1. Ann Johnson Associate Director ann@baremt.com
Review for Introduction to Remote Sensing: Science Concepts and Technology
“Empowering Colleges:
Expanding the Geospatial
Workforce”
Funded by National Science Foundation Advanced Technological Education program [DUE # Author’s opinions are not necessarily shared by NSF

2. 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
Radiometric
Composite images: Pixels, Brightness and Digital
Numbers
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
But How can that be done and what resources existed to help?

3. 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.

4. Not just a pretty picture! How it can be used!
Land Use Change
Climate
Disasters
Floods, fires, volcanoes, earthquakes
Forestry
Agriculture
Many more!

5. 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 “real- world use by industry or government - ROI

6. Use “sensors” to detect and acquire the “information” about features
The human eye as
a sensor and brain
as processor!

7. Two Types of Remote Sensing Sensors
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
MODIS
Aster

8. Two Types of Remote Sensing Sensors
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
MODIS
Aster
What about our eyes – Active or Passive?

9. Graphic From: Natural Resources Canada Fundamentals of Remote Sensing Tutorial
Need:
energy source,
sensor(s),
target,
collection method,
processing method and
a distribution method

10. Electromagnetic Spectrum
NASA Movie
Can download a NASA book on the Tour of the Electromagnetic Spectrum

11. One Wavelength
crest
crest

12. “Resolution” 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

13. Spectral Resolutions
Landsat Sensors Collect data in specific Wavelengths or “Bands” of Electromagnetic Spectrum 3 2 1
In fact, the Landsat mission has been around since The sensors (located on the satellites platforms) on different missions should be understood, but for our lab we are going to be looking at the sensors that collect data in these 7 bands of the electromagnetic spectrum. Our “eyes” are sensors that collect data in three bands of the spectrum that correspond to Bands 1, 2 and 3. For most of us, our eyes and brain assigns the values and produces colored images using a mix of Blue, Green and Red.
Band 1: m (Blue)
Band 2: m (Green)
Band 3: m (Red)
Band 4: m (Near infrared)
Band 5: m (Mid-Infrared)
Band 6: m (Thermal infrared)
Band 7: m (Mid-infrared)
Our Eyes
Landsat 7

14. Spectral Resolutions SAR; radar
Lidar; nm (some visible and some infrared)
Multispectral: nm (some visible and some infrared)
Lidar
Multispectral

15. 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

16. Look at Examples of Different types of Imagery and compare their “footprints” – logon to link below:

17. Temporal Resolution How often data is collected of the same location
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 coverage)
Must be “important” (U.S. and outside U.S.)

18. Landsat Image – Orbits (Path and Rows)
View Orbits video

19. Why focus on Landsat Data?
Cost
Access
Archive
Tools and other resources

20. Atmosphere “blocks” some wavelengths: sensors collect wavelength data in specific regions (bands or channels) of the spectrum
Lidar

21. Gray shading: Wavelength Regions with good transmission
Lidar

22. What Does “data” look like
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

23. 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 band
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) A B C

24. Creating Visualizations: Composites
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

25. Landsat 7. Natural or True Color. Bands 3, 2, 1. False Color
Landsat 7 Natural or True Color Bands 3, 2, 1 False Color Band 5, 4, Pseudo Color Bands 7, 5, 3

26. 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

27. Resource for Viewing Natural and False Color Composites on USGS Website
xamples.php
Go to this site and use the swipe to see the difference using different bands for images from four regions of the U.S.

28. Change Matters Website
See imagery/viewer

29. Identifying and Classifying Features
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”

30. Spectral Signatures From Different Surfaces in an Image

31. NDVI –Image Analysis and “Greeness” Using NIR and Red Bands
NDVI Leaf
Using the Image Analysis Window, you can easily use the
NDVI algorithm by highlighting band 3 and 4, then click on the
NDVI button that looks like a green maple leaf in the Processing
part of the Window. The resulting image is added to the map, displaying
the vegetation in shades of green and non-vegetation
From yellow and orange to red.

32. Land Cover Change – and “Greeness” - NDVI

33. 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

34. 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.)

35. 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.
Software uses spectral value file of the different features and classifies pixels based on the specified land cover types.

36. So many satellites! Resources:
Satellite Viewer
al&satellite=14484
EarthNow! Landsat Image Viewer
Real time view as data is collected showing current path of satellite
tml?sessionId=fdbe7bc eda2c68d 1e603ed

37. Finding Data:. https://lpdaac. usgs. gov/data_access. https://lpdaac
Go to GloVIS and Try Path 41 and Row 36

38. Lidar – Active Remote Sensing. NOAA Lidar Tutorial: http://www. csc

39. Thank You!
Much of the material for this Presentation was developed by iGETT-Remote Sensing grant from the National Science Foundation (DUE )
More Exercises:
iGETT.delmar.edu
Concept Modules on YouTube Channel at
iGETT Remote Sensing Education
Ann Johnson