Target Audience
·
high school teachers and students
·
pre-service high school teachers
·
community college instructors and students
·
undergraduate instructors and students
Relevant
Subjects
·
Earth systems science
·
Earth science
·
remote sensing
·
biology
·
space science
·
geography
·
oceanography
·
chemistry
·
physics
·
geomorphology
·
atmospheric science
·
environmental science
·
technology
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Remote Sensing
Tutorial
This part of the
tutorial consists of a downloadable, PowerPoint
presentation of 50 slides encompassing many basic definitions of
remote sensing with colorful examples, as well as detailed information about
electromagnetic (EM) radiation and how EM theory helps explain how we do remote
sensing. Most of the slides are
accompanied by detailed notes and references to pertinent on-line
resources. These slides could form
the basis of one or two class lectures in remote sensing. Here is an example of one of the slides
in the tutorial. You can also view
the notes.
What Is Remote
Sensing?
In its broadest definition,
remote sensing means collecting information about an object without being in
direct physical contact with it: learning without touching. The most
familiar kind of remote sensing is the use of our eyes to detect light. We also
use remote sensing when we hear, and when we feel heat that radiates from a warm
object.
Bats sense their
environments by emitting sound waves (shown above in black). The sound waves hit
objects and are reflected back (shown above in white) to the bats. The time it
takes for the reflected sound waves to get back to the bats indicates to them
how far they are from insect prey, trees, and objects around them. We use
remote sensing when we hear in everyday situations. When a car honks its horn,
we immediately focus all our attention on it to learn whether or not we are in
danger. We also use sound waves to make medical images (ultrasound) and to look
for submarines (sonar) from a ship.
Animals use sound waves in sophisticated ways. Bats use them to find
insects and to find their way through their surroundings. When we sit near a fire, we sense its
radiant energy (heat). This is a form of remote sensing! Other animals can sense
heat even better than we do. Rattlesnakes use special organs on their heads to
detect heat radiation from small prey animals such as mice.
Understanding the Advanced Very High Resolution
Radiometer (AVHRR) Images
The satellites used for this
image are the NOAA Polar Orbiters (also known as TIROS satellites). NOAA, the
National Oceanic and Atmospheric Administration, has a very nice description of the Polar Orbiters. The Canada Centre for
Remote Sensing has a very nice web page with details on all the NOAA polar orbiter satellites, from NOAA-1
on to future ones. Briefly, the Advanced Very High Resolution Radiometer
(AVHRR) sensor on the NOAA Polar Orbiters scans and transmits back to
earth a narrow strip across its ground track 6 times each second. Each strip is
about 1 km wide and 1462 km long (909 miles). These scans are received as long
as the satellite is in view of the ground station and all the strips together
form an image of the earth below. The along track size of the image depends on
how long the satellite is visible from the ground station. This may be a very
short time if the satellite appears to just skim the horizon, or up to about 15
or 16 minutes if it passes directly overhead. The AVHRR image data has 5
channels or wavelength bands, ranging from the visible to the far infrared. Each
band shows somewhat different features as discussed in the next section. The Satellite Imagery FAQ gives a lot of details about this
topic and pointers to other websites.
Color Composite Images: Channels 2, 1, and 3 Displayed
as Red, Green, and Blue
This color
combination was selected to try to match the commonly used Landsat infrared color images (which typically uses Landsat
bands 4, 3, and 1 as red, green, and blue (wavelengths not the same as AVHRR)).
The match is not perfect but the images are similar. Vegetation areas show as
red or some variations such as orange or purple. Muddy water or shallow water
shows as green. Clear water shows as black if cold or blue if warm. Clouds
appear fairly white if warm and yellow if cold. This usually gives a good
indication of the relative cloud heights, low clouds are warm and high clouds
cold (and yellow). An unfortunate side effect of this color combination is that
cold snow appears yellow. Perhaps the best way to learn what the colors in the
image mean is to check what time of year the image was taken and look at a
detailed road map or other atlas. Urban areas often look pale blue or blue-gray.
However barren regions also sometimes show a similar color. Agricultural areas
show as a bright red or somewhat orange. Barren areas with some vegetation may
appear somewhat purple from the mixture of red and blue. Mountainous areas such
as in Colorado show altitude related color bands, from the low barren
areas that appear blue, to somehwat more vegetated darker areas, to a brighter
red where more vegetation occurs to a darker red typical of pine trees, to a
dark yellow-gray of areas above the timberline (and perhaps some brighter yellow
areas of snow). Although clear views are the most sought after, it is not always
possible to avoid some clouds or variations in atmospheric clarity. High thin
cirrus cloud areas may appear as areas that are a bit more yellow than nearby
areas, perhaps even faint shadows may be seen. This makes interpretation of any
single image harder. Also the time of day makes a difference in the appearance
of an image. NOAA-14 daytime views usually have a high sun angle, especially in
summer, so relief is not well seen. NOAA-12 images sometimes show very nice
shadowing making mountainous areas very apparent. Unfortunately NOAA-12 passes
occur in darkness in the winter, useful for water temperature and fires but not
for landform images in general.
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Classroom Activities
This part of the tutorial consists
of two classroom activities which do not require a student to have computer
access.
