scholarly journals TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment

2021 ◽  
Vol 10 (4) ◽  
pp. 267
Author(s):  
Inder Tecuapetla-Gómez ◽  
Gerardo López-Saldaña ◽  
María Isabel Cruz-López ◽  
Rainer Ressl
Keyword(s):  
Time Series ◽  
Open Source ◽  
Satellite Data ◽  
Time Series Data ◽  
Earth Observation ◽  
Quality Analysis ◽  
Quality Data ◽  
Series Data ◽  
Data Handling ◽  
Geographical Regions

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

A review of irregular time series data handling with gated recurrent neural networks

2021 ◽  
Vol 441 ◽  
pp. 161-178
Author(s):  
Philip B. Weerakody ◽  
Kok Wai Wong ◽  
Guanjin Wang ◽  
Wendell Ela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Handling

scholarly journals OSARIS, the “Open Source SAR Investigation System” for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications

2019 ◽  
Vol 7 ◽  
Author(s):  
David Loibl ◽  
Bodo Bookhagen ◽  
Sébastien Valade ◽  
Christoph Schneider
Keyword(s):  
Time Series ◽  
Open Source ◽  
Time Series Data ◽  
Series Data

scholarly journals TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

2011 ◽  
Vol 12 (1) ◽  
pp. 119 ◽  
Author(s):  
Michael Lindner ◽  
Raul Vicente ◽  
Viola Priesemann ◽  
Michael Wibral
Keyword(s):  
Time Series ◽  
Open Source ◽  
Information Flow ◽  
Time Series Data ◽  
Transfer Entropy ◽  
Series Data

scholarly journals rhizoTrak: A flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons

2019 ◽  
Author(s):  
Birgit Möller ◽  
Hongmei Chen ◽  
Tino Schmidt ◽  
Axel Zieschank ◽  
Roman Patzak ◽  
...  
Keyword(s):  
Time Series ◽  
Open Source ◽  
Data Exchange ◽  
Time Series Data ◽  
Full Range ◽  
Open Data ◽  
Series Data ◽  
Root Turnover ◽  
User Friendly ◽  
Time Series Images

AbstractBackground and aimsMinirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires intensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak.Methods and resultsrhizoTrak builds on TrakEM2 and is publically available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving conflicts. It facilitates easy data exchange with other software by supporting open data formats.ConclusionsrhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.

scholarly journals Using SAR satellite data time-series for regional glacier mapping

2017 ◽  
Author(s):  
Solveig H. Winsvold ◽  
Andreas Kääb ◽  
Christopher Nuth ◽  
Liss M. Andreassen ◽  
Ward van Pelt ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Time Series Data ◽  
Series Representation ◽  
Series Data ◽  
Landsat 8 ◽  
Change Analysis ◽  
Surface Mass ◽  
Sentinel 2A ◽  
Optical Satellite Images

Abstract. With dense SAR satellite data time-series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and Radarsat-2 backscatter images over mainland Norway and Svalbard, we have used descriptive methods for outlining the possibilities of using SAR time-series for mapping glaciers. We present five application scenarios, where the first shows potential for tracking transient snow lines with SAR backscatter time-series, and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time-series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time-series together with a coupled energy balance and multi-layer firn model. We find strong correlation between backscatter signals with both the modeled firn air-content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time-series, revealing important information about the area extent of internal accumulation. Finally, in the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time-series data. Our results reveal the potential of satellite imagery for automatically derived products as important input in modeling assessments and glacier change analysis.

scholarly journals PhilDB - The time series database with built-in change logging

2015 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

scholarly journals Characterizing bracken fern phenological cycle using time series data derived from Sentinel-2 satellite sensor

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257196
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda
Keyword(s):  
Time Series ◽  
Invasive Plant ◽  
Time Series Data ◽  
Model Fitting ◽  
Quality Data ◽  
Series Data ◽  
Bracken Fern ◽  
Phenological Cycle ◽  
Phenological Metrics ◽  
Sentinel 2

