Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine

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Abstract

Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disaster
assessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.
Original languageEnglish
Article number46
JournalRemote Sensing
Volume12
Issue number1
Number of pages16
ISSN2072-4292
DOIs
Publication statusPublished - 2019

Keywords

  • Dual polarization SAR data
  • Change detection
  • Flood monitoring
  • Deforestation
  • Port activity
  • Uranium mining
  • Google Earth Engine
  • Python
  • Jupyter Notebook

Cite this

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title = "Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine",
abstract = "Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disasterassessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.",
keywords = "Dual polarization SAR data, Change detection, Flood monitoring, Deforestation, Port activity, Uranium mining, Google Earth Engine, Python, Jupyter Notebook",
author = "Canty, {Morton J.} and Nielsen, {Allan Aasbjerg} and Knut Conradsen and Henning Skriver",
year = "2019",
doi = "10.3390/rs12010046",
language = "English",
volume = "12",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "M D P I AG",
number = "1",

}

Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine. / Canty, Morton J.; Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning.

In: Remote Sensing, Vol. 12, No. 1, 46, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine

AU - Canty, Morton J.

AU - Nielsen, Allan Aasbjerg

AU - Conradsen, Knut

AU - Skriver, Henning

PY - 2019

Y1 - 2019

N2 - Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disasterassessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.

AB - Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disasterassessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.

KW - Dual polarization SAR data

KW - Change detection

KW - Flood monitoring

KW - Deforestation

KW - Port activity

KW - Uranium mining

KW - Google Earth Engine

KW - Python

KW - Jupyter Notebook

U2 - 10.3390/rs12010046

DO - 10.3390/rs12010046

M3 - Journal article

VL - 12

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

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ER -