Publications

Zhang, Y; Hepner, GF (2017). The Dynamic-Time-Warping-based k-means plus plus clustering and its application in phenoregion delineation. INTERNATIONAL JOURNAL OF REMOTE SENSING, 38(6), 1720-1736.

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.

DOI:
10.1080/01431161.2017.1286055

ISSN:
0143-1161