18 May 2012 Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna
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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 12×10  km 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.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)
Michael Schmidt, Thomas Udelhoven, Achim Röder, Tony K. Gill, "Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna," Journal of Applied Remote Sensing 6(1), 063512 (18 May 2012). https://doi.org/10.1117/1.JRS.6.063512 . Submission:
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