A robust approach for phenological change detection within satellite image time series

Jan Verbesselt, Martin Herold, Rob Hyndman, Achim Zeileis, Darius Culvenor

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000-2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).

Original languageEnglish
Title of host publication2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp)
Subtitle of host publicationProceedings
EditorsLorenzo Bruzzone, Francesca Bovolo
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages41-44
Number of pages4
ISBN (Print)9781457712036
DOIs
Publication statusPublished - 2011
EventInternational Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2011 - Trento, Italy
Duration: 12 Jul 201114 Jul 2011
Conference number: 6th

Workshop

WorkshopInternational Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2011
Abbreviated titleMulti-Temp 2011
CountryItaly
CityTrento
Period12/07/1114/07/11

Keywords

  • bfast
  • change detection
  • land surface phenology
  • MODIS
  • NDVI
  • phenology
  • time series

Cite this

Verbesselt, J., Herold, M., Hyndman, R., Zeileis, A., & Culvenor, D. (2011). A robust approach for phenological change detection within satellite image time series. In L. Bruzzone, & F. Bovolo (Eds.), 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp): Proceedings (pp. 41-44). [6005042] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Multi-Temp.2011.6005042
Verbesselt, Jan ; Herold, Martin ; Hyndman, Rob ; Zeileis, Achim ; Culvenor, Darius. / A robust approach for phenological change detection within satellite image time series. 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp): Proceedings. editor / Lorenzo Bruzzone ; Francesca Bovolo. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2011. pp. 41-44
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abstract = "The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000-2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).",
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Verbesselt, J, Herold, M, Hyndman, R, Zeileis, A & Culvenor, D 2011, A robust approach for phenological change detection within satellite image time series. in L Bruzzone & F Bovolo (eds), 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp): Proceedings., 6005042, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 41-44, International Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2011, Trento, Italy, 12/07/11. https://doi.org/10.1109/Multi-Temp.2011.6005042

A robust approach for phenological change detection within satellite image time series. / Verbesselt, Jan; Herold, Martin; Hyndman, Rob; Zeileis, Achim; Culvenor, Darius.

2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp): Proceedings. ed. / Lorenzo Bruzzone; Francesca Bovolo. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 41-44 6005042.

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

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T1 - A robust approach for phenological change detection within satellite image time series

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N2 - The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000-2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).

AB - The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000-2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).

KW - bfast

KW - change detection

KW - land surface phenology

KW - MODIS

KW - NDVI

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M3 - Conference Paper

SN - 9781457712036

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EP - 44

BT - 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp)

A2 - Bruzzone, Lorenzo

A2 - Bovolo, Francesca

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

ER -

Verbesselt J, Herold M, Hyndman R, Zeileis A, Culvenor D. A robust approach for phenological change detection within satellite image time series. In Bruzzone L, Bovolo F, editors, 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp): Proceedings. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2011. p. 41-44. 6005042 https://doi.org/10.1109/Multi-Temp.2011.6005042