Principles & Practice of Remote Sensing: Introduction to Remote Sensing

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 www.geog.ucl.ac.uk/~mdisney

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 principles of radiation • Orbits, geoids and principles of geodesy

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://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/

6. 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

7. 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

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

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

11. Remote Sensing Examples

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

13. 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/

14. 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?

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

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

17. Remote Sensing Examples • Global maps of vegetation from MODIS instrument

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

19. 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

20. 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

21. 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

22. 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

23. 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

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

25. 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

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

27. 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

28. Spectral information: vegetation

29. 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

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

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

32. 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

33. 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

34. 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

35. 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

36. 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)

37. 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.)

38. 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.

39. 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

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

41. 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

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

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

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

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

46. Example biophysical variables After Jensen, p. 9

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

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

49. 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)

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