Authors / CoAuthors
Tan, P. | Lymburner, L. | Thankappan, M. | Lewis, A.
Abstract
The MODIS (or Moderate Resolution Imaging Spectroradiometer) 250 m EVI dataset provides a valuable ongoing means of characterising and monitoring changes in land use and resource condition. However the multiple factors that influence a time series of greenness data make the data difficult to analyse and interpret. Without prior knowledge, underlying models for time series in a given remote sensing image are often heterogeneous. So while conventional time series analysis methods such as wavelet transform and Fourier analysis may work well for part of the image, these models are either invalid or must be substantially re-parameterised for other parts of the image. To overcome these challenges we propose a new approach to distil information from earth observation time series. The characteristics of a remote sensing time series are represented by a set of statistics (which we call coefficients) selected to correspond to the dynamics of a natural system. To ensure the coefficients are robust and generic, statistics are calculated independently by applying statistical models with less complexity on shorter segments within the time series. An International Standards Organization (ISO) Land Cover classification was generated for cropping regions in the Gwydir and Namoi catchments, in Australia. Areas identified in the classification as irrigated and rain fed cropping were analysed using a tailored time series analysis tool. The crop analysis tool identifies time series features such as the number and duration of fallow periods, crop timing, presence/absence of a crop during a year for a specific growing season. This information is combined with paddock boundaries derived from Landsat imagery to provide detailed year-by-year insight into cropping practices in the Gwydir and Namoi catchments.
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nonGeographicDataset
eCat Id
71145
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Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
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Keywords
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- External PublicationScientific Journal Paper
- ( Theme )
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- model
- ( Theme )
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- remote sensing
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
Publication Date
2010-01-01T00:00:00
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geoscientificInformation
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