Bracken fern is an invasive plant that has caused serious disturbances in many ecosystems due to its ability to encroach into new areas swiftly. Adequate knowledge of the phenological cycle of bracken fern is required to serve as an important tool in formulating management plans to control the spread of the fern. This study aimed to characterize the phenological cycle of bracken fern using NDVI and EVI2 time series data derived from Sentinel-2 sensor. The TIMESAT program was used for removing low quality data values, model fitting and for extracting bracken fern phenological metrics. The Sentinel-2 satellite-derived phenological metrics were compared with the corresponding bracken fern phenological events observed on the ground. Findings from our study revealed that bracken fern phenological metrics estimated from satellite data were in close agreement with ground observed phenological events with R2 values ranging from 0.53–0.85 (p < 0.05). Although they are comparable, our study shows that NDVI and EVI2 differ in their ability to track the phenological cycle of bracken fern. Overall, EVI2 performed better in estimating bracken fern phenological metrics as it related more to ground observed phenological events compared to NDVI. The key phenological metrics extracted in this study are critical for improving the precision in the controlling of the spread of bracken fern as well as in implementing active protection strategies against the invasion of highly susceptible rangelands.

scholarly journals A Comparison of ArcGIS and QGIS for Animation

2017 ◽  
pp. 23-32
Author(s):  
Owen Stuckey
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Open Source ◽  
Time Series Data ◽  
Series Data ◽  
Data Input ◽  
Input Processing ◽  
Similarities And Differences ◽  
Baseline Cost ◽  
Criteria For Evaluation

I compare two GIS programs which can be used to create cartographic animations—the commercial Esri ArcGIS and the free and open-source QGIS. ArcGIS implements animation through the “Time Slider” while QGIS uses a plugin called “TimeManager.” There are some key similarities and differences as well as functions unique to each plugin. This analysis examines each program’s capabilities in mapping time series data. Criteria for evaluation include the number of steps, the number of output formats, input of data, processing, output of a finished animation, and cost. The comparison indicates that ArcGIS has more control in input, processing, and output of animations than QGIS, but has a baseline cost of $100 per year for a personal license. In contrast, QGIS is free, uses fewer steps, and enables more output formats. The QGIS interface can make data input, processing, and output of an animation slower.

Automatic offset detection using R open source libraries

2021 ◽  
Author(s):  
Shambo Bhattacharjee ◽  
Alvaro Santamaría-Gómez
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Open Source ◽  
Change Point ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Position Time Series ◽  
Gnss Time Series ◽  
Gnss Position Time Series

&lt;p&gt;Long GNSS position time series contain offsets typically at rates between 1 and 3 offsets per decade. We may classify the offsets whether their epoch is precisely known, from GNSS station log files or Earthquake databases, or unknown. Very often, GNSS position time series contain offsets for which the epoch is not known a priori and, therefore, an offset detection/removal operation needs to be done in order to produce continuous position time series needed for many applications in geodesy and geophysics. A further classification of the offsets corresponds to those having a physical origin related to the instantaneous displacement of the GNSS antenna phase center (from Earthquakes, antenna changes or even changes of the environment of the antenna) and those spurious originated from the offset detection method being used (manual/supervised or automatic/unsupervised). Offsets due to changes of the antenna and its environment must be avoided by the station operators as much as possible. Spurious offsets due to the detection method must be avoided by the time series analyst and are the focus of this work.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Even if manual offset detection by expert analysis is likely to perform better, automatic offset detection algorithms are extremely useful when using massive (thousands) GNSS time series sets. Change point detection and cluster analysis algorithms can be used for detecting offsets in a GNSS time series data and R offers a number of libraries related to performing these two. For example, the &amp;#8220;Bayesian Analysis of Change Point Problems&amp;#8221; or the &amp;#8220;bcp&amp;#8221; helps to detect change points in a time series data. Similarly, the &amp;#8220;dtwclust&amp;#8221; (Dynamic Time Warping algorithm) is used for the time series cluster analysis. Our objective is to assess various open-source R libraries for the automatic offset detection.&lt;/p&gt;

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