Detecting ground motion in Schleswig-Holstein from radar satellite data

Mapping Intimacies ◽

10.5194/egusphere-egu21-13454 ◽

2021 ◽

Author(s):

Dieter Hoogestraat ◽

Henriette Sudhaus ◽

Andreas Omlin

Keyword(s):

Time Series ◽

Ground Motion ◽

Time Series Data ◽

Ground Motions ◽

Anthropogenic Sources ◽

Series Data ◽

Small Scale ◽

Near Surface ◽

Radar Images ◽

Persistent Scatterers

The near-surface geology of northern Germany is characterized by glacial deposits, deformed by rising Permian and Upper Triassic salt structures. Ground motions potentially associated with salt tectonic processes are very slow and are superimposed by signals of e.g. hydrological and anthropogenic sources. To measure them requires the detection of motion rates in the range of a few millimeters per year with sufficient spatial coverage. For large areas little is known about the rates and the characteristics of ground motions, even though they directly affect anthropogenic infrastructure and could have an impact on the future use of the underground for storage purposes or the exploitation of geothermal energy.To measure ground motion, we use radar interferometric time series data provided by the German Aerospace Center and the Federal Institute for Geosciences and Natural Resources' Ground motion service. These data are based on Synthetic Aperture Radar images acquired by ESA's ERS and Sentinel satellites. Time-series analyses are possible for temporally stable backscattering objects (persistent scatterers) on the ground. Generally, this results in spatially dense observations over built-up areas and sparse observations over rural areas.We use a set of geostatistical methods to analyze these time series data. We see signals of large-scale surface-deforming processes such as the subsidence of the marshes and small-scale signals like the swelling of Permian anhydrite at the Segeberger "Kalkberg". And we can observe subsidence processes over the historic town of L&#252;beck.Our work extends the area of application of the PS-InSAR technique from areas with high motion rates to regions with particulary low motion rates. We discuss methods that can be used to link ERS data to the Sentinel-1 data, in particular, to separate long-term motion processes from short-term effects. We are working on techniques that shall help to decompose different signal sources. Finally, we aim to prepare a set of tools, that can be used by the community.

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Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-19-0287.1 ◽

2020 ◽

Vol 77 (8) ◽

pp. 2921-2940

Author(s):

Amandine Kaiser ◽

Davide Faranda ◽

Sebastian Krumscheid ◽

Danijel Belušić ◽

Nikki Vercauteren

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

Time Series Data ◽

Temperature Inversion ◽

Series Data ◽

Small Scale ◽

Dynamical Regime ◽

Near Surface ◽

Nonstationary Field ◽

Regime Transitions

Abstract Many natural systems undergo critical transitions, i.e., sudden shifts from one dynamical regime to another. In the climate system, the atmospheric boundary layer can experience sudden transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in polar regions or at night when the atmospheric boundary layer is stably stratified, and they have important consequences in the strength of mixing with the higher levels of the atmosphere. To analyze the stable boundary layer, many approaches rely on the identification of regimes that are commonly denoted as weakly and very stable regimes. Detecting transitions between the regimes is crucial for modeling purposes. In this work a combination of methods from dynamical systems and statistical modeling is applied to study these regime transitions and to develop an early warning signal that can be applied to nonstationary field data. The presented metric aims to detect nearing transitions by statistically quantifying the deviation from the dynamics expected when the system is close to a stable equilibrium. An idealized stochastic model of near-surface inversions is used to evaluate the potential of the metric as an indicator of regime transitions. In this stochastic system, small-scale perturbations can be amplified due to the nonlinearity, resulting in transitions between two possible equilibria of the temperature inversion. The simulations show such noise-induced regime transitions, successfully identified by the indicator. The indicator is further applied to time series data from nocturnal and polar meteorological measurements.

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Multivariate analysis of nonlinearity in sandbar behavior

Nonlinear Processes in Geophysics ◽

10.5194/npg-15-145-2008 ◽

2008 ◽

Vol 15 (1) ◽

pp. 145-158 ◽

Cited By ~ 12

Author(s):

L. Pape ◽

B. G. Ruessink

Keyword(s):