This lesson consists of a “paper-and-pencil”
activity designed to get students thinking about Earth’s temperature. They are given a black and white map of
Earth and asked to color the map according to what they believe is the
appropriate surface temperature distribution around the Earth. This activity is accompanied by a very
short PowerPoint
presentation about Global Earth Temperature for use by the teacher.
Using Satellite Images From MODIS to Estimate the Size and Speed of
Hurricane Isabel
This is a series of PowerPoint
slides which the teacher can use as a lecture or can print and give to the
students to do as an exercise. It
asks students to use two spectacular satellite images from MODIS of Hurricane
Isabel and their skill in estimation of known quantities (or those easily
looked-up) to determine the approximate area and forward speed of this
destructive storm.
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Computer-Based Activities
The computer laboratories (in MS
Word and PDF) in this part of the tutorial have step-by-step instructions for
how to analyze and interpret satellite images of (a) global sea surface
temperature and (b) land vegetation indices. The student labs are accompanied by an
instructor’s guide containing answers to all of the questions.
Sea Surface Temperature and
Oceanography From Space
In this lab,
your students will have to opportunity to explore global oceanographic sea
surface temperature (SST) using a research-quality, satellite derived data set
and an easy-to-use, free image analysis software program called ImageJ.
The computer lab itself contains step-by-step instructions
for using ImageJ to explore the SST data set. Your students will be able to conduct
quantitative investigations of the:
- Global geographic
distribution of sea surface temperature
- Seasonal cycle of SST
- El
Nino and how it effects the SST
They will be empowered to perform simple image analysis,
create image montages and animations, compute simple statistical analysis, and
answer questions about the data as they proceed through the lab.
An instructor’s guide to the lab is also provided which
contains all of the answers.
Download and print the lab and the instructor’s guide
first:
Download lab in MS Word (link)
Download instructor’s guide in MS Word
(link)
Download lab in PDF (link)
Download instructor’s guide in PDF
(link)
Software for Sea Surface Temperature
Data Activity
ImageJ is a public domain
Java image processing program inspired by NIH Image
for the Macintosh. It runs on any computer with a Java 1.1 or later
virtual machine, either as an online applet or as a downloadable
application. The author, Wayne
Rasband (wayne@codon.nih.gov), is at the
Research Services Branch, National Institute of Mental Health,
Bethesda, Maryland,
USA. The best source of information about
ImageJ can be found at the ImageJ homepage (http://rsb.info.nih.gov/ij/) and
by subscribing to the ImageJ mailing list (details on the home page). The ImageJ
Manual is meant to be an introduction to ImageJ for light microscopy – a
small part of ImageJ’s repertoire.
Download ImageJ (link)
Monthly Averaged Sea
Surface Temperature Data
It is recommended that you download this small PowerPoint
file which has a very brief introduction to the satellite sensor, the
Advanced Very High Resolution Radiometer (AVHRR), from which the Sea Surface
Temperature (SST) images are derived.
(need info about zipping the data, and how big once unzipped).
The data
provided for the SST exercises is a wonderful time-series of satellite-derived
SST for the global oceans. Hence,
we call it a “global” data set.
Each file, or image, represents a monthly average of all of the SST data
collected during that period. The
data begin with an image in December 1981 and extend through April 2004. The images are organized into annual
folders, named by the corresponding year.
There is one folder for each year of data from 1981/82 through 2004:
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Computer-Based
Activities
Using Land Vegetation Data (Normalized Difference Vegetation Index) to
Investigate Landscape and Greenness
In this lab, your students will have to opportunity to
explore global and regional vegetation “greenness” data using a
research-quality, satellite derived Normalized Difference Vegetation Index
(NDVI) data set from the AVHRR with an easy-to-use, free image analysis software
program from NASA called NASA Image2000.
The computer lab itself contains step-by-step instructions
for using NASA Image2000 to explore the AVHRR-derived NDVI data set. Your students will be able to conduct
quantitative investigations of the:
- Computation of NDVI
from AVHRR Channels 1 and 2 radiances
- Different signatures
of clouds, water and land in NDVI images
- Seasonal “greening”
cycle of NDVI in the Northern and Southern Hemispheres
- Interannual
variability (i.e., El Nino) and its effect on greenness
They will be empowered to perform simple image analysis,
create image montages and animations, compute simple statistical analysis, and
answer questions about the data as they proceed through the lab.
An instructor’s guide to the lab is also provided which
contains all of the answers.
Download and print the lab and the instructor’s guide
first:
Download
lab in MS Word (link)
Download
instructor’s guide in MS Word (link)
Download lab in PDF
(link)
Download instructor’s guide in
PDF (link)
Software For Land
Vegetation Greenness Data Activity
The purpose of NASA Image2000 is to provide a
host-independent image processing system for students and educators using
tutorials developed by SEE and the Center for Image
Processing in Education (CIPE). The core plug-in
architecture allows the system to be expanded to accommodate other segments of
the imaging community. NASA Image2000 is based on Sun's Java Advanced
Imaging (JAI) and
includes tools for science which are accessed through a standard menu interface
with toolbars that are customizable through XML definitions.
Download NASA Image2000
(link)
Download
NASA Image2000 documentation
Insert project title
Insert project title
Insert project title
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Last revised: 25
October 2004
Data for Sea
Surface Temperature Activity