A new method for detection “greening” or “browning” change trend in vegetation from NDVI sequences
Liang TANG1,2, Zhongming ZHAO2, Ping TANG2()
1.Hainan Tropical Ocean University, Sanya 572022, China 2.Institute of Remote Sensing and Digital Earth Applications, Chinese Academy of Sciences, Beijing 100101, China
Normalized difference vegetation index (NDVI) trends can approximate the trend of “greening” or “browning” of vegetation and reflect the adaptation process of vegetation to global change. In this paper, an NDVI trend analysis method combining empirical mode decomposition (EMD) and Mann-Kendall (MK) significance test is proposed on vegetation monotone trend detection. The method includes mainly two steps: firstly, EMD is used to decompose NDVI time series into a finite number of intrinsic mode functions (IMF), and these components contain the local characteristic information of different time scales of the original signal. The first component is a high-frequency component, the subsequent component frequency gradually decreases, and the residual is a monotonic function, indicating the average trend. From the decomposition, the NDVI variation trend along with time is extracted naturally. Secondly, the MK significance test is used to detect the monotonicity of the trend varied, that is, to detect that the trend is monotonically increasing or monotonically decreasing, the monotonically increasing is corresponding to the trend of vegetation getting “greening”, and the monotonically decreasing is corresponding to the trend of vegetation getting “browning”. The test data are MODIS NDVI time series of 16 days from 2006 to 2015. The analysis of the trend detection of those NDVI time series shows that the method proposed in this paper is an effective method for time series trend analysis and has a wide application prospect.
唐亮, 赵忠明, 唐娉. 从NDVI序列检测植被“绿化”或“褐化”变化趋势的新方法[J]. 国土资源遥感, 2019, 31(2): 89-95.
Liang TANG, Zhongming ZHAO, Ping TANG. A new method for detection “greening” or “browning” change trend in vegetation from NDVI sequences. Remote Sensing for Land & Resources, 2019, 31(2): 89-95.
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