Time Series ◽

Time Series Data ◽

Nonlinear Models ◽

Multivariate Time Series ◽

Series Data ◽

Small Scale ◽

Synthetic Datasets ◽

Long Term Prediction ◽

Sandbar Migration

Abstract. Alongshore sandbars are often present in the nearshore zones of storm-dominated micro- to mesotidal coasts. Sandbar migration is the result of a large number of small-scale physical processes that are generated by the incoming waves and the interaction between the wave-generated processes and the morphology. The presence of nonlinearity in a sandbar system is an important factor determining its predictability. However, not all nonlinearities in the underlying system are equally expressed in the time-series of sandbar observations. Detecting the presence of nonlinearity in sandbar data is complicated by the dependence of sandbar migration on the external wave forcings. Here, a method for detecting nonlinearity in multivariate time-series data is introduced that can reveal the nonlinear nature of the dependencies between system state and forcing variables. First, this method is applied to four synthetic datasets to demonstrate its ability to qualify nonlinearity for all possible combinations of linear and nonlinear relations between two variables. Next, the method is applied to three sandbar datasets consisting of daily-observed cross-shore sandbar positions and hydrodynamic forcings, spanning between 5 and 9 years. Our analysis reveals the presence of nonlinearity in the time-series of sandbar and wave data, and the relative importance of nonlinearity for each variable. The relation between the results of each sandbar case and patterns in bar behavior are discussed, together with the effects of noise. The small effect of nonlinearity implies that long-term prediction of sandbar positions based on wave forcings might not require sophisticated nonlinear models.

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Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea

Remote Sensing ◽

10.3390/rs12213505 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3505

Author(s):

Muhammad Fulki Fadhillah ◽

Arief Rizqiyanto Achmad ◽

Chang-Wook Lee

Keyword(s):

Time Series ◽

South Korea ◽

Metropolitan Area ◽

Land Subsidence ◽

System Analysis ◽

Time Series Data ◽

Series Data ◽

Seoul Metropolitan Area ◽

Persistent Scatterers ◽

Multiclass Classifier

The aims of this research were to map and analyze the risk of land subsidence in the Seoul Metropolitan Area, South Korea using satellite interferometric synthetic aperture radar (InSAR) time-series data, and three ensemble machine-learning models, Bagging, LogitBoost, and Multiclass Classifier. Of the types of infrastructure present in the Seoul Metropolitan Area, subway lines may be vulnerable to land subsidence. In this study, we analyzed Persistent Scatterer InSAR time-series data using the Stanford Method for Persistent Scatterers (StaMPS) algorithm to generate a deformation time-series map. Subsidence occurred at four locations, with a deformation rate that ranged from 6–12 mm/year. Subsidence inventory maps were prepared using deformation time-series data from Sentinel-1. Additionally, 10 potential subsidence-related factors were selected and subjected to Geographic Information System analysis. The relationship between each factor and subsidence occurrence was analyzed by using the frequency ratio. Land subsidence susceptibility maps were generated using Bagging, Multiclass Classifier, and LogitBoost models, and map validation was carried out using the area under the curve (AUC) method. Of the three models, Bagging produced the largest AUC (0.883), with LogitBoost and Multiclass Classifier producing AUCs of 0.871 and 0.856, respectively.

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Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data

Forecasting ◽

10.3390/forecast3040051 ◽

2021 ◽

Vol 3 (4) ◽

pp. 850-867

Author(s):

Guoqi Qian ◽

Antoinette Tordesillas ◽

Hangfei Zheng

Keyword(s):

Time Series ◽

Ground Motion ◽

Slope Failure ◽

Time Series Data ◽

Warning System ◽

Threshold Level ◽

Series Data ◽

High Dimensional ◽

Landslide Risk ◽

Landslide Data

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance.

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Let's Do the Time Warp Again: Non-linear time series matching as a tool for sequentially structured data in ecology

10.1101/2021.04.19.440490 ◽

2021 ◽

Author(s):

Jens C Hegg ◽

Brian P Kennedy

Keyword(s):

Time Series ◽

Time Series Data ◽

Linear Time ◽

Temporal Structure ◽

Structured Data ◽

Series Data ◽

Small Scale ◽

Temporal Data ◽

Ecological Data ◽

Aggregation Techniques

Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contains information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic Time Warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but any sequentially structured dataset, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small-scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological datasets. In this study we use an example dataset of high-resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith datasets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered "pseudo time series" data such as classification of species by shape.

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In situ quantification of supraglacial cryoconite morphodynamics using time-lapse imaging: an example from Svalbard

Journal of Glaciology ◽

10.3189/002214311797409695 ◽

2011 ◽

Vol 57 (204) ◽

pp. 651-657 ◽

Cited By ~ 31

Author(s):

Tristram D.L. Irvine-Fynn ◽

Jonathan W. Bridge ◽

Andrew J. Hodson

Keyword(s):

