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

REMOTE SENSING OF ENVIRONMENT

卷:158

Article

Schmidt, Michael^1,2  Lucas, Richard³  Bunting, Peter⁴  Verbesselt, Jan⁵  Armston, John^1,2 

[1] Remote Sensing Ctr Environm & Resource Sci, Dept Sci Informat Technol Innovat & Arts, Brisbane, Qld 4001, Australia

[2] Univ Queensland, Joint Remote Sensing Res Program, Sch Geog Planning & Environm Management, Brisbane, Qld 4072, Australia

[3] Univ New S Wales, Sch Biol Earth & Environm Sci BEES, Kensington, NSW 2052, Australia

[4] Univ Wales, Inst Geog & Earth Sci, Aberystwyth SY23 3DB, Ceredigion, Wales

[5] Wageningen Univ, Lab Geoinformat Sci & Remote Sensing, NL-6708 PB Wageningen, Netherlands

关键词: STARFM;    BEAST;    Landsat TM/ETM;    MODIS;    Forest change;    Clearing;    Time series;    Regrowth;    Data fusion;   

DOI  :  10.1016/j.rse.2014.11.015

来源: Elsevier

【 摘 要 】

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 r(2) values of 0.99 between Landsat and the closest STARFM simulated data; and values of 0.84 and 0.94 between MODE 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. (C) 2014 Elsevier Inc All rights reserved.

【 授权许可】

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