1. CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing
Dr. Mathias (Mat) Disney
UCL Geography
Office: 113, Pearson Building
Tel:

2. Format of the course Term 1
Radiometric principles and data collection (Disney)
Mapping science (Dowman, Iliffe, Haklay, Backes, Smith, Cross)
Computing for image analysis (Lewis)
Analytical methods (Ziebart, Iliffe)
Image processing & GIS (Liu)
Organisations (Harris)
Global Monitoring of Environment & Security (Muller, Disney,Laxon etc.)
Seminars (Thurs afternoons, 5-6 pm dates TBC, room 304 3rd floor Pearson BUT north entrance!)

3. Format of the course Term 2 Term 3 Advanced Modules Research project
Geomatics for coastal zone/Geomatics for ocean management (Oceans 2) (Simons, Morley, Iliffe)
Topographic mapping/Terrestrial laser scanning(Backes, Robson)
Airborne laser scanning/Digital mapping (Backes, Lewis)
Renewable natural resources (Lewis, Wooster)
Term 3
Research project

4. Miscellaneous Remote Sensing and Photogrammetry Society
£19 for students + get 1 yr RSE for €83
student meeting Mar 2008, New Forest, organised by Tina Thomson from GE
travel bursaries
NERC National Centre for Earth Observation (NCEO)
involvment in several themes at UCL
Solid Earth (Centre for the Observation and Modelling of Earthquakes & GE
NERC National Centre for Earth Observation (NCEO
Cryosphere (Centre for Polar Observation and Earth Sciences:
Carbon Theme (formerly Carbon Centre for Terrestrial Carbon Geography

5. Reading and browsing Remote sensing
Campbell, J. B. (2006) Introduction to Remote Sensing (4th ed), London:Taylor and Francis.
Harris, R. (1987) "Satellite Remote Sensing, An Introduction", Routledge & Kegan Paul.
Jensen, J. R. (2006, 2nd ed) Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall, New Jersey. (Excellent on RS but no image processing).
Jensen, J. R. (2005, 3rd ed.) Introductory Digital Image Processing, Prentice Hall, New Jersey. (Companion to above) BUT some available online at
Jones, H. and Vaughan, R. (2010, paperback) Remote Sensing of Vegetation: Principles, Techniques, and Applications, OUP, Oxford. Excellent.
Lillesand, T. M., Kiefer, R. W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing and Image Interpretation, John Wiley, New York.
Mather, P. M. (2004) Computer Processing of Remotely‑sensed Images, 3rdEdition. John Wiley and Sons, Chichester.
Rees, W. G. (2001, 2nd ed.). Physical Principles of Remote Sensing, Cambridge Univ. Press.
Warner, T. A., Nellis, M. D. and Foody, G. M. eds. (2009) The SAGE Handbook of Remote Sensing (Hardcover). Limited depth, but very wide-ranging – excellent reference book.
General
Monteith, J. L. and Unsworth, M. H. (1990) ”Principles of Environmental Physics”, 2nd ed. Edward Arnold, London.
Hilborn, R. and Mangel, M. (1997) “The Ecological Detective: Confronting models with data”, Monographs in population biology 28, Princeton University Press, New Jersey, USA.

6. Reading and browsing Moodle & www.geog.ucl.ac.uk/~mdisney/pprs.html
Web
Tutorials
Glossary of alphabet soup acronyms!
Other resources
NASA
NASAs Visible Earth (source of data):
European Space Agency earth.esa.int
NOAA
Remote sensing and Photogrammetry Society UK
IKONOS:
QuickBird:

7. Lecture outline
General introduction to remote sensing (RS), Earth Observation (EO)
definitions of RS
Why do we do it?
Applications and issues
Who and where?
Concepts and terms
remote sensing process, end-to-end

8. What is remote sensing? The Experts say "Remote Sensing is...”
...techniques for collecting image or other forms of data about an object from measurements made at a distance from the object, and the processing and analysis of the data (RESORS, CCRS).
”...the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information.”

