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Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building

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Presentation on theme: "Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building"— Presentation transcript:

1 GEOGG141/ 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 Component 1 (GEOGG141 only) Component 2 (GEOGG141 & GEOG3051)
Mapping principles (Dowman, Iliffe, Haklay, Backes, Smith, Cross) Understanding the geometry of data acquisition Orbits, geoids and principles of geodesy Component 2 (GEOGG141 & GEOG3051) Radiometric principles (Disney) Understanding the what we measure and how Radiative transfer (GEOGG141 only – Reading Week) Resolution, sampling and practical tradeoffs Pre-processing and ground segment Active remote sensing (LIDAR, RADAR…)

3 Miscellaneous Remote Sensing at UCL
NERC National Centre for Earth Observation (NCEO) Involvement in several themes at UCL Earth Sciences: (Wingham, Laxman et al.) Carbon Geography (Lewis, Mat Disney et al.) Solid Earth: GE (Ziebart) More generally MSSL: e.g. imaging (Muller), planetary, astro, instruments UK prof. body - Remote Sensing and Photogrammetry Society

4 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.

5 Browsing Moodle & Web Tutorials
Glossary : Other resources NASA NASAs Visible Earth (source of data): European Space Agency (eg Image of the week….) NOAA IKONOS: QuickBird:

6 Today General introduction to remote sensing (RS), Earth Observation (EO) definitions of RS Concepts and terms remote sensing process, end-to-end Radiation I

7 What is remote sensing? The Experts say "Remote Sensing (RS) is...”
“The science technology and art of obtaining information about objects or phenomena from a distance (i.e. without being in physical contact with them” But not the whole story: Tend to use Earth Observation (EO). To distinguish from? Domains (atmosphere, terrestrial, ocean, cryosphere, biosphere etc) But also astronomy, planetary remote sensing etc.

8 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

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

10 Remote Sensing Examples

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

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

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

14 Remote Sensing Examples
IKONOS-2 image of Venice Remote Sensing Examples Global maps of vegetation from MODIS instrument

15 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

16 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

17 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)

18 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

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

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

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

22 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

23 Spectral information: vegetation

24 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

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

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

27 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

28 Temporal information Change detection Rondonia 1975 Rondonia 1986

29 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

30 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

31 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)

32 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.)

33 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.

34 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

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

36 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

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

38 Output: above/below canopy signal
Light environment below a deciduous (birch) canopy 38

39 LIDAR signal: single birch tree
Higher density Allows interpretation of signal, development of new methods 39

40 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.

41 Example biophysical variables
After Jensen, p. 9

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

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

44 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)

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

46 Interesting stuff….. m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20England

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