Surface Temperature from Landsat Data: a New Lab Using the Semi-Automatic Classification Plugin

In a previous post I have illustrated how to estimate land surface temperature using Landsat images and the Semi-Automatic Classification Plugin.
I was very pleased when Katie Fankhauser, a graduate student at Portland State University, informed me that she was preparing a lab, inspired by that tutorial, about how to determine ground surface temperature using satellite imagery and my plugin.
The document that she prepared provides background information about remote sensing and Landsat imagery (such as conversion of Landsat images to TOA reflectance and brightness temperature), and describes all the required phases about:
- download of software and data;
- data processing and supervised classification of land cover;
- calculation of an emissivity raster and estimation surface temperature.
Temperature calculated for a Landsat image of Portland (data available from the U.S. Geological Survey)

The image used in the lab is a Landsat image of Portland (OR, USA). The processing of data is described step by step, from the ROI creation to the raster calculation, which is ideal also for students that have little experience with remote sensing.
The lab document (pdf file) is freely available at the following link.

This lab was prepared by Katie Fankhauser, and Evan Thomas who is Assistant Professor of the course Mechanical Engineering Measurements at the Portland State University, Sustainable Water, Energy and Environmental Technologies Laboratory. Very kindly, they credited also me as an author of this lab and allowed me to share the document.
Katie is currently working for a health campaign that aims to reduce the amount of wood fuel use consumed by traditional stone fires, and she is involved in ground truthing satellite-derived land surface temperature to study the rates of deforestation in Rwanda.
Evan Thomas holds a Ph.D. in Aerospace Engineering Sciences, and in particular he is the Director of the Sweet (Sustainable Water, Energy and Environmental Technologies) Laboratory, a very interesting and worthy project (
"At Portland State, the SweetLab designs and tests sustainable life support technologies for spacecraft and developing countries. The SweetLab's current primary focus is developing and implementing remotely accessible instrumented monitoring technologies designed to improve the collection of effectiveness evidence in global health programs, including high efficiency cookstoves, water pumps, household water filters, sanitation systems, pedestrian footbridges and other developing world appropriate technologies. The SweetLab has projects in India, Nepal, Indonesia, the Philippines, Rwanda, Kenya, Uganda, Haiti and other countries with partners including the Gates Foundation, USAID, Mercy Corps, the Lemelson Foundation, the Global Alliance for Clean Cookstoves, and DelAgua. The SweetLab also has on-going work with the NASA-Johnson Space Center on microgravity fluid management systems".

I am really glad and honored that my work can be useful for courses about environmental sustainability like this. Also, I hope that the upcoming new version of the plugin will allow for improved environmental analyses.
I would like to thank very much Katie and Evan and I hope that there will be other opportunities of cooperation in the future.

If one is interested in sharing the work done using the Semi-Automatic Classification Plugin, please contact me at the Facebook group or the Google+ Community.