9. What is remote sensing (II)?
The not so experts say "Remote Sensing is...”
Advanced colouring-in.
Seeing what can't be seen, then convincing someone that you're right.
Being as far away from your object of study as possible and getting the computer to handle the numbers.
Legitimised voyeurism
(more of the same from

10. Remote Sensing Examples
First aerial photo credited to Frenchman Felix Tournachon in Bievre Valley, 1858.
Boston from balloon (oldest preserved aerial photo), 1860, by James Wallace Black.

11. Remote Sensing Examples
Kites (still used!) Panorama of San Francisco, 1906.
Up to 9 large kites used to carry camera weighing 23kg.

12. Remote Sensing Examples

13. Remote Sensing: scales and platforms
Not always big/expensive equipment
Individual/small groups
Calibration/validation campaigns

14. Remote Sensing: scales and platforms
Both taken via kite aerial photography

15. Remote Sensing: scales and platforms
upscale
upscale
Platform depends on application
What information do we want?
How much detail?
What type of detail?

16. Remote Sensing: scales and platforms
E.g. aerial photography
From multimap.com
Most of UK
Cost? Time?

17. Remote Sensing: scales and platforms
upscale
Many types of satellite
Different orbits, instruments, applications

18. Remote Sensing Examples
Global maps of vegetation from MODIS instrument

19. Remote Sensing Examples
Global maps of sea surface temperature and land surface reflectance from MODIS instrument

20. Remote sensing applications
Environmental: climate, ecosystem, hazard mapping and monitoring, vegetation, carbon cycle, oceans, ice
Commercial: telecomms, agriculture, geology and petroleum, mapping
Military: reconnaissance, mapping, navigation (GPS)
Weather monitoring and prediction
Many, many more

21. EO process in summary..... Collection of data
Some type of remotely measured signal
Electromagnetic radiation of some form
Transformation of signal into something useful
Information extraction
Use of information to answer a question or confirm/contradict a hypothesis

22. Remote sensing process: I
Statement of problem
What information do we want?
Appropriate problem-solving approach?
In situ: field, lab, ancillary data (Meteorology? Historical? Other?)
EO data: Type? Resolution? Cost? Availability?
Pre/post processing?
Data collection
Analog: visual, expert interp.
Digital: spatial, photogrammetric, spectral etc.
Modelling: prediction & understanding
Information extraction
Data analysis
Products: images, maps, thematic maps, databases etc.
Models: parameters and predictions
Quantify: error & uncertainty analysis
Graphs and statistics
Presentation of information
Formulate hypothesis
Hypothesis testing

23. The Remote Sensing Process: II
Collection of information about an object without coming into physical contact with that object
Passive: solar reflected/emitted
Active:RADAR (backscattered); LiDAR (reflected)

24. The Remote Sensing Process: III
What are we collecting?
Electromagnetic radiation (EMR)
What is the source?
Solar radiation
passive – reflected (vis/NIR), emitted (thermal)
OR artificial source
active - RADAR, LiDAR

25. Electromagnetic radiation?
Electric field (E)
Magnetic field (M)
Perpendicular and travel at velocity, c (3x108 ms-1)

26. Energy radiated from sun (or active sensor)
Energy  1/wavelength (1/)
shorter  (higher f) == higher energy
longer  (lower f) == lower energy
from

27. Information What type of information are we trying to get at?
What information is available from RS?
Spatial, spectral, temporal, angular, polarization, etc.

28. Spectral information: vegetation
Wavelength, nm
400
600
800
1000
1200
reflectance(%)
0.0
0.1
0.2
0.3
0.4
0.5
very high leaf area
very low leaf area
sunlit soil
NIR, high reflectance
Visible green, higher than red
Visible red, low reflectance

29. Spectral information: vegetation

30. Colour Composites: spectral
‘Real Colour’ composite
Green band on green
Red band on red
Blue band on blue
Approximates “real” colour (RGB colour composite)
Landsat TM image of Swanley, 1988

