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Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND).

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Title: Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND).
Authors: Burrell, Arden L.1,2 arden.burrell@unsw.edu.au, Evans, Jason P.1,2, Liu, Yi1,2,3
Source: Remote Sensing of Environment. Aug2017, Vol. 197, p43-57. 15p.
Subjects: Arid regions ecology, Land degradation, Time series analysis, Food production, Meteorological precipitation
Abstract: Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND).
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing+of+Environment%22">Remote Sensing of Environment</searchLink>. Aug2017, Vol. 197, p43-57. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Arid+regions+ecology%22">Arid regions ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Land+degradation%22">Land degradation</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Food+production%22">Food production</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+precipitation%22">Meteorological precipitation</searchLink>
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  Data: Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.rse.2017.05.018
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 43
    Subjects:
      – SubjectFull: Arid regions ecology
        Type: general
      – SubjectFull: Land degradation
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Food production
        Type: general
      – SubjectFull: Meteorological precipitation
        Type: general
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      – TitleFull: Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND).
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              M: 08
              Text: Aug2017
              Type: published
              Y: 2017
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              Value: 197
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