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Introduction to Remote Sensing Principles and Practices

Understand the basics of remote sensing technology, applications, and data analysis. Explore radiometric and geometric principles, computing methods, and information extraction. Dive into global change monitoring and advanced modules.

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Introduction to Remote Sensing Principles and Practices

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  1. MSc Remote Sensing 2006-7Principles & Practice of Remote Sensing (PPRS)1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney

  2. Format of the course • Term 1 • Radiometric principles and data collection (Disney, Harris) • Geometric principles (Cross, Dowman, Iliffe, Harris) • Computing methods (Haklay, Lewis, Morley) • Information extraction (Liu, Mason) • Organisations (Harris) • Global change monitoring (Disney, Hunt, Laxon, Morley, Muller, Mason, Wingham) • Seminars (Thurs afternoons, 5-6 pm from 20th October)

  3. Format of the course • Term 2 • Advanced Modules • Oceans • Topo/Dig Mapping • Vegetation science & renewable natural resources • Image Understanding • Term 3 • Research project

  4. Miscellaneous • Remote Sensing and Photogrammetry Society • http://www.rspsoc.org/ • £19 for students + get 1 yr IJRS for £55 and/or RSE for €79 • student meeting Mar 2007, Edinburgh, Scotland • travel bursaries • NERC EO Centres of Excellence • involvment in 3 out of 6 at UCL • COMET (Centre for the Observation and Modelling of Earthquakes & Tectonics) @ GE http://comet.nerc.ac.uk/ • CPOM (Centre for Polar Observation and Modelling) @ Earth Sciences: http://www.cpom.org/ • CTCD (Centre for Terrestrial Carbon Dynamics) @ Geography http://ctcd.nerc.ac.uk

  5. Reading and browsing Campbell, J.B. (1996) Introduction to Remote Sensing (2nd Ed),London:Taylor and Francis. R. Harris, 1987. "Satellite Remote Sensing, An Introduction", Routledge & Kegan Paul. Jensen, J. R. (2000) Remote Sensing of the Environment: An Earth Resource Perspective, 2000, 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 mostly available online at http://www.cla.sc.edu/geog/rslab/751/index.html Lillesand, T.M., Kiefer, R.W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing and ImageInterpretation, John Wiley, New York. Mather, P.M. (1999) Computer Processing of Remotely‑sensedImages, 2nd Edition. John Wiley and Sons, Chichester. W.G. Rees, 1996. "Physical Principles of Remote Sensing", Cambridge Univ. Press

  6. Reading and browsing • Web • Tutorials • http://rst.gsfc.nasa.gov/ • http://earth.esa.int/applications/data_util/SARDOCS/spaceborne/Radar_Courses/ • http://www.crisp.nus.edu.sg/~research/tutorial/image.htm • http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/fundam_e.html • http://octopus.gma.org/surfing/satellites/index.html • Glossary of alphabet soup acronyms! http://www.ccrs.nrcan.gc.ca/ccrs/learn/terms/glossary/glossary_e.html • Other resources • NASA www.nasa.gov • NASAs Visible Earth (source of data): http://visibleearth.nasa.gov/ • European Space Agency earth.esa.int • NOAA www.noaa.gov • Remote sensing and Photogrammetry Society UK www.rspsoc.org • IKONOS: http://www.spaceimaging.com/ • QuickBird: http://www.digitalglobe.com/

  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.” http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_1_e.html

  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 http://www.ccrs.nrcan.gc.ca/ccrs/eduref/misc)

  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 • http://arch.ced.berkeley.edu/kap/kaptoc.html • http://activetectonics.la.asu.edu/Fires_and_Floods/

  15. upscale upscale upscale http://www-imk.fzk.de:8080/imk2/mipas-b/mipas-b.htm Remote Sensing: scales and platforms • 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. upscale Remote Sensing: scales and platforms • 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. Statement of problem Data collection Data analysis Presentation of information • 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? • Analog: visual, expert interp. • Digital: spatial, photogrammetric, spectral etc. • Modelling: prediction & understanding • Information extraction • Products: images, maps, thematic maps, databases etc. • Models: parameters and predictions • Quantify: error & uncertainty analysis • Graphs and statistics Remote sensing process: I Formulate hypothesis Hypothesis testing

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

  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 http://rst.gsfc.nasa.gov/Intro/Part2_4.html

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

  29. Spectral information: vegetation

  30. Red band on red Green band on green Blue band on blue Colour Composites: spectral ‘Real Colour’ composite 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. Rondonia 1975 Rondonia 1986 Rondonia 1992 Temporal information Change detection http://earth.jsc.nasa.gov/lores.cgi?PHOTO=STS046-078-026 http://www.yale.edu/ceo/DataArchive/brazil.html

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

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

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

  44. External forcing Hydrosphere Cryosphere Atmosphere Geosphere Biosphere EO and the Earth “System” From Ruddiman, W. F., 2001. Earth's Climate: past and future.

  45. Example biophysical variables After Jensen, p. 9

  46. Example biophysical variables Good discussion of spectral information extraction: http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf After Jensen, p. 9

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

  48. 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)

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

  50. Interesting stuff….. • http://www.spaceimaging.com/gallery/zoomviewer.asp?zoomifyImagePath=http://www.spaceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoom=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20England • http://www.digitalglobe.com/images/katrina/new_orleans_dwtn_aug31_05_dg.jpg • http://www.spaceimaging.com/gallery/tsunami/default.htm • http://www.spaceimaging.com/gallery/9-11/default.htm

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