Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

Michael Schmidt, Richard Lucas, Peter Bunting, Jan Verbesselt, John Armston

Research output: Contribution to journalArticlepeer-review

99 Citations (SciVal)
407 Downloads (Pure)


High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30 m spatial resolution data was generated by the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Landsat sensor observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data as input. The time series showed a close relationship over homogeneous forested and grassland sites, with r2 values of 0.99 between Landsat and the closest STARFM simulated data; and values of 0.84 and 0.94 between MODIS and STARFM. The time and magnitude of clearing and re-clearing events were estimated through a phenological breakpoint analysis, with 96.2% of the estimated breakpoints of the clearing event and 83.6% of the re-clearing event being within 40 days of the true clearing. The study highlights the benefits of using these moderate resolution data for quantifying and understanding land cover change in open forest environments.
Original languageEnglish
Pages (from-to)156-168
Number of pages13
JournalRemote Sensing of Environment
Early online date04 Dec 2014
Publication statusPublished - 01 Mar 2015


  • Landsat TM/ETM +
  • forest change
  • clearing
  • time series
  • regrowth
  • data fusion


Dive into the research topics of 'Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia'. Together they form a unique fingerprint.

Cite this