Spatio-temporal evolution of crop fields in Sentinel-2 Satellite Image Time Series

2017 ◽  
Author(s):  
Yady Tatiana Solano-Correa ◽  
Francesca Bovolo ◽  
Lorenzo Bruzzone ◽  
Diego Fernandez-Prieto
Keyword(s):  
Time Series ◽  
Temporal Evolution ◽  
Satellite Image ◽  
Crop Fields ◽  
Spatio Temporal ◽  
Sentinel 2

scholarly journals SPATIO-TEMPORAL OBJECT STABILITY FOR MONITORING EVOLVING AREAS IN SATELLITE IMAGE TIME SERIES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1273-1280
Author(s):  
C. Tuna ◽  
F. Merciol ◽  
S. Lefèvre
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Temporal Stability ◽  
Space Time ◽  
Spatial And Temporal Scales ◽  
Temporal Object ◽  
Novel Method ◽  
Spatio Temporal ◽  
Sentinel 2 ◽  
Do So

Abstract. Monitoring observable processes in Satellite Image Time Series (SITS) is one of the crucial way to understand dynamics of our planet that is facing unexpected behaviors due to climate change. In this paper, we propose a novel method to assess the evolution of objects (and especially their surface) through time. To do so, we first build a space-time tree representation of image time series. The so-called space-time tree is a hierarchical representation of an image sequences into a nested set of nodes characterizing the observed regions at multiple spatial and temporal scales. Then, we measure for each node the spatial area occupied at each time sample, and we focus on its evolution through time. We thus define the spatio-temporal stability of each node. We use this attribute to identify and measure changing areas in a remotely-sensed scene. We illustrate the purpose of our method with some experiments in a coastal environment using Sentinel-2 images, and in a flood occurred area with Sentinel-1 images.

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES

2016 ◽  
Vol XLI-B3 ◽  
pp. 469-473
Author(s):  
D. Cerra ◽  
J. Bieniarz ◽  
R. Müller ◽  
P. Reinartz
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Reconstruction Error ◽  
Sparse Reconstruction ◽  
Shadow Mask ◽  
Spectral Evolution ◽  
Random Samples ◽  
Restoration Algorithm ◽  
Cloud Removal ◽  
Sentinel 2

In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

scholarly journals Spatio-temporal reasoning for the classification of satellite image time series

2012 ◽  
Vol 33 (13) ◽  
pp. 1805-1815 ◽  
Author(s):  
François Petitjean ◽  
Camille Kurtz ◽  
Nicolas Passat ◽  
Pierre Gançarski
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Temporal Reasoning ◽  
Spatio Temporal

scholarly journals Adjoint-based Direct Data Assimilation of Gnss Time Series for Optimizing Frictional Parameters and Predicting Postseismic Deformation Following the 2003 Tokachi-oki Earthquake

2020 ◽  
Author(s):  
Masayuki Kano ◽  
Shin'ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract PostseismicGlobal Navigation Satellite System (GNSS) time seriesfollowed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip andcan be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important toquantitativelycapture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developedanadjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault.The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip can be roughly reproduced but not in detail if the observation noise is included. The optimization of frictional parameters provides not only the reproduction ofpostseismic displacements used for the assimilation but also the improvement in the prediction skill of the following time series. Then, we appliedthe developed method to the observed GNSS time series for the first 15 d following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A-B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). The large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismicGNSS time series for the following 15 d. These characteristics can be also detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, isan effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals Extracting multilayer networks from Sentinel-2 satellite image time series

2020 ◽  
Vol 8 (S1) ◽  
pp. S26-S42 ◽  
Author(s):  
Roberto Interdonato ◽  
Raffaele Gaetano ◽  
Danny Lo Seen ◽  
Mathieu Roche ◽  
Giuseppe Scarpa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Image ◽  
European Space Agency ◽  
Image Data ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Case Scenario ◽  
Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

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