1 / 46

GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing

GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592 Email: mdisney@ucl.geog.ac.uk http ://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141. html

tybalt
Download Presentation

GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. GEOGG141/ GEOG3051Principles & Practice of Remote Sensing (PPRS)1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592 Email: mdisney@ucl.geog.ac.uk http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html http://www2.geog.ucl.ac.uk/~mdisney/teaching/3051/GEOG3051.html

  2. Format • 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…)

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

  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 http://www.cla.sc.edu/geog/rslab/751/index.html 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 ImageInterpretation, John Wiley, New York. Mather, P.M. (2004) Computer Processing of Remotely‑sensedImages, 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 & www.geog.ucl.ac.uk/~mdisney/pprs.html • 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://ccrs.nrcan.gc.ca/resource/index_e.php#tutor • http://octopus.gma.org/surfing/satellites/index.html • Glossary :http://ccrs.nrcan.gc.ca/glossary/index_e.php • Other resources • NASA www.nasa.gov • NASAs Visible Earth (source of data): http://visibleearth.nasa.gov/ • European Space Agency earth.esa.int (eg Image of the week….) • NOAA www.noaa.gov • IKONOS: http://www.spaceimaging.com/ • QuickBird: http://www.digitalglobe.com/

  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 • Concepts and terms • remote sensing process, end-to-end

  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” http://ccrs.nrcan.gc.ca/glossary/index_e.php?id=486 • 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 http://www.ccrs.nrcan.gc.ca/ccrs/eduref/misc)

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

  12. 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?

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

  14. Remote Sensing Examples • Global maps of vegetation from MODIS instrument IKONOS-2 image of Venice http://www.esa.int/esaEO/SEM44R0UDSG_index_1.html

  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. 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

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

  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. 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

  23. Spectral information: vegetation

  24. 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

  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. 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

  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 • 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)

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

  36. 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

  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

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

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

  41. Example biophysical variables After Jensen, p. 9

  42. Example biophysical variables Good discussion of spectral information extraction: http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf 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….. • 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

More Related