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The Dynamic-Time-Warping-based k-means++ clustering and its application in phenoregion delineation
- Zhang, Yuan, Hepner, George F.
- International journal of remote sensing 2017 v.38 no.6 pp. 1720-1736
- cohesion, monitoring, natural resource management, normalized difference vegetation index, phenology, remote sensing, time series analysis, vegetation types
- 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.