Tech Briefs

The Time Series Product Tool (TSPT) is a MATLAB-based software application that computes and displays high-quality vegetation and environmental monitoring indices from high temporal revisit rate Mod erate resolution Imaging Spectroradiometer (MODIS) and other satellite sensors. The original purpose of the TSPT was to fuse MODIS Terra and Aqua products to improve the temporal interpolation and filtering of time series affected by clouds. TSPT provides single-timeframe and multi-temporal change images as time series plots at a selected location, or as temporally processed image videos. The labor involved with manually creating these types and quantities of products is considerable; however, by using the TSPT, this process becomes simplified, efficient, and largely automated. This software tool enables and/or aides in the rapid regional surveillance of crops, forests, and other vegetative surfaces.

With TSPT version 2.0, several improvements to the original software were incorporated and the overall efficiency of the software program was increased. Novel features include the ability to fuse data from multiple satellite sources into one time series, as well as a capability that allows users to temporally sub-sample higher density data sets. Improvements to the outlier removal capability and enhanced temporal filtering techniques, as well as expanded pre-processing and index calculation capabilities, have enhanced the overall functionality of the software, and make additional data processing easier. The new version of the TSPT also has expanded MODIS data product support, and has optimized algorithms that take advantage of the vector processing capabilities of MATLAB, enabling efficient processing of multi-year MODIS time series data sets over the entire conterminous United States.

Although a wide range of forest and agricultural crop diseases and pathogens have been identified, their spectral, temporal, and spatial signatures are not well described, especially at the MODIS scale. The enhanced TSPT version 2.0 has the capability to research and monitor phenologies of healthy and diseased forest and agricultural crops, and is suited for addressing regional and global questions regarding vegetation health status and productivity.

This work was done by Rodney McKellip of NASA Stennis Space Center; Joseph Spruce and James Smoot of Computer Sciences Corporation; Gerald Gasser of Lockheed Martin; Robert Ryan and Kara Holekamp of Innovative Imaging and Research Corp; and Kenton Ross of NASA. SSC-00381

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