Applying object-based segmentation in the temporal domain to characterise snow seasonality

Jeffery A. Thompson, Brian G. Lees

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In the context of a changing climate it is important to be able to monitor and map descriptors of snow seasonality. Because of its relatively low elevation range, Australia's alpine bioregion is a marginal area for seasonal snow-cover with high inter-annual variability. It has been predicted that snow-cover will become increasingly ephemeral within the alpine bioregion as warming continues. To assist the monitoring of snow seasonality and ephemeral snow-cover, a remote sensing method is proposed. The method adapted principles of object-based image analysis that have traditionally be used in the spatial domain and applied them in the temporal domain. The method allows for a more comprehensive characterisation of snow seasonality relative to other methods. Using high-temporal resolution (daily) MODIS image time-series, remotely sensed descriptors were derived and validated using in situ observations. Overall, moderate to strong relationships were observed between the remotely sensed descriptors of the persistent snow-covered period (start r=. 0.70, p<. 0.001; end r=. 0.88, p<. 0.001 and duration r=. 0.88, p<. 0.001) and their in situ counterparts. Although only weak correspondence (. r=. 0.39, p<. 0.05) was observed for the number of ephemeral events detected using remote sensing, this was thought to be related to differences in the sampling frequency of the in situ observations relative to the remotely sense observations. For 2009, the mapped results for the number of snow-cover events suggested that snow-cover between 1400 and 1799. m was characterised by a high numbers of ephemeral events.

Original languageEnglish (US)
Pages (from-to)98-110
Number of pages13
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Aug 25 2014
Externally publishedYes

Bibliographical note

Funding Information:
The authors wish to acknowledge Jason Venables of Snowy Hydro LTD for providing the in situ snow-depth data used for validation. Special thanks are due to the anonymous reviewers for their suggested improvements that greatly improved the manuscript. Parts of the study were conducted during the first author’s PhD candidature, which was made possible by an Australian Postgraduate Award (APA) scholarship granted through the University of New South Wales, Canberra campus. Financial support for writing this manuscript was provided to the first author in the form of a Publication Fellowship awarded by UNSW Canberra.

Publisher Copyright:
© 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Copyright 2017 Elsevier B.V., All rights reserved.


  • Alpine
  • Australia
  • Object-based image analysis
  • Seasonality
  • Snow cover
  • Time-series

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