Land cover dynamic change modes analysis based on empirical mode decomposition

The dynamic change mode of land cover not only reflects the change characteristics of the land cover itself, but also reflects the influence exerted by external factors. Empirical mode decomposition is an effective time-frequency analysis method for nonlinear and nonstationary signals. In this paper, empirical mode decomposition firstly uses to each pixel time series to get its intrinsic mode functions, each intrinsic mode function relates to change mode with some time scale, clustering those intrinsic mode functions over the image, and different land cover change modes are obtained. The experimental dataset used is MODIS subset from the Jan 2011 to Dec 2020, located in the Ningxia Hui Autonomous Region, China. The clustering results of half-annual cycle and one-year cycle of intrinsic mode functions show the influence of local climate and policy factors on land cover change.