scholarly journals Satellite-derived sulphur dioxide (SO<sub>2</sub>) emissions from the 2014–2015 Holuhraun eruption (Iceland)

2018 ◽  
Author(s):  
Elisa Carboni ◽  
Tamsin A. Mather ◽  
Anja Schmidt ◽  
Roy G. Grainger ◽  
Melissa A. Pfeffer ◽  
...  
Keyword(s):  
Time Series ◽  
Sulphur Dioxide ◽  
Time Series Data ◽  
Remote Sensing Data ◽  
Optimal Estimation ◽  
Series Data ◽  
So2 Emissions ◽  
Atmospheric Sounding ◽  
Estimation Scheme ◽  
First Time

Abstract. The six-month-long 2014–2015 Holuhraun eruption was the largest in Iceland for 200 years, emitting huge quantities of sulphur dioxide (SO2) into the troposphere, at times overwhelming European anthropogenic emissions. Weather, terrain and latitude, made continuous ground-based or UV satellite sensor measurements challenging. Infrared Atmospheric Sounding Interferometer (IASI) data, is used to derive the first time-series of daily SO2 mass and vertical distribution over the eruption period. A new optimal estimation scheme is used to calculate daily SO2 fluxes and average e-folding time every twelve hours. The algorithm is used to estimate SO2 fluxes of up to 200 kt per day and a minimum total SO2 erupted mass of 4.4 ± 0.8 Tg. The average SO2 e-folding time was 2.4 ± 0.6 days. Where comparisons are possible, these results broadly agree with ground-based near-source measurements, independent remote-sensing data and model simulations of the eruption. The results highlight the importance of high-resolution time-series data to accurately estimate volcanic SO2 emissions.

scholarly journals Identification of dynamically homogeneous areas with time series segmentation of remote sensing data

2018 ◽  
Vol 42 (3) ◽  
pp. 447-456 ◽  
Author(s):  
D. E. Plotnikov ◽  
P. A. Kolbudaev ◽  
S. A. Bartalev
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Computation Time ◽  
Remote Sensing Data ◽  
Identification Accuracy ◽  
Series Data ◽  
Homogeneous Surface ◽  
Multi Temporal ◽  
Sensing Time

We propose a method of segmentation of remote sensing time series data, which exploits multi-temporal information to identify objects’ boundaries. Extracting homogeneous objects with similar temporal behavior, the method analyzes large volumes of multi-temporal input data in a piecewise way and produces a consistent output segmentation layer for large territories. Segment building logic is simplified to minimize the computation time, while objects’ boundary identification accuracy remains sufficient for remote monitoring and mapping of vegetation, and specifically, agricultural crops. At the Space Research Institute of the RAS, the proposed method is currently applied for automated on-line satellite imagery analysis for recognition and mapping of (winter and spring) crops on large territories and land-use evaluation. The method successfully deals with gaps in remote sensing time series data and performs well even when input images are contaminated with speckle noise. Due to its  ability to map dynamically homogeneous surface areas with partially missing data, the method provides a potential for their recovery.

scholarly journals A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems

2018 ◽  
Vol 29 (1) ◽  
pp. 846-857 ◽  
Author(s):  
Heba Al Nsour ◽  
Mohammed Alweshah ◽  
Abdelaziz I. Hammouri ◽  
Hussein Al Ofeishat ◽  
Seyedali Mirjalili
Keyword(s):  
Time Series ◽  
Error Rate ◽  
Time Series Data ◽  
Search Algorithm ◽  
Probabilistic Neural Network ◽  
Series Data ◽  
Grey Wolf ◽  
Classification Problems ◽  
Hybrid Classification ◽  
First Time

Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO algorithm along with the PNN for solving the classification of time series problem. PNN was used for obtaining the primary solution, and thereby the PNN weights were adjusted using the GWO for solving the time series data and further decreasing the error rate. In this study, the main goal was to investigate the application of the GWO algorithm along with the PNN classifier for improving the classification precision and enhancing the balance between exploitation and exploration in the GWO search algorithm. The hybrid GWO-PNN algorithm was used in this study, and the results obtained were compared with the published literature. The experimental results for six benchmark time series datasets showed that this hybrid GWO-PNN outperformed the PNN algorithm for the studied datasets. It has been seen that hybrid classification techniques are more precise and reliable for solving classification problems. A comparison with other algorithms in the published literature showed that the hybrid GWO-PNN could decrease the error rate and could also generate a better result for five of the datasets studied.

scholarly journals CROP AREA MAPPING BY INTELLIGENT PIXEL INFORMATION INFERRED USING 250M MODIS VEGETATION TIMESERIES IN TRANSBOUNDARY INDUS BASIN

2020 ◽  
Vol 1 (2) ◽  
pp. 32-35
Author(s):  
Muhammad Mohsin Khan ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Talha Mahmood ◽  
Saddam Hussain ◽  
Muhammad Sohail Waqas ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Remote Sensing Data ◽  
Secondary Data ◽  
Indus Basin ◽  
Series Data ◽  
Pixel Resolution ◽  
Error Matrix ◽  
Crop Area

Irrigation water could be managed properly by mapping area of various crops. Remote sensing data can provide useful Land Use Land Cover (LULC) for assessment of different crop area and change detection. The present study was carried out with core objective to map crop area within the Indus Basin’s transboundary. Four major crops (i.e. wheat, rice, cotton and sugarcane) were identified using Normalize Difference Vegetation Index (NDVI) time series that was picked up from MODIS sensors aboard Terra (EOS AM) and Aqua (EOS PM) satellites with 250m pixel resolution. Crop phonological information was used to train each pixel intelligently for interpretation of unanalyzed NDVI data into crops. Eight days of time series data was used for identification and mapping of various crops on the basis of their phenology for the years 2008, 2010 and 2013. Error matrix was prepared to reveal mapping accurateness and ground truthing was also done in particular canal commands within the Indus basin. Furthermore, the temporal variation in cropped area was determined and for accuracy check, secondary data was matched with prepared maps. LULC maps for year 2008, 2010 and 2013 were defined for Rabi and kharif seasons.

Automatic Linear Disturbance Footprint Extraction Based on Dense Time-Series Landsat Imagery

2014 ◽  
Author(s):  
Zhaohua Chen ◽  
Bill Jefferies ◽  
Paul Adlakha ◽  
Bahram Salehi ◽  
Des Power
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Oil And Gas ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Series Data ◽  
Linear Features ◽  
Seismic Lines

Linear disturbances from the construction of pipelines, roads and seismic lines for oil and gas extraction and mining have caused landscape changes in Western Canada; however these linear features are not well recorded. Inventory maps of pipelines, seismic lines and temporary access routes created by resource exploration are essential to understanding the processes causing ecological changes in order to coordinate resource development, emergency response and wildlife management. Mapping these linear disturbances traditionally relies on manual digitizing from very high resolution remote sensing data, which usually limits results to small operational area. Extending mapping to large areas is challenging due to complexity of image processing and high logistical costs. With increased availability of low cost satellite data, more frequent and regular observations are available and offer potential solutions for extracting information on linear disturbances. This paper proposes a novel approach to incorporate spectral, geometric and temporal information for detecting linear features based on time series data analysis of regularly acquired, and low cost satellite data. This approach involves two steps: multi-scale directional line detection and line updating based on time series analysis. This automatic method can effectively extract very narrow linear features, including seismic lines, roads and pipelines. The proposed method has been tested over three sites in Alberta, Canada by detecting linear disturbances occurring over the period of 1984–2013 using Landsat imagery. It is expected that extracted linear features would be used to facilitate preparation of baseline maps and up-to-date information needed for environmental assessment, especially in extended remote areas.

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.

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