An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

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A history of university income in the United Kingdom and Australia, 1922–2017

History of Education Review ◽

10.1108/her-06-2020-0040 ◽

2020 ◽

Vol 49 (2) ◽

pp. 229-248

Author(s):

Tamson Pietsch

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Content Type ◽

The United Kingdom ◽

History Of University ◽

History Of ◽

Strategic Influence ◽

The Uk ◽

Comparable Time

PurposeThe purpose of this paper is to create comparable time series data on university income in Australia and the UK that might be used as a resource for those seeking to understand the changing funding profile of universities in the two countries and for those seeking to investigate how such data were produced and utilised.Design/methodology/approachA statistical analysis of university income from all sources in the UK and Australia.FindingsThe article produces a new time series for Australia and a comparable time series for the UK. It suggests some of the ways these data related to broader patterns of economic change, sketches the possibility of strategic influence, and outlines some of their limitations.Originality/valueThis is the first study to systematically create a time series on Australian university income across the twentieth century and present it alongside a comparable dataset for the UK.

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Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Remote Sensing ◽

10.3390/rs9050479 ◽

2017 ◽

Vol 9 (5) ◽

pp. 479 ◽

Cited By ~ 12

Author(s):

Shanshan Liu ◽

Xinliang Wei ◽

Dengqiu Li ◽

Dengsheng Lu

Keyword(s):

Time Series ◽

Time Series Data ◽

Forest Disturbance ◽

Subtropical Forest ◽

Zhejiang Province ◽

Series Data ◽

Forest Region ◽

Disturbance And Recovery

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Identification of forest disturbance and estimation of forest age in subtropical mountainous areas based on Landsat time series data

Earth Science Informatics ◽

10.1007/s12145-021-00728-w ◽

2021 ◽

Author(s):

Shibin Ma ◽

Zhongfa Zhou ◽

Yongrong Zhang ◽

Yulun An ◽

GuangBin Yang

Keyword(s):

Time Series ◽

Time Series Data ◽

Forest Disturbance ◽

Series Data ◽

Forest Age ◽

Mountainous Areas

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Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

The Scientific World JOURNAL ◽

10.1155/2014/256815 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Peng Li ◽

Na Zhao ◽

Donghua Zhou ◽

Min Cao ◽

Jingjie Li ◽

...

Keyword(s):

Time Series ◽

Transmission Line ◽

Time Series Data ◽

Power Transmission ◽

Time Series Prediction ◽

Series Data ◽

Power Transmission Line ◽

Alarm Systems ◽

History Of ◽

Overhead Power Transmission Line

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

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Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Remote Sensing ◽

10.3390/rs12182953 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2953 ◽

Cited By ~ 2

Author(s):

Eliakim Hamunyela ◽

Sabina Rosca ◽

Andrei Mirt ◽

Eric Engle ◽

Martin Herold ◽

...

Keyword(s):

Time Series ◽

Tropical Forest ◽

Satellite Data ◽

Time Series Data ◽

Forest Disturbance ◽

Google Earth ◽

Series Data ◽

Observation Data ◽

Large Area ◽

Change Monitoring

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.

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Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada

10.32920/14639841 ◽

2021 ◽

Author(s):

K. Wayne Forsythe ◽

Grant McCartney

Keyword(s):

Time Series ◽

Time Series Data ◽

Forest Disturbance ◽

Timber Harvest ◽

Series Data ◽

Cultural Significance ◽

Forest Disturbances ◽

Moisture Index ◽

Central Plateau ◽

Provincial Park

The Nagagamisis Central Plateau (located in Northern Ontario, Canada) is an area of distinct natural and cultural significance. The importance of this land was officially recognized in 1957 through the establishment of the Nagagamisis Provincial Park Reserve. The park has experienced significant expansion since its inception and is currently under development as one of Ontario Parks ‘Signature Sites’. Since the 1980s, timber harvest activity has led to widespread forest disturbance just outside of the park boundaries. This research is focused on the detection of stand level forest disturbances associated with timber harvest occurring near Nagagamisis Provincial Park. The image time-series data selected for this project were Landsat TM and ETM+; spanning a twenty-five year period from 1984 to 2009. The Tasselled Cap Transformation and Normalized Difference Moisture Index were derived for use in unsupervised image classification to determine the land cover for each image in the time-series. Image band differencing and raster arithmetic were performed to create maps illustrating the size and spatial distribution of stand level forest disturbances between image dates. A total area of 1649 km2 or 26.1% of the study area experienced stand level disturbance during the analysis period.

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How Gaps in Time-Series Data Affect Asteroseismic Interpretation

Frontiers in Astronomy and Space Sciences ◽

10.3389/fspas.2021.793930 ◽

2021 ◽

Vol 8 ◽

Author(s):

Paul A. Bradley

Keyword(s):

Time Series ◽

White Dwarf ◽

Time Series Data ◽

Fourier Transforms ◽

Series Data ◽

Frequency Content ◽

Dwarf Stars ◽

Advantages And Disadvantages ◽

White Dwarf Stars ◽

History Of

Most pulsating white dwarf stars pulsate with many periods, each of which is a probe of their interior, which has made asteroseismolgy of these stars an active field. However, disentangling the multiple periodicities requires long, uninterrupted strings of data. We briefly describe the history of multi-site observing campaigns that culminated in the development of the Whole Earth Telescope in the late 1980s that still functions today. Through examples from the May 1990 campaign on GD 358, we show how critical it is to eliminate periodic gaps in data to greatly reduce aliasing in Fourier Transforms normally used to analyze the frequency content of pulsating white dwarfs. We close with a brief description of space satellite-based data, along with the advantages and disadvantages of these data compared to ground-based data.

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