scholarly journals Cumulated subsidence maps of Mexico city

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Penélope López Quiroz ◽  
Marie Pierre Doin ◽  
Iván Martínez Zazueta ◽  
Francisco Javier Osorno Covarrubias
Keyword(s):  
Time Series ◽  
Information System ◽  
Mexico City ◽  
Soil Compaction ◽  
Temporal Evolution ◽  
Synthetic Aperture ◽  
Radar Images ◽  
The World ◽  
Over Exploitation ◽  
Mexico Basin

A set of 38, 30 x 30 m resolution rasters of the subsidence over Mexico City and part of its metropolitan area, has been made available through this work. Subsidence rates in Mexico, one of the most populated cities in the world, reaches up to 0.4 m/yr, mainly due to soil compaction led by over exploitation of the Mexico Basin aquifer. To accurately map the spatial and temporal evolution of this phenomena, a set of Synthetic Aperture Radar images has been processed through interferometry and the results have been formatted as rasters that can be read and analyzed on any Geographic Information System. Moreover, the whole set of rasters can be visualized as interactive animated time series of the subsidence, through the journal platform.

scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

scholarly journals Measuring the state and temporal evolution of glaciers in Alaska and Yukon using synthetic-aperture-radar-derived (SAR-derived) 3D time series of glacier surface flow

2021 ◽  
Vol 15 (9) ◽  
pp. 4221-4239
Author(s):  
Sergey Samsonov ◽  
Kristy Tiampo ◽  
Ryan Cassotto
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Flow Velocity ◽  
Temporal Evolution ◽  
Surface Flow ◽  
Synthetic Aperture ◽  
Surface Geometry ◽  
Warming Climate ◽  
Sar Data ◽  
Aperture Radar

Abstract. Climate change has reduced global ice mass over the last 2 decades as enhanced warming has accelerated surface melt and runoff rates. Glaciers have undergone dynamic processes in response to a warming climate that impacts the surface geometry and mass distribution of glacial ice. Until recently no single technique could consistently measure the evolution of surface flow for an entire glaciated region in three dimensions with high temporal and spatial resolution. We have improved upon earlier methods by developing a technique for mapping, in unprecedented detail, the temporal evolution of glaciers. Our software computes north, east, and vertical flow velocity and/or displacement time series from the synthetic aperture radar (SAR) ascending and descending range and azimuth speckle offsets. The software can handle large volumes of satellite data and is designed to work on high-performance computers (HPCs) as well as workstations by utilizing multiple parallelization methods. We then compute flow velocity–displacement time series for glaciers in southeastern Alaska during 2016–2021 and observe seasonal and interannual variations in flow velocities at Seward and Malaspina glaciers as well as culminating phases of surging at Klutlan, Walsh, and Kluane glaciers. On a broader scale, this technique can be used for reconstructing the response of worldwide glaciers to the warming climate using archived SAR data and for near-real-time monitoring of these glaciers using rapid revisit SAR data from satellites, such as Sentinel-1 (6 or 12 d revisit period) and the forthcoming NISAR mission (12 d revisit period).

scholarly journals A Novel Method of Generating Deformation Time-Series Using Interferometric Synthetic Aperture Radar and Its Application in Mexico City

2018 ◽  
Vol 10 (11) ◽  
pp. 1741 ◽  
Author(s):  
Xiaying Wang ◽  
Qin Zhang ◽  
Chaoying Zhao ◽  
Feifei Qu ◽  
Juqing Zhang
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Mexico City ◽  
Land Subsidence ◽  
Functional Model ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Deformation Pattern ◽  
Interferometric Synthetic Aperture Radar ◽  
Aperture Radar

As a result of rapid societal development and urbanization, the pumping of groundwater has gradually increased. Land subsidence has thus become a common geological disaster, which can result in huge economic losses. Interferometric synthetic aperture radar (InSAR), with its large-scale and high-accuracy monitoring characteristics, can attain information on Earth surface deformation using the interferometric phase between couples of SAR images acquired at different times. Time-series results for the ground surface are the key information required to understand the deformation pattern and further study the reason for the subsidence. However, in recent research, most methods for resolving time-series deformation—like the Berardino method—that use residuals in functional model solving and distinguish high-pass displacement and the atmospheric component by filtering do not generally work well and functional models focusing on prior information in the time-series solution process are not always available. In this paper, to solve the above problems, 34 Sentinel-1A descending mode scenes of Mexico City captured between 2015/04/13 and 2016/09/10 are used as experimental data. Firstly, a new functional model is provided to obtain the deformation time-series. The nonlinear deformation and atmospheric phase are combined as an unknown parameter and the method of singular value decomposition (SVD) is used to solve this variable. The nonlinear displacement and atmospheric phase are then separated by the singular spectrum analysis (SSA) method. Finally, the total land subsidence time-series is obtained by adding together the linear displacement and nonlinear displacement. Two typical methods and the proposed method were compared using both unit weights and adaptive weights. The experimental results show that the proposed method can obtain a more accurate time-series deformation result. Moreover, the different weights do not result in significant differences and the solved atmospheric and nonlinear phases have good consistency with the interferogram phase.

