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GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 113,

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Presentation on theme: "GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 113,"— 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 2 Component 1 (GEOGG141 only) –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…) Format

3 3 Remote Sensing at UCL –NERC National Centre for Earth Observation (NCEO) ) –Involvement in several themes at UCL Earth Sciences: (Wingham, Laxman et al.)http://www.cpom.org/ Carbon Geography (Lewis, Mat Disney et al.) Solid Earth: GE (Ziebart)http://comet.nerc.ac.uk/ –More generally MSSL: e.g. imaging (Muller), planetary, astro, instrumentshttp://www.ucl.ac.uk/mssl UK prof. body - Remote Sensing and Photogrammetry Society –http://www.rspsoc.org/ Miscellaneous

4 4 Reading and browsing Remote sensing Campbell, J. B. (2006) Introduction to Remote Sensing (4 th ed), London:Taylor and Francis. Harris, R. (1987) "Satellite Remote Sensing, An Introduction", Routledge & Kegan Paul. Jensen, J. R. (2006, 2 nd 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, 2 nd 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”, 2 nd 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 5 Moodle & Web Tutorials Glossary : Other resources NASA NASAs Visible Earth (source of data): European Space Agency earth.esa.int (eg Image of the week….) NOAA IKONOS: QuickBird: Browsing

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

7 7 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. What is remote sensing?

8 8 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 What is remote sensing (II)?

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

10 10 Remote Sensing Examples

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

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

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

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

15 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 16 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 EO process in summary.....

17 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 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 19 Electromagnetic radiation? Electric field (E) Magnetic field (M) Perpendicular and travel at velocity, c (3x10 8 ms -1 )

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

21 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 22 Spectral information: vegetation Wavelength, nm reflectance(%) very high leaf area very low leaf area sunlit soil NIR, high reflectance Visible red, low reflectance Visible green, higher than red

23 23 Spectral information: vegetation

24 24 Colour Composites: spectral ‘Real Colour’ composite Red band on red Green band on green Blue band on blue Approximates “real” colour (RGB colour composite) Landsat TM image of Swanley, 1988

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

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

27 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 28 Rondonia 1975 Temporal information Change detection Rondonia 1986 Rondonia 1992

29 29 Colour Composites: angular ‘False Colour’ composite many channel data, much not comparable to RGB (visible) –e.g. MISR -Multi-angular data (August 2000) Real colour composite (RCC) Northeast Botswana 0 o ; +45 o ; -45 o

30 30 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 Always bear in mind.....

31 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 32 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.) Why do we study/use remote sensing?

33 33 Caveats! Remote sensing has many problems –Can be expensive –Technically difficult –NOT direct measure surrogate variables e.g. reflectance (%), brightness temperature (Wm -2  o K), backscatter (dB) RELATE to other, more direct properties.

34 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 35 Back to the process.... What sort of parameters are of interest? Variables describing Earth system....

36 36 Information extraction process After Jensen, p. 22 Image interpretation Tone, colour, stereo parallax Size, shape, texture, pattern, fractal dimension Height/shadow Site, association Primary elements Spatial arrangements Secondary elements Context Analogue image processing Multi: spectral, spatial, temporal, angular, scale, disciplinary Visualisation Ancillary info.: field and lab measurements, literature etc. Presentation of information Multi: spectral, spatial, temporal, angular, scale, disciplinary Statistical/rule- based patterns Hyperspectral Modelling and simulation

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

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

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

40 40 EO and the Earth “System” From Ruddiman, W. F., Earth's Climate: past and future. External forcing Hydrosphere Atmosphere Geosphere Cryosphere Biosphere

41 41 Example biophysical variables After Jensen, p. 9

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

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

44 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 45 Electromagnetic spectrum Interaction with the atmosphere –transmission NOT even across the spectrum –need to choose bands carefully!

46 46 ceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoo m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20Englandhttp://www.spaceimaging.com/gallery/zoomviewer.asp?zoomifyImagePath=http://www.spa ceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoo m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20England Interesting stuff…..


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