A clustering method for inter-annual NDVI time series. Issue 8 (3rd August 2021) - Yang, Zhen; Shen, Yingying; Li, Jing; Jiang, Huawei; Zhao, like
ABSTRACT: Inter-annual Normalized Difference Vegetation Index (NDVI) time series has been applied to change detection in many fields. Research on change detection in inter-annual NDVI time series always requires several related parameters or sample training, which may limit the transferability of the methods to some extent. In this letter, we try to develop a shape-based clustering to analyse the inter-annual NDVI time series by considering the shapes of time series. The method adopts shape-based distance based on cross-correlation analysis to measure the distance between time series and uses shape-based averaging method named Dynamic Time Warping Barycentre Averaging to get the cluster centroids. Through experimental analysis, the shape-based clustering can well gather the NDVI time series with similar trends into a cluster. Also, the shape-based clustering is proven to be reliable with 94.4% overall accuracy for detection of vegetation disturbance.