Adaptive Decomposition and Multitimescale Analysis of Long Time Series of Climatic Factors and Vegetation Index Based on ICEEMDAN-SVM

Abstract

Climate change is of great significance to vegetation coverage. However, the long time series of climatic factors and vegetation index are nonstationary and nonlinear, containing different information in frequency and time scales. The study innovatively integrated a support vector machine (SVM) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and analyzed the relationship between climatic factors and the normalized difference vegetation index (NDVI) at three timescales in Loess Plateau (LP). The results indicate that: 1) The mode mixing phenomenon of empirical mode decomposition can be better solved by using ICEEMDAN, and the end effect can be mitigated by using SVM to expand both ends of the data before decomposition. 2) The residual NDVI stripped out using ICEEMDAN-SVM can represent the long-term trend of the original data. The results show that the NDVI and climate factors performed noticeable spatial and temporal differences under different climate zones. 3) The relationship between vegetation and climatic factors revealed obvious spatial heterogeneity at different timescales, and the climate change in the overall trend explained the vegetation change to the greatest extent, approximately 95%. The study has shown that temperature is a limiting factor affecting the growth of vegetation on the LP. We propose ICEEMDAN-SVM to study the relationship between climate factors and vegetation indices at multiple timescales, revealing some hidden information in long time series and providing a new method to quantify the impact of climate change on vegetation dynamics.

Keywords

• Climate change
• improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
• Loess Plateau (LP)
• multiple timescales
• normalized difference vegetation index (NDVI)
• support vector machine(SVM)