Mapping Cropping Practices Using MODIS Time Series: Harnessing the Data Explosion
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RESEARCH ARTICLE
Mapping Cropping Practices Using MODIS Time Series: Harnessing the Data Explosion Peter Tan & Leo Lymburner & Medhavy Thankappan & Adam Lewis
Received: 13 October 2010 / Accepted: 23 May 2011 / Published online: 25 June 2011 # Indian Society of Remote Sensing 2011
Abstract The MODIS (Moderate Resolution Imaging Spectroradiometer) 250m 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 data. 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 P. Tan (*) : L. Lymburner : M. Thankappan : A. Lewis National Earth Observation Group, Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia e-mail: [email protected]
(ISO) Land Cover classification (Jansen 2000) 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. Keywords Time series . MODIS . Remote sensing . EVI
Introduction Analysing Time Series Remotely Sensed Imagery Many current time series analysis methods used in remote sensing imagery analysis reconstruct a time series with one or a set of functions from a particular function class. Such methods often come with strong model assumptions and arbitrary parameters which must be manually specified. For example, autoregressive-moving average (ARMA) models as-
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sume targeted time series are stationary (Emanuel 1982; Hamilton 1994), i.e., the behaviour (estimated parameters) of the time series do not shift dramatically along the time line. In many cases, such assumptions do not hold. In order to apply analysis methods such as wavelet transform (Walden 2006) a
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