These exercises have been provided by instructors from various institutions, to help students and researchers to learn the usage of MODIS data subsets.
Note to users: Please contact the professors before using their exercises in your class. If you create new classroom exercises or scripts that utlize the ORNL DAAC MODIS subsetting tools consider contributing to the list: Contact Us.
MODIS based time series analysis using BFAST
- Jan Verbesselt
- Associate Professor
- Laboratory of GIS and Remote Sensing
- Wageningen University
- The Netherlands
- Change Detection and Monitoring
This exercise is an introduction to MODIS NDVI time series analysis in R and implementation of the BFAST library for the decomposition of the time series into trend, season, and remainder components. This type of analysis is useful for detecting and characterizing change within the time series.
You will learn how to access MODIS NDVI fixed site data in CSV format, visualize the time series, perform quality filtering, and compute BFAST metrics within the R environment. This exercise is based on data for the Gelderland Loobos Site in The Netherlands.
Analysis of Vegetation Index
- May Myklebust
- Course Instructor
- Biology Department
- Trent University
- Peterborough, ON
- Trent University Biology
Scientists have access to more information today than ever before. The information comes in the form of research publications but also as research quality data. Often, these resources are free. We can therefore study just about anything we are interested in. The problem is knowing where to look and how the data may be used. The purpose of this lab is to expose you to research quality remotely sensed data.
Remote Sensing in Hydrology
- Hongjie Xie
- Associate Professor
- Director of Remote Sensing and Geoinformatics Lab
- Department of Geological Sciences
- University of Texas at San Antonio
- UTSA LRSG
The purpose of this lab is to learn how to order, download, process, and analyze MODIS vegetation indices and compare it with in-situ soil moisture data.
Another exercise is included to learn how analyze MODIS LST products and to use them to examine the urban heat island phenomena for any one of the four cities: Houston, Dallas-Fort Worth, San Antonio, El Paso.
Quantitative Remote Sensing for Terrestrial Monitoring
- David Roy
- Senior Scientist, Professor of Geography
- Geographic Information Science Center of Excellence
- South Dakota State University
- Geospatial Sciences, SD State
This classroom exercise teaches quantitative remote sensing for terrestrial monitoring. You will learn how to (a) Extract the 16-day NDVI values from the M0D13A2 data, (b) Extract the 8-day MODIS Band 1 (red) and Band 2 (NIR) values from the MOD09A1 data and compute the NDVI from these values, and (c) Extract the 16-day MODIS Band 1 (red) and Band 2 (NIR) values from the MOD43B4 data and compute the NDVI from these values.
Remote Sensing Lab Exercise
- James T. Randerson
- Department of Earth System Science
- University of California, Irvine
- Randerson Lab at UCI
Learn to retrieve ASCII text EVI and NDVI time series for your site using MODIS subsetting tool from NASA's ORNL DAAC.
MODIS Subset Data Extraction and Visualization
- Alfredo Huete
- Plant Functional Biology and Climate Change
- University of Technology, Sydney
- UTS Climate Change Cluster
The goals of this exercise are to (a) Understand some basic concepts in time series analysis with remote sensing, (b) Derive seasonal profiles of some land cover types, and (c) Derive some phenology metrics for various land cover types.
Geospatial Programming in Python
- David R. Maidment
- Center for Research in Water Resources
- Department of Civil, Architectural and Environmental Engineering
- University of Texas, Austin
Learning objectives of this exercise are: (a) Understand the benefits of extending ArcGIS through programming, (b)Introduce Python programming basics, (c) Understand how to collect data using web services, and (d) Demonstrate how to build a Python script that uses ArcMap tools.