Multiscale detection of practically significant changes in a gradually varying time series

Procedures for detecting change points in sequences of correlated observations (e.g., time series) can help elucidate their complicated structure. Current literature on the detection of multiple change points emphasizes the analysis of sequences of independent random variables. We address the problem of an unknown number of variance changes in the presence of long-range dependence (e.g., long memory processes). Our results are also applicable to time series whose spectrum slowly varies across octave bands. An iterated cumulative sum of squares procedure is introduced in order to look at the multiscale stationarity of a time series; that is, the variance structure of the wavelet coefficients on a scale by scale basis. The discrete wavelet transform enables us to analyze a given time series on a series of physical scales. The result is a partitioning of the wavelet coefficients into locally stationary regions. Simulations are performed to validate the ability of this procedure to detect and locate multiple variance changes. A ‘time’ series of vertical ocean shear measurements is also analyzed, where a variety of nonstationary features are identified.

Linear disturbances from the construction of pipelines, roads and seismic lines for oil and gas extraction and mining have caused landscape changes in Western Canada; however these linear features are not well recorded. Inventory maps of pipelines, seismic lines and temporary access routes created by resource exploration are essential to understanding the processes causing ecological changes in order to coordinate resource development, emergency response and wildlife management. Mapping these linear disturbances traditionally relies on manual digitizing from very high resolution remote sensing data, which usually limits results to small operational area. Extending mapping to large areas is challenging due to complexity of image processing and high logistical costs. With increased availability of low cost satellite data, more frequent and regular observations are available and offer potential solutions for extracting information on linear disturbances. This paper proposes a novel approach to incorporate spectral, geometric and temporal information for detecting linear features based on time series data analysis of regularly acquired, and low cost satellite data. This approach involves two steps: multi-scale directional line detection and line updating based on time series analysis. This automatic method can effectively extract very narrow linear features, including seismic lines, roads and pipelines. The proposed method has been tested over three sites in Alberta, Canada by detecting linear disturbances occurring over the period of 1984–2013 using Landsat imagery. It is expected that extracted linear features would be used to facilitate preparation of baseline maps and up-to-date information needed for environmental assessment, especially in extended remote areas.

Decades of studies have suggested several criteria to detect interplanetary coronal mass ejections (ICME) in time series from in situ spacecraft measurements. Among them, the most common are an enhanced and smoothly rotating magnetic field, a low proton temperature, and a low plasma beta. However, these features are not all observed for each ICME due to their strong variability. Visual detection is time-consuming and biased by the observer interpretation, leading to non-exhaustive, subjective, and thus hardly reproducible catalogs. Using convolutional neural networks on sliding windows and peak detection, we provide a fast, automatic, and multi-scale detection of ICMEs. The method has been tested on the in situ data from WIND between 1997 and 2015, and on the 657 ICMEs that were recorded during this period. The method offers an unambiguous visual proxy of ICMEs that gives an interpretation of the data similar to what an expert observer would give. We found at a maximum 197 of the 232 ICMEs of the 2010–2015 period (recall 84% ± 4.5%), including 90% of the ICMEs present in the lists of Nieves-Chinchilla et al. and Chi et al. The minimal number of False Positives was 25 out of 158 predicted ICMEs (precision 84% ± 2.6%). Although less accurate, the method also works with one or several missing input parameters. The method has the advantage of improving its performance by just increasing the amount of input data. The generality of the method paves the way for automatic detection of many different event signatures in spacecraft in situ measurements.

The historical flood-level time series of the River Nile (AD 622–1470) is chosen to identify abrupt climate changes by applying global and local analysis techniques: the Mann–Kendall test and a non-hierarchical cluster analysis method to improve the Mann–Kendall test; a multiscale moving t-test with correction to the degree of freedom and an antisymmetric wavelet transform. The global estimates show three distinct epochs, AD 622–1078, 1079–1325 and 1326–1470, coinciding with larger scale climate changes: a relatively cool age, the Little Climatic Optimum of the Middle Ages, and an interim period before the Little Ice Age. The local estimates reveal the following results. The reference time of abrupt changes can be clearly identified, the associated time-scale coincides with the persistent anomaly period, and the maximum absolute t-value is statistically significant. There are about eight almost synchronous abrupt changes in the minimum and maximum River Nile flood levels, many of them are associated with 35–45 year persistence time-scales. An association of these short time-scales with those of interdecadal variability reported for the mid- and high-latitude sea-surface temperature of the North Atlantic is suggested, although information on phase coherence is not available. ©1997 by the Royal Meteorological Society. Int. J Climatol., 17: 1301–1315 (1997) (No. of Figures: 9 No. of Tables: 1 No. of

Usefulness of archive C-band Synthetic Aperture Radar (SAR) imagery and recently acquired X-band SAR data was explored in Nasca (Peru) for condition monitoring of cultural features threatened by various natural and anthropogenic hazards. Amplitude information from medium resolution ENVISAT ASAR IS2 time series (2003–2007) acquired along ascending and descending orbits was exploited to depict recent landscape changes. Beneficial impacts on temporal analysis of surface processes are discussed, also in light of the promising perspectives of high resolution SpotLight TerraSAR-X images for feature detection. The approach demonstrates its suitability for regional/local-scale applications.

The historical flood-level time series of the River Nile (AD 622–1470) is chosen to identify abrupt climate changes by applying global and local analysis techniques: the Mann–Kendall test and a non-hierarchical cluster analysis method to improve the Mann–Kendall test; a multiscale moving t-test with correction to the degree of freedom and an antisymmetric wavelet transform. The global estimates show three distinct epochs, AD 622–1078, 1079–1325 and 1326–1470, coinciding with larger scale climate changes: a relatively cool age, the Little Climatic Optimum of the Middle Ages, and an interim period before the Little Ice Age. The local estimates reveal the following results. The reference time of abrupt changes can be clearly identified, the associated time-scale coincides with the persistent anomaly period, and the maximum absolute t-value is statistically significant. There are about eight almost synchronous abrupt changes in the minimum and maximum River Nile flood levels, many of them are associated with 35–45 year persistence time-scales. An association of these short time-scales with those of interdecadal variability reported for the mid- and high-latitude sea-surface temperature of the North Atlantic is suggested, although information on phase coherence is not available. ©1997 by the Royal Meteorological Society. Int. J Climatol., 17: 1301–1315 (1997) (No. of Figures: 9 No. of Tables: 1 No. of