scholarly journals Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada

2009 ◽  
Vol 67 (4) ◽  
pp. 638-645 ◽  
Author(s):  
Daniel Ricard ◽  
Robert M. Branton ◽  
Donald W. Clark ◽  
Peter Hurley
Keyword(s):  
Time Series ◽  
Information System ◽  
Trawl Survey ◽  
Independent Information ◽  
Stock Assessments ◽  
The World ◽  
Abundance Trends ◽  
Selected Subset ◽  
Temporal Abundance ◽  
Trawl Surveys

Abstract Ricard, D., Branton, R. M., Clark, D. W., and Hurley, P. 2010. Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada. – ICES Journal of Marine Science, 67: 638–645. Scientific trawl surveys have been conducted in different regions of the world and by a variety of countries and agencies since the mid-1900s. Although the data are collected in a scientifically and statistically appropriate context and represent an important source of fishery-independent information for agency-specific stock assessments, their use and dissemination has often been limited to the agencies conducting the surveys. In recent years, Internet data portals such as the Ocean Biogeographic Information System have provided an arena for the wider distribution and use of marine fish data. Despite the increased accessibility of such data, their scientific acceptability has been limited by a lack of reproducibility in data analyses. We present a methodology for the computation of time-series of groundfish stock indices using publicly available trawl survey data derived from the Canadian Department of Fisheries and Oceans Maritimes region. Potential pitfalls associated with the computation of time-series are discussed and proper stratified random estimates of temporal abundance trends are compared with other methods for a selected subset of species. Also, the broader applicability of the methods for datasets collected under similar sampling designs is discussed, along with the reproducibility of the analyses and results.

scholarly journals Trend of epidemiological indicators of leprosy in an endemic state of the Amazon region

2021 ◽  
Vol 42 ◽  
Author(s):  
Maria Eduarda de Macedo Basso ◽  
Rosemary Ferreira de Andrade ◽  
Rodrigo Luís Ferreira da Silva
Keyword(s):  
Time Series ◽  
Information System ◽  
Linear Regression ◽  
Detection Rate ◽  
Temporal Trend ◽  
Temporal Evolution ◽  
Late Diagnosis ◽  
Time Series Study ◽  
Series Study ◽  
Endemic State

ABSTRACT Objective To characterize the temporal trend of epidemiological indicators of leprosy in the State of Amapá. Method Time series study, carried out in the Notifiable Diseases Information System. The indicators analyzed were: annual detection rate of new cases, detection rate of new cases in the population from 0 to 14 years old, rate of new cases with grade 2 of disability, proportion of new cases with grade 2 and proportion of new multibacillary cases, between 2005 and 2018. The analysis of the temporal evolution was made by linear regression. Results The detection rate of new cases and the rate of children under 15 years showed a decreasing trend. The rate of new cases with grade 2 of disability and the proportion of cases with grade 2 showed oscillation. The proportions of multibacillary remained constant. Conclusion The epidemiological indicators analyzed suggest active transmission and late diagnosis, signaling a possible hidden endemic disease.

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 Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

scholarly journals Development of a Radar Reflector Kit for Older Adults to Use to Signal Their Location and Needs in a Large-Scale Earthquake Disaster

2021 ◽  
Vol 13 (10) ◽  
pp. 1883
Author(s):  
Yuma Morisaki ◽  
Makoto Fujiu ◽  
Ryoichi Furuta ◽  
Junichi Takayama
Keyword(s):  
Older Adults ◽  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Disaster Response ◽  
Large Scale ◽  
Synthetic Aperture ◽  
Earthquake Disaster ◽  
Large Numbers ◽  
The World ◽  
Vulnerable People

In Japan, older adults account for the highest proportion of the population of any country in the world. When large-scale earthquake disasters strike, large numbers of casualties are known to particularly occur among seniors. Many are physically or mentally vulnerable and require assistance during the different phases of disaster response, including rescue, evacuation, and living in an evacuation center. However, the growing number of older adults has made it difficult, after a disaster, to quickly gather information on their locations and assess their needs. The authors are developing a proposal to enable vulnerable people to signal their location and needs in the aftermath of a disaster to response teams by deploying radar reflectors that can be detected in synthetic aperture radar (SAR) satellite imagery. The purpose of this study was to develop a radar reflector kit that seniors could easily assemble in order to make this proposal feasible in practice. Three versions of the reflector were tested for detectability, and a sample of older adults was asked to assemble the kits and provide feedback regarding problems they encountered and regarding their interest in using the reflectors in the event of a large-scale disaster.

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