The Dynamic-Time-Warping-based k-means++ clustering and its application in phenoregion delineation

ABSTRACT

The phenoregion delineation facilitates more effective monitoring and more accurate forecasting of land-surface phenology (LSP), and thereby can greatly improve natural resources management. This article delineated a series of phenoregion maps by applying the Dynamic-Time-Warping (DTW)-based k-means++ clustering on normalized difference vegetation index (NDVI) time series. The DTW distance, a well-known shape-based similarity measure for time series data, was used as the distance measure instead of the traditional Euclidean distance in k-means++ clustering. These phenoregion maps were compared with the ones clustered based on the similarity of phenological forcing variables. The results demonstrated that the DTW-based k-means++ clustering can capture much more homogeneous phenological cycles within each phenoregion; the two types of phenoregion maps have a medium degree of spatial concordance, and their representativeness of vegetation types is comparable. The phenocycle-based phenoregion map with 15 phenoregions was selected as the optimal one, based on the criteria of cluster cohesion and separation.

Disclosure statement

No potential conflict of interest was reported by the authors.