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

Published Date

1 March 2015, Vol.158:156–168, doi:10.1016/j.rse.2014.11.015

Title 

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

• Author 

• Michael Schmidt ^a,b,
• Richard Lucas ^c
• Peter Bunting ^d
• Jan Verbesselt ^e
• John Armston ^a,b

• aRemote Sensing Centre, Environment and Resource Sciences, Department of Science, Information Technology, Innovation and the Arts, GPO Box 2454, Brisbane, Queensland 4001, Australia
• bJoint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, The University of Queensland, Campbell Rd, Brisbane, Queensland 4072, Australia
• cSchool of Biological, Earth and Environmental Sciences (BEES), the University of New South Wales (UNSW), High Street, Kensington, NSW 2052, Australia
• dInstitute of Geography and Earth Sciences, the University of Wales, Aberystwyth, Ceredigion SY23 3DB, United Kingdom
• eLaboratory of Geo-information Science and Remote Sensing, Wangeningen University, Droevendaalsesteeg 3, 6708PB Wageningen, Netherlands

Received 7 August 2012. Revised 31 October 2014. Accepted 7 November 2014. Available online 4 December 2014.

Highlights

• •
  Establishing stable vegetation reference sites with repeated LiDAR and aerial photography
• •
  Forest change mapping for clearing and re-clearing sites with a STARFM generated 12 year time series
• •
  Estimation of forest regrowth with a time series breakpoint analysis using BFAST

Abstract

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 r²values 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.

Keywords

• STARFM
• BFAST
• Landsat TM/ETM +
• MODIS
• Forest change
• Clearing
• Time series
• Regrowth
• Data fusion

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• ⁎ 
  Corresponding author.

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