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

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Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. / Schmidt, Michael; Lucas, Richard; Bunting, Peter; Verbesselt, Jan; Armston, John.

Yn: Remote Sensing of Environment, Cyfrol 158, 01.03.2015, t. 156-168.

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Schmidt, Michael ; Lucas, Richard ; Bunting, Peter ; Verbesselt, Jan ; Armston, John. / Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Yn: Remote Sensing of Environment. 2015 ; Cyfrol 158. tt. 156-168.

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@article{7bced12646d04240878456303f1816f0,
title = "Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia",
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 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.",
keywords = "STARFM, BFAST, Landsat TM/ETM +, MODIS, forest change, clearing, time series, regrowth, data fusion",
author = "Michael Schmidt and Richard Lucas and Peter Bunting and Jan Verbesselt and John Armston",
note = "Schmidt, M., Lucas, R., Bunting, P., Verbesselt, J., Armston, J. (2015). Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158, 156-168",
year = "2015",
month = mar,
day = "1",
doi = "10.1016/j.rse.2014.11.015",
language = "English",
volume = "158",
pages = "156--168",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

AU - Schmidt, Michael

AU - Lucas, Richard

AU - Bunting, Peter

AU - Verbesselt, Jan

AU - Armston, John

N1 - Schmidt, M., Lucas, R., Bunting, P., Verbesselt, J., Armston, J. (2015). Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158, 156-168

PY - 2015/3/1

Y1 - 2015/3/1

N2 - 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.

AB - 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.

KW - STARFM

KW - BFAST

KW - Landsat TM/ETM +

KW - MODIS

KW - forest change

KW - clearing

KW - time series

KW - regrowth

KW - data fusion

UR - http://hdl.handle.net/2160/27180

U2 - 10.1016/j.rse.2014.11.015

DO - 10.1016/j.rse.2014.11.015

M3 - Article

VL - 158

SP - 156

EP - 168

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -

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