1. Image interpretation Aliens create Indian Head with an iPod ?
Badlands Guardian (CBC)
This feature can be found 180 miles SE of Calgary. 50° 1’ N ° 7‘ W

2. Milestones in the History of Remote Sensing
19th century
Invention of photography
use of balloons and kites
1858 first aerial photograph from a captive balloon
pigeons
oblique photos (mountain peaks)
Stereo photography -

3. 20th century 1910s First use of aerial photography from planes
(World War I: photo interpretation)
1920s Development of photogrammetry for mapping
Military use of radar (World War II)
-> Main aerial photo programs in Canada and others
1950s Use of colour photography and infra-red
Term 'remote sensing' first appeared (Evelyn Pruit)
First reconnaissance satellites: Corona Wikipedia Cool Photos
1960s First weather satellites: Tiros (1960); Nimbus (1964)
(and first digital data transfer)

4. Reconnaissance satellite: Corona
~ 7.5m photos
Declassified for environmental studies 2000s
Pentagon
Mt. Ararat

5. Multispectral image processing: The Landsat Era
Launch of Landsat 1 Nasa USGS satellite and the 80metre MultiSpectral Sensor (MSS)
Landsat 4: the 'next generation 30metre sensor': Thematic Mapper (TM) [and Landsat 5 in 1984]
SPOT-1 satellite: 10 and 20metre data (France)
1988: India IRS 1A

6. 1990s 1990s Other countries' satellites / sensors: JERS: Japan
ERS: Europe
RESURS: USSR
1995 Radarsat- Canada's first remote sensing satellite
1999 Landsat 7 Enhanced Thematic Mapper (ETM);
- Reduction of data cost
removal of data copyright - enabled online data libraries

7. New millennium events Many new sensors including very high resolution
2000 Terra satellite: ASTER and MODIS data
2000 High resolution private sector satellites:
Ikonos (2000) and Quickbird (2001)
………many more -> present
Online viewers
2005 Online image viewers- e.g. google earth
Free high resolution image data
2009 Landsat image data archive goes online   (free)
2013 Landsat 8

8. Satellite Image interpretation – manually uses the same factors as Air photos
Colour
Tone
Texture
Pattern
Shape
Shadow
Size
Association
Multispectral
signature
e.g. water reflects no IR

9. Shape: the form of an object on an air photo helps to identify the object. Regular uniform shapes often indicate a human involvement;
Pattern: similar to shape, the spatial arrangement of objects (e.g. row crops vs. pasture) is also useful to identify an object and its usage;

10. Shadow: a shadow provides information about height, shape, and orientation
Texture: the physical characteristics of an object affects how they appear
(e.g. calm water has a smooth texture; a forest canopy has a rough texture);
PCI: ‘TEX’

11. Size: a measure of the object's surface area
(e.g. single-lane vs. multi-lane highways);
Time: temporal characteristics of a series of photographs can be helpful in determining the historical change of an area (e.g. looking at a series of photos of a city taken in different years can help determine the growth of suburban neighbourhoods;

12. Association/Site: associating the presence of one object with another, or relating it to its environment, can help identify the object (e.g. industrial buildings often have access to railway sidings;
Tone/Colour: the colour characteristics of an object, relative to other objects in the photo (e.g. sand has a bright tone, while water usually has a dark tone; tree species can be determined by the colour of their leaves at certain times of the year);

13. Differences with satellite images (compared to aerial photographs)
Spatial resolution
Shape and pattern

14. Landsat Air photo

15. Image fusion / pansharpening
Goal: Combine higher spatial information in one band with higher spectral information in others to create ‘synthetic’ higher resolution multispectral images

16. Shadows: usually from the SE (~10am)

17. Shadows: (almost) always from the SE
Deriba Caldera, Sudan: from the Space Station

18. Great Barrier Reef, Australia

19. Tasmania

20. Bowron Lakes Colour: 3,2,1 composite (e.g. google maps)

21. Colour: ,4,3 composite

22. Histograms: Prince George scene Landsat
3-2-1
5-4-3

23. Air photos: Colour Tone Texture Pattern Shape Shadow ** Size
Association **
Digital Images
Multispectral
signature
e.g. water reflects no IR

24. Reflectance of water based on dissolved solids

25. Clouds and snow

26. Reflection in visible / near IR / midIR
Water: highest in blue, medium in green/red, very low in IR
Snow/ice: high in visible, almost as high in near IR, low in midIR
Less green 3,2,1 composite = less green vegetation
Less green 5,4,3 composite = lower healthy vegetation (Near IR)
Vegetation – coniferous and deciduous are similar in green/red (summer)
Deciduous reflects higher in near and mid IR

27. Reflection in visible / near IR / midIR low-medium-high -> screen colours
Dark green: coniferous
Bright green: deciduous
Pink: arable / agricultural
Light purple: residential / built up
Black: deep water
Blue: shallow or sediment laden water
White: Clouds
Pink-Red: bare rock
Light blue/green: ice/snow

28. Enhancement based on screen display
Based on whole scene Based on zoom into UNBC campus
Screen enhancement does NOT affect digital numbers