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

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.

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

A Phenology Modelling Using MODIS Time Series Data in South Korea.

2014 ◽  
Vol 47 (3) ◽  
pp. 186-193 ◽  
Author(s):  
Nam-Shin Kim ◽  
◽  
Yong-Chan Cho ◽  
Seung-Hwan Oh ◽  
Hye-Jin Kwon ◽  
...  
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Series Data

scholarly journals Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

2021 ◽  
Vol 13 (3) ◽  
pp. 1283
Author(s):  
Ki-Hong Choi ◽  
Insin Kim
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Tail Dependence ◽  
Tourism Industry ◽  
Series Data ◽  
Dynamic Copula ◽  
Source Countries ◽  
Secondary Time ◽  
Dependence Structures

Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence structures exist between tourist flows into South Korea from five major source countries, as South Korea has undergone fluctuations in tourist arrivals due to diverse circumstances and has complex relations with tourism source countries. Additionally, the study examines the structures of extreme tail dependence, which is indicated in the case of unexpected events, and identifies how co-movements vary over time through dynamic copula–GARCH (generalized autoregressive conditional heteroskedasticity) tests. The secondary time series data for the 2005–2019 period of tourist arrivals to Korea were derived from the Korea Tourism Knowledge and Information System for testing the copula models. The copula estimations indicate significant dependencies among all market pairs as well as the strongest dependence between China and Taiwan. Moreover, extreme tail dependence structures show co-movements for four pairs of tourism markets in only negative shocks, for five pairs in both positive and negative conditions, but no co-movement in the China–Taiwan pair. Finally, the dynamic dependence structures reveal that the China–Taiwan dependence is higher than the other time-varying dependence structures, implying that the two markets complement each other.

Detecting ground motion in Schleswig-Holstein from radar satellite data

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

<p>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.</p><p>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.</p><p>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 Lübeck.</p><p>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.</p>

scholarly journals Discovering Spatial-temporal Patterns of COVID-19 Pandemic in South Korea: A Data-driven Approach using Dynamic Mode Decomposition

2021 ◽  
Author(s):  
Sungchan Kim ◽  
Minseok Kim ◽  
Sunmi Lee ◽  
Young Ju Lee
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Temporal Patterns ◽  
Dynamic Mode Decomposition ◽  
Series Data ◽  
Dynamic Mode ◽  
Regional Patterns ◽  
Effective Prevention ◽  
Mode Decomposition

Abstract A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in Daegu and Gyeongbuk area but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

scholarly journals Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sungchan Kim ◽  
Minseok Kim ◽  
Sunmi Lee ◽  
Young Ju Lee
Keyword(s):  
Time Series ◽  
South Korea ◽  
Time Series Data ◽  
Spatiotemporal Patterns ◽  
Dynamic Mode Decomposition ◽  
Series Data ◽  
Dynamic Mode ◽  
Regional Patterns ◽  
Effective Prevention ◽  
Mode Decomposition

AbstractA novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial–temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial–temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

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