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The ARSET (Applied Remote Sensing Training) program from NASA organizes a  satellite remote sensing training that builds the skills to integrate NASA Earth Science data into an agency’s and organization's decision-making activities. The online training will take place on Monday 15 April and on Wednesday 17 April.  ARSETTrainings are open and free, like all NASA data. 

 Advanced Webinar: Investigating Time Series of Satellite Imagery

Organization: NASA Applied Remote Sensing Training Program (ARSET)    

Course Overview: Evaluation of satellite imagery for an area over time can be used to identify trends and changes. This type of time series analysis can be used to assess forest disturbance, land cover changes, vegetation health, and agriculture monitoring and expansion. NASA Earth observations can provide long-term records from Landsat, and frequent imagery from sensors including MODIS. This training will focus on two tools, AppEEARS from the LPDAAC and LandTrendr via Google Earth Engine (GEE). AppEEARS enables users to integrate point or polygon ground-based data with satellite imagery. The GEE implementation of LandTrendr enables users to analyze land cover dynamics, including short-term disturbances and long-term trends. Both sessions will feature a lecture, followed by time for hands-on exercises and questions.

Learning Objectives: By the end of this training, attendees will become familiar with time series analysis techniques:

  •   Understand how to evaluate time series of MODIS & Landsat data
  •   Learn how to integrate point data with satellite imagery using the AppEEARS web-based interface
  •   Learn the basics of the GEE implementation of LandTrendr including:
    • Generating annual land surface reflectance images
    • Plotting a time series of land surface reflectance
    • Creating a map of change detection, the magnitude of change, duration of a change event, and pre-change spectral values

Course Dates:  Monday, April 15 and Wednesday, April 17, 2019.

Times: 10:00-12:00 or 18:00-20:00 EDT (UTC-4)

Training URL:

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