7.1Intro to Remote Sensing GeoTed Ajohnson V4

Review for Introduction to
Remote Sensing:
Science Concepts and Technology
Ann Johnson
Associate Director
[email protected]
Funded by National Science Foundation Advanced
Technological Education program [DUE #1304591.
Author’s opinions are not necessarily shared by NSF
“Empowering Colleges:
Expanding the Geospatial
What is Remote Sensing and how is it used?
Passive and Active Remote Sensing
Electromagnetic Spectrum and sensor wavelength
and their “band numbers”
Resolutions – Temporal, Spatial, Spectral and
Composite images: Pixels, Brightness and Digital
Pixels and its Remote Sensing Signature graphic
Finding and using data – Landsat focused
Lidar – what is it and how can it be used
Resources to learn more
USGS Definition
Acquiring information about a natural
feature or phenomenon, such as the
Earth’s surface, without actually being in
contact with it.
Sensor can be ground
based, aerial or satellite.
Not just a pretty picture!
How it can be used!
• Land Use Change
• Climate
• Disasters
▫ Floods, fires, volcanoes, earthquakes
• Forestry
• Agriculture
• Many more!
Factors to consider when you use remote
sensing data to understand or solve a
geospatial problem
• Scale – or Resolution
Where is the study location?
How large is the study are?
What is the “size” of features under study?
Is this a one time event or over multiple times over
days, months or years?
• Access to needed resources:
▫ Data and its cost?
▫ Hardware and software and skills to use them
• Why is study important? Important for “realworld use by industry or government - ROI
• Use “sensors” to detect and acquire the
“information” about features
The human eye as
a sensor and brain
as processor!
Two Types of Remote Sensing
• Active – Energy source is “provided”
▫ Lidar – Light Detection and Ranging using
pulsed laser beam from one wavelength)
▫ SAR – Synthetic Aperture Radar – pulses
of radio wavelengths
• Passive – Sun as the “energy source”
▫ Landsat
▫ Aster
Two Types of Remote Sensing
• Active – Energy source is “provided”
▫ Lidar – Light Detection and Ranging using
pulsed laser beam (of varying wavelengths)
▫ SAR – Synthetic Aperture Radar – pulses
of radio wavelengths
• Passive – Sun as the “energy source”
▫ Landsat
▫ Aster
What about our eyes –
Active or Passive?
Graphic From: Natural Resources Canada
Fundamentals of Remote Sensing Tutorial
• energy source,
• sensor(s),
• target,
• collection method,
• processing method and
• a distribution method
Electromagnetic Spectrum
• NASA Movie
• Can download a NASA book on the Tour of
the Electromagnetic Spectrum
One Wavelength
• Spectral –wavelengths of spectrum
collected by sensors
• Spatial – size of area on the ground covered
by one pixel (grid size) which can
affect size of image footprint
• Temporal – how often data (image) is
acquired for a location
• Radiometric – the sensitivity of sensor to
collect very slight differences in emitted or
reflected energy
Spectral Resolutions
Landsat Sensors
Collect data in
or “Bands”
of Electromagnetic
Our Eyes
Landsat 7
Band 1: 0.45 - 0.52 m (Blue)
Band 2: 0.52 - 0.60 m (Green)
Band 3: 0.63 - 0.69 m (Red)
Band 4: 0.76 - 0.90 m (Near infrared)
Band 5: 1.55 - 1.75 m (Mid-Infrared)
Band 6: 10.4 - 12.5 m (Thermal infrared)
Band 7: 2.08 - 2.35 m (Mid-infrared)
Spectral Resolutions
• SAR; radar
• Lidar; 600-1000 nm (some visible and some
• Multispectral: 450-2300 nm (some visible and some
Spatial Resolution Comparison – Scale
• High spatial resolution:
▫ Meter to sub meter pixels
▫ Small objects can be identified
▫ Small area for each image footprint
• Moderate spatial resolution
▫ Generally 30 meter pixels (Landsat)
▫ Object identification generally greater than 30 meters
▫ Moderate area image footprint
• Low spatial resolution
▫ 1 KM or larger pixels (MODIS)
▫ Objects smaller than 1 KM not observable
▫ Very large footprint
Look at Examples of Different types of
Imagery and compare their “footprints” –
logon to link below:
Temporal Resolution
• How often data is collected of the same
▫ Only once
▫ Daily – or multiple times a day
▫ “Frequently” – every so many days
• Landsat missions
▫ Once every 16 days – but . . . .
