Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011

2013 ◽  
Vol 79 ◽  
pp. 44-52 ◽  
Author(s):  
Mirela G. Tulbure ◽  
Mark Broich
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Time Series Data ◽  
Water Bodies ◽  
Series Data ◽  
Spatiotemporal Dynamic ◽  
Surface Water Bodies

scholarly journals Automatic processing of Sentinel-2 image for Kerch peninsula lake areas extraction using QGIS and Python

2020 ◽  
Vol 203 ◽  
pp. 03011
Author(s):  
Denis Krivoguz ◽  
Sergei Mal’ko ◽  
Raisa Borovskaya ◽  
Anna Semenova
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Remote Monitoring ◽  
Technological Development ◽  
Climatic Changes ◽  
Water Bodies ◽  
The Past ◽  
Kerch Peninsula ◽  
Surface Water Bodies ◽  
Sentinel 2

Climatic changes that have occurred over the past decades, with an acceleration of urbanization of territories and technological development, leads to the significant changes not only in the atmosphere, but also in the Earth’s surface. Surface water bodies are one of these components. Today there are 3 main methods of monitoring water bodies -field, remote and combined. In this paper, we show the possibility of automating remote monitoring of water bodies using QGIS, Python and Sentinel-2 data of the main and largest lakes of the Kerch Peninsula. Having analysed both the available satellite data and the features of the study area, we came to the conclusion that it is advisable to use the NDWI index instead of the mNDWI. After processing and analysing the Sentinel-2 data for 2018 using the data processing model presented in the work, we obtained time series of changes in the areas of the studied lakes of the Kerch Peninsula.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

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

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