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An Overview of Satellite Imagery. Originally presented as part of: NASA ARSET- AQ On-line Short Course Week 3 Presentation May 23, 2012. ARSET - AQ A pplied R emote SE nsing T raining – A ir Q uality A project of NASA Applied Sciences. Session 3 – Outline
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An Overview of Satellite Imagery Originally presented as part of: NASA ARSET- AQ On-line Short Course Week 3 Presentation May 23, 2012 ARSET - AQ AppliedRemoteSEnsingTraining –Air Quality A project of NASA Applied Sciences
Session 3 – Outline • What are true and false color images? • What can we learn from images? • A tour of useful image archives. • Assignment #3
RGB Images Red, Green and Blue correspond to the three color receptors in the human eye. These 3 colors are also the basis for all color display technologies from LCD sub-pixels to television color “guns”.
Remote Sensing of Radiation Earth-observing satellite remote sensing instruments typically make observations at many discrete wavelengths or wavelength bands. Terra MODIS 36 wavelength bands covering the wavelength range 405 nm (blue) to 14.385 μm (infrared)
250 M 500 M MODIS Reflected Solar Bands
RGB Images and Remote Sensing Instruments We can create an image by selecting any three bands and load them into the “Red” “Green” and “Blue” display channels. “True Color Image” To simulate what the human eye sees we load the red, green and blue satellite bands into the corresponding display channels.
True Color Image A MODIS “True Color Image” will use MODIS visible wavelength bands 1-4-3 0 = 36° R = 0.66 µm G = 0.55 µm B = 0.47 µm
What can we learn from true color imagery? 1 5 6 3 8 4 2 7 (Possible) Identification of land, ocean and atmosphere features
What can we learn from true color imagery? (Possible) Identification of land, ocean and atmosphere features
Dust over the Sahara 17 February 2008, Aqua Images courtesy of Yuval Ben-Ami
Plume’s area ~55,600 km2 = ~4.5 × area of Maryland 18 February 2008, Aqua
Geographic extent and transport of aerosols 19 February 2008, Aqua
Feature Identification is more reliable when a clear source can be seen in the image. Sahara dust Wildfire smoke Urban- Industrial Pollution? Smoke from Alaska wildfires Images courtesy of Phil Russell
The color of dust or smoke can tell us something about chemical properties.
Doing More with Satellite Imagery If we understand the physics of how particular wavelengths interact with objects in the world we can create images to emphasize what we want to see • In visible imagery water is dark because it absorbs most of the energy. • Clouds are white because most of the incoming energy is reflected • Pollution is hazy depending upon its absorptive properties
False Color Images “False Color Image” To enhance particular features we want to see in an image we load bands into the red, green and blue display channels which do not correspond to the visible red, green, and blue wavelengths. R = 1.6 µm G = 1.2 µm B = 2.1 µm
Volcanic Ash • R:0.645 um, G : 0.858um, B : 0.469 um (All channels equalized)
Volcanic Ash • R:0.645 um, G : 0.858um, B : 11.03 um (All channels equalized, B channel also flipped)
Using False Color Images toIdentify aerosol types Dust Both dust and smoke interact with the shorter wavelengths reflecting light back to the sensor. Smoke from Y. Kaufman
Spectral optical properties of aerosol Dust Dust particles interact with the longer infrared wavelengths but not the smaller smoke particles which remain invisible. Smoke from Y. Kaufman
Spectral optical properties of aerosol Dust • The distinction • of aerosol types • is made • possible by: • The wide spectral • range of the MODIS sensor. • Understanding • how light • interacts • with the particles, • gases and surfaces • it interacts with. Smoke from Y. Kaufman
Spectral Images vs Color Maps True Color Image False Color Image SWIR (RGB=1.64m, 1.24m, 2.13m) Visible (RGB=0.66m, 0.55m, 0.46m) NDVI=(0.86-0.66)/(0.86-0.66) AFRI=(0.86-2.1/2)/(0.86-2.1/2) Color Maps Values are assigned colors according to the scale below each image. Spectral information is used to detect chlorophyll. High values indicate more vegetation.
Landsat True and False Color Animation http://landsat.gsfc.nasa.gov/education/resources/L7_sanfran_onionskin.mov
Site #2 NASA’s Earth Observatoryhttp://earthobservatory.nasa.gov/ The site includes a searchable archive of “Image of the Day”
NASA’s Earth Observatory is intended to be an educational site for the general public with an • interest in Earth Science. • Feature articles explaining Earth Science issues and • phenomena • The site includes a searchable • archive of “Image of the Day”
A simple search using “fires” “california” returns 63 pages of results. • You can use the drop down menu to choose a section of the site • to search to reduce the number of results. • Site sections include: • Image of the day • Features • Natural Hazards • News • Blogs
You can browse the archive by broad topic categories
Natural Hazards Section You can browse within a topic by subtopic or by date.
http://earthobservatory.nasa.gov/Subscribe/ One very nice feature of this site is the option to subscribe to any of several e-mail news letters. For example, if you wanted to keep track of the most recent news on volcanic eruptions, you would subscribe to the Natural Hazards Daily
Sample Update e-mail from the Natural Hazard mailing list
GLIDER Software • GLIDER is a comprehensive tool for image analysis, classification and data mining. • Explore the various aspects of GLIDER • More importantly, it’s free! • http://miningsolutions.itsc.uah.edu/glider/
Imagery Links MODIS Rapid Response Site http://earthdata.nasa.gov/data/near-real-time-data/rapid-response NASA’s Visible Earth http://visibleearth.nasa.gov NASA’s Earth Observatory http://earthobservatory.nasa.gov NASA Earth Observations (NEO) http://neo.sci.gsfc.nasa.gov MODIS- Atmos (MODIS Atmosphere Product Reference Site) http://modis-atmos.gsfc.nasa.gov/IMAGES/index.html • Tutorial on constructing RBG images. http://www.zamg.ac.at/eumetrain/CAL_Modules/CALRGB/rgb1_2.htm • GLIDER Tool http://www.ssec.wisc.edu/hydra/