.

分类号： TP

  • 相关文献
作者其他论文 更多>>

微信小程序