Research Papers

Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna

[+] Author Affiliations
Michael Schmidt

Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, Environment and Resource Sciences, GPO Box 2454, Brisbane 4001, Australia

Thomas Udelhoven

University of Trier, Remote Sensing Department, Behringstraße 21, 54286 Trier, Germany

Tony Gill

Office of Environment and Heritage, Department of Premier and Cabinet, 209 Cobra Street, Dubbo NSW 2830, Australia

Achim Röder

University of Trier, Remote Sensing Department, Behringstraße 21, 54286 Trier, Germany

J. Appl. Remote Sens. 6(1), 063512 (May 18, 2012). doi:10.1117/1.JRS.6.063512
History: Received September 12, 2011; Revised February 20, 2012; Accepted February 29, 2012
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Abstract.  The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12×10km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product (r2=0.92) from the Queensland Department of Environment and Resource Management.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Topics

MODIS

Citation

Michael Schmidt ; Thomas Udelhoven ; Tony Gill and Achim Röder
"Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna", J. Appl. Remote Sens. 6(1), 063512 (May 18, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063512


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