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Near-real-time disturbance detection within the remotely sensed time series has become a crucial task in many environmental applications that can help policymakers and responsible authorities to make rapid decisions and proper actions. Although there are several techniques for the near-real-time monitoring of time series, their reliability in regions with higher latitudes are not yet assessed, particularly in regions with consistent data gaps in certain time periods and with large observational uncertainties. A new method is proposed that determines a stable history period from which the least-squares spectral analysis can detect and classify the changes in newly acquired data. To validate the effectiveness of the method, both simulated and real-world vegetation time series obtained for a region in northern Alberta, Canada, are used, where there are consistent data gaps from November to April each year due to the availability of valid Landsat satellite imagery and climate conditions. Furthermore, the least-squares cross-wavelet analysis is applied to demonstrate how the temperature and precipitation time series can be used for assessment of the results. The proposed method is fast, does not rely on any interpolation methods, leaves the data gap as is, considers the observational uncertainties, and does not depend on thresholds.

remote sensing

The Potential of the Least-Squares Spectral and Cross-Wavelet Analyses for Near-Real-Time Disturbance Detection within Unequally Spaced Satellite Image Time Series