February 10, 2016 - March 02, 2016

In this advanced webinar, participants will learn how to acquire, use, and derive NDVI imagery from Landsat and MODIS. Weekly webinars will include lectures, hands-on demonstrations of exercises, and written instructions on how to conduct the exercises. The exercises will use QGIS, a cross-platform open source GIS application. Each session will guide participants through the exercises, however to achieve the course learning objectives, participants should expect to spend additional time outside the webinars. There will be homework to complete after each exercise, which is required to be eligible for a completion certificate.


Learn how to create NDVI imagery, time series, and anomaly maps using NASA data including:

  • A basic understanding of NDVI
  • Acquiring Landsat and MODIS imagery
  • Creating NDVI images from Landsat
  • Using MODIS NDVI images to derive time series and NDVI anomaly maps

Local, regional, state, federal, and international organizations interested in assessing vegetation condition using satellite imagery. Professional organizations in the public and private sectors engaged in environmental management and monitoring will be given preference over organizations focused primarily on research.

Course Format
  • Four, one-hour sessions
  • Advanced Webinar
  • Each week features a presentation, exercises and a homework assignment
Session One: NDVI & QGIS
Session Two: Deriving NDVI from Landsat

Acquiring a Landsat image and deriving NDVI from Landsat using QGIS. View the Recording



Session Three: MODIS NDVI Time Series

An overview of MODIS NDVI, a demonstration of the MODIS/NDVI Time Series Database from the Global Agriculture Monitoring (GLAM) Project, acquiring MODIS NDVI images, and how to create a time series from MODIS NDVI. View the Recording



Session Four: MODIS NDVI Anomalies

An overview of MODIS NDVI anomaly mapping, a demonstration of the GIMMS MODIS Agricultural Monitoring System, and how to create a MODIS NDVI anomaly map. View the Recording





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