Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 414706
Title A comparison of time series similarity measures for classification and change detection of ecosystem dynamics
Author(s) Lhermitte, S.; Verbesselt, J.; Verstraeten, W.W.; Coppin, P.
Source Remote Sensing of Environment 115 (2011)12. - ISSN 0034-4257 - p. 3129 - 3152.
Department(s) Laboratory of Geo-information Science and Remote Sensing
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) land-cover classification - conterminous united-states - nino-southern oscillation - rangeland vegetation type - finding coupled patterns - change-vector analysis - satellite sensor data - sub-saharan africa - leaf-area index - avhrr ndvi data
Abstract Time series of remote sensing imagery or derived vegetation indices and biophysical products have been shown particularly useful to characterize land ecosystem dynamics. Various methods have been developed based on temporal trajectory analysis to characterize, classify and detect changes in ecosystem dynamics. Although time series similarity measures play an important role in these methods, a quantitative comparison of the similarity measures is lacking. The objective of this study was to provide an overview and quantitative comparison of the similarity measures in function of varying time series and ecosystem characteristics, such as amplitude, timing and noise effects. For this purpose, the performance was evaluated for the commonly used similarity measures (D), ranging from Manhattan (DMan), Euclidean (DE) and Mahalanobis (DMah) distance measures, to correlation (DCC), Principal Component Analysis (PCA; DPCA) and Fourier based (DFFT,D¿,DFk) similarities. The quantitative comparison consists of a series of Monte-Carlo simulations based on subsets of global MODIS Normalized Difference Vegetation index (NDVI) and Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) data. Results of the simulations reveal four main groups of time series similarity measures with different sensitivities: (i) DMan, DE, DPCA, DFk quantify the difference in time series values, (ii) DMah accounts for temporal correlation and non-stationarity of variance, (iii) DCC measures the temporal correlation, and (iv) the Fourier based DFFT and D¿ show their specific sensitivity based on the selected Fourier components. The difference measures show relatively the highest sensitivity to amplitude effects, whereas the correlation based measures are highly sensitive to variations in timing and noise. The Fourier based measures, finally, depend highly on the signal to noise ratio and the balance between amplitude and phase dominance. The heterogeneity in sensitivity of each D stresses the importance of (i) understanding the time series characteristics before applying any classification of change detection approach and (ii) defining the variability one wants to identify/account for. This requires an understanding of the ecosystem dynamics and time series characteristics related to the baseline, amplitude, timing, noise and variability of the ecosystem time series. This is also illustrated in the quantitative comparison, where the different sensitivities of D for the NDVI, EVI, and LAI data relate specifically to the temporal characteristics of each data set. Additionally, the effect of noise and intra- and interclass variability is demonstrated in a case study based on land cover classification.
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