No abstract

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.

Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.