A clustering method for inter-annual NDVI time series

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.

Automated summary

A shape-based clustering method was developed to analyze inter-annual NDVI time series by considering the shapes of time series, effectively grouping NDVI time series with similar trends into clusters, and proven to be reliable in detecting vegetation disturbance with an overall accuracy of 94.4%.