31. Colour Composites: spectral
‘False Colour’ composite (FCC)
NIR band on red
red band on green
green band on blue

32. Colour Composites: spectral
‘False Colour’ composite
NIR band on red
red band on green
green band on blue

33. Colour Composites: temporal
‘False Colour’ composite
many channel data, much not comparable to RGB (visible)
e.g. Multi-temporal data
but display as spectral
AVHRR MVC 1995
April
August
September

34. Temporal information Change detection Rondonia 1975 Rondonia 1986

35. Colour Composites: angular
‘False Colour’ composite
many channel data, much not comparable to RGB (visible)
e.g. MISR -Multi-angular data (August 2000)
0o; +45o; -45o
Real colour composite (RCC)
Northeast Botswana

36. Always bear in mind.....
when we view an RS image, we see a 'picture’ BUT need to be aware of the 'image formation process' to:
understand and use the information content of the image and factors operating on it
spatially reference the data

37. Why do we use remote sensing?
Many monitoring issues global or regional
Drawbacks of in situ measurement …..
Remote sensing can provide (not always!)
Global coverage
Range of spatial resolutions
Temporal coverage (repeat viewing)
Spectral information (wavelength)
Angular information (different view angles)

38. Why do we study/use remote sensing?
source of spatial and temporal information (land surface, oceans, atmosphere, ice)
monitor and develop understanding of environment (measurement and modelling)
information can be accurate, timely, consistent
remote access
some historical data (1960s/70s+)
move to quantitative RS e.g. data for climate
some commercial applications (growing?) e.g. weather
typically (geo)'physical' information but information widely used (surrogate - tsetse fly mapping)
derive data (raster) for input to GIS (land cover, temperature etc.)

39. Caveats! Remote sensing has many problems Can be expensive
Technically difficult
NOT direct
measure surrogate variables
e.g. reflectance (%), brightness temperature (Wm-2  oK), backscatter (dB)
RELATE to other, more direct properties.

40. Colour Composites: polarisation
‘False Colour’ composite
many channel data, much not comparable to RGB (visible)
e.g. Multi-polarisation SAR
HH: Horizontal transmitted polarization and Horizontal received polarization
VV: Vertical transmitted polarization and Vertical received polarization
HV: Horizontal transmitted polarization and Vertical received polarization

41. What sort of parameters are of interest?
Back to the process....
What sort of parameters are of interest?
Variables describing Earth system....

42. Information extraction process
Analogue image processing
Multi:
spectral, spatial, temporal, angular, scale, disciplinary
Visualisation
Ancillary info.: field and lab measurements, literature etc.
Image interpretation
Tone, colour, stereo parallax
Size, shape, texture, pattern, fractal dimension
Height/shadow
Site, association
Primary elements
Spatial arrangements
Secondary elements
Context
Presentation of information
Multi:
spectral, spatial, temporal, angular, scale, disciplinary
Statistical/rule-based patterns
Hyperspectral
Modelling and simulation
After Jensen, p. 22

43. Example: Vegetation canopy modelling
Develop detailed 3D models
Simulate canopy scattering behaviour
Compare with observations

44. Output: above/below canopy signal
Light environment below a deciduous (birch) canopy 44

45. LIDAR signal: single birch tree
Higher density
Allows interpretation of signal, development of new methods 45

46. EO and the Earth “System”
Atmosphere
EO and the Earth “System”
External forcing
Cryosphere
Geosphere
Biosphere
Hydrosphere
From Ruddiman, W. F., Earth's Climate: past and future.

47. Example biophysical variables
After Jensen, p. 9

48. Example biophysical variables
Good discussion of spectral information extraction:
After Jensen, p. 9

49. Remote Sensing Examples
Ice sheet dynamics
Wingham et al. Science, 282 (5388): 456.

50. Electromagnetic spectrum
Zoom in on visible part of the EM spectrum
very small part
from visible blue (shorter )
to visible red (longer )
~0.4 to ~0.7m (10-6 m)

51. Electromagnetic spectrum
Interaction with the atmosphere
transmission NOT even across the spectrum
need to choose bands carefully!

52. Interesting stuff…..
ceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoo m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20England