Time Series ◽

High Resolution ◽

Time Series Data ◽

Time Lapse ◽

Random Motion ◽

Biologically Active ◽

Series Data ◽

Small Scale ◽

Meteorological Forcing ◽

Ice Surface

AbstractThere is growing recognition of the significance of biologically active supraglacial dust (cryoconite) for glacial mass balance and ecology. Nonetheless, the processes controlling the distribution, transport and fate of cryoconite particles in the glacial system remain somewhat poorly understood. Here, using a 216 hour time series of plot-scale (0.04 m2) images, we quantify the small-scale dynamics of cryoconite on Longyearbreen, Svalbard. We show significant fluctuations in the apparent cryoconite area and dispersion of cryoconite over the plot, within the 9 day period of observations. However, the net movement of cryoconite across the ice surface averaged only 5.3 mm d−1. High-resolution measurements of cryoconite granule motion showed constant, random motion but weak correlation with meteorological forcing factors and no directional trends for individual particle movement. The high-resolution time-series data suggest that there is no significant net transport of dispersed cryoconite material across glacier surfaces. The areal coverage and motion of particles within and between cryoconite holes appears to be a product of differential melting leading to changes in plot-scale microtopography, local meltwater flow dynamics and weather-dependent events. These subtle processes of cryoconite redistribution may be significant for supraglacial albedo and have bearing on the surface energy balance at the glacier scale.

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Use of GRACE time-series data for estimating groundwater storage at small scale

Journal of Earth System Science ◽

10.1007/s12040-020-01465-2 ◽

2020 ◽

Vol 129 (1) ◽

Author(s):

Ch Samurembi Chanu ◽

Harika Munagapati ◽

V M Tiwari ◽

Arvind Kumar ◽

L Elango

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Small Scale ◽

Groundwater Storage

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Monitoring deciduous tree phenology estimates with Sentinel-2, phenocam and field measurements in Ireland

10.5194/egusphere-egu21-8038 ◽

2021 ◽

Author(s):

Gourav Misra ◽

Fiona Cawkwell ◽

Astrid Wingler

Keyword(s):

Time Series ◽

Time Series Data ◽

Vegetation Indices ◽

Tree Canopy ◽

Deciduous Tree ◽

Series Data ◽

Near Surface ◽

Phenological Data ◽

Sentinel 2 ◽

Daily Time

Phenology is an important driver of ecosystem performance. However, studies of phenology in Ireland have been limited by the availability of data at high spatial and temporal resolutions. The new suite of Sentinel-2 sensors, with their enhanced spatial and temporal resolutions might help overcome some of these challenges. Additionally, the presence of red edge bands in the Sentinel-2 sensors provides a unique opportunity to evaluate the performance of different vegetation indices in tracking near surface (phenocam) and ground/laboratory measures of phenology. In this study, we present our initial analyses for the year 2020. Nine common lime trees (Tilia x europaea) on the University College Cork campus (Cork, Ireland) and three undisturbed broadleaf woodland sites from the National Park and Wildlife Services (NPWS) survey were selected. The phenology of these sites was analyzed from satellite derived vegetation indices of NDVI, EVI, GNDVI and NDRE. The available 24 cloud free Sentinel-2 images were pre-processed and interpolated to daily time steps. The start of season (SOS), position of peak (POP) and end of season (EOS) were then extracted from the daily time series using the half amplitude and maximum value method. Similarly, daily data from a phenocam overlooking three of the lime trees were processed to extract the phenological dates. Weekly measurements of leaf chlorophyll or chlorophyll content index (CCI) and maximum photosystem II efficiency (Fv/Fm) by sampling five leaves from each lime tree were made during June to November of 2020. Preliminary results indicate that different vegetation indices vary in their correlation to ground and phenocam observations. The dates of SOS, POP and EOS obtained from Sentinel-2 do not exactly match the ground and phenocam observations, nor are the different indices coincident with each other, with maximum deviations of up to a month and a week for EOS and SOS respectively. The phenological metrics estimated from the EVI time series were in general earlier (i.e. 116, 162 and 270 day of year for SOS, POP and EOS respectively) and those from the NDRE were the last (i.e. 131, 211 and 288 day of year for SOS, POP and EOS respectively). Although local differences were observed in the field, the Sentinel-2 time series data were shown to perform well in tracking the autumn phenology, and in most cases the observed mismatches in phenological data could be ascribed to differences in the scale of observations i.e. pixel vs point comparisons and on spectral basis i.e. sensor vs instrument for measuring CCI. A steeper drop in CCI and Fv/Fm values was also observed in the late autumn period. Such differences in the progression of each time series curve can possibly lead to mismatches in the phenology estimated from vegetation indices and from observations. Other mismatches could also emanate from the fact that field sampling of leaves was done from below the canopy whereas the satellite view of canopy is from the top. Experience from the field revealed differences in the rates of greening and yellowing of the leaves in different regions of the tree canopy.

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