 Must be clear (or have a percent cloud
 Must be “important” (U.S. and outside U.S.)
Landsat Image – Orbits (Path
and Rows)
View Orbits video
Why focus on Landsat Data?
Tools and other
Atmosphere “blocks” some wavelengths:
sensors collect wavelength data in specific
regions (bands or channels) of the spectrum
Gray shading: Wavelength Regions with good transmission
What Does “data” look like? Landsat 7 Spectral
Bands and “gray scale” values of each band data set
Landsat 7 - Band data
comes in as rasters with
grayscale values 0 to 255
Landsat 8 – more than 4,000
scaled to 55,000 gray values
Radiometric Resolution
Ability of a Sensor to
discriminate very small
differences in reflected or
emitted energy
Pixel Brightness – White to
Black in shades of Gray for one
Digital Number: the numeric
values of its Brightness
Landsat 5 and 7 are 8 bit for
256 gray levels (0 to 255)
Landsat 8 is 12 Bit for 4,096
gray levels (scaled to 55,000)
Creating Visualizations:
Brightness values (DN) from three Bands
are combined and colored on a computer
monitor by designating which of the 3
bands will be coded as Red, Blue or Green
Landsat 7
Natural or True Color
Bands 3, 2, 1
False Color
Band 5, 4, 3
Pseudo Color
Bands 7, 5, 3
Selecting three different bands as Red, Green or Blue
creates different images of the same location
Note: Band
numbers for
Landsat 5 and 7
are different than
for Landsat 8
Resource for Viewing Natural
and False Color Composites on
USGS Website
• http://landsat.usgs.gov/LDCM_Image_E
• Go to this site and use the swipe to see the
difference using different bands for
images from four regions of the U.S.
Change Matters Website
• See
Identifying and Classifying
• Visual investigate using composites
• Using “band algebra” with data from bands
▫ Normalized Difference Vegetation Index
(NDVI) uses Near Infra Red and Red bands
• Classification using spectral data from
multiple bands for one pixel creating a
“spectral signature”
Spectral Signatures From
Different Surfaces in an Image
NDVI –Image Analysis and
“Greeness” Using NIR and Red Bands
Land Cover Change – and
“Greeness” - NDVI
Classification Using Software
• Unsupervised Classification
▫ User tells Software how many “classes” to
group the image data into and software
“gathers like values” into “classes” with
similar spectral values
▫ User then labels the classes into land use types
and may combine classes
Unsupervised Classification
Natural Color Composite of San
Fernando Valley, CA
Data clustered by software and
colored to match Land Use types
(i.e. blue = water, green =
vegetation, etc.)
• Supervised Classification
▫ User identifies pixels that are different
types of feature (soil, urban, vegetation,
etc) and creates a file with spectral
information that can be used by
▫ Software uses spectral value file of the
different features and classifies pixels
based on the specified land cover types.
So many satellites! Resources:
• Satellite Viewer
• http://science.nasa.gov/iSat/?group=visu
• EarthNow! Landsat Image Viewer
▫ Real time view as data is collected showing
current path of satellite
▫ http://earthnow.usgs.gov/earthnow_app.h
Finding Data:
Go to GloVIS and Try Path 41 and Row 36
Lidar – Active Remote Sensing
NOAA Lidar Tutorial:
Thank You!
Much of the material for this Presentation was
developed by iGETT-Remote Sensing grant from
the National Science Foundation (DUE 1205069)
More Exercises:
Concept Modules on YouTube Channel at
iGETT Remote Sensing Education
Ann Johnson
[email protected]
[email protected]

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