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MSc Conservation 2008 Remote sensing module: lecture 1

MSc Conservation 2008 Remote sensing module: lecture 1. Dr. Mathias (Mat) Disney UCL Geography Office: 113, 1 st Floor, Pearson Building Tel: 7670 0592 (x30592) Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney. Module structure. Day 1 AM Lecture: Introduction

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MSc Conservation 2008 Remote sensing module: lecture 1

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  1. MSc Conservation 2008 Remote sensing module: lecture 1 Dr. Mathias (Mat) Disney UCL Geography Office: 113, 1st Floor, Pearson Building Tel: 7670 0592 (x30592) Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney

  2. Module structure • Day 1 • AM Lecture: Introduction • PM Practical I: Introduction session in basement UNIX lab (PB 110) • Day 2 • AM Practical I continued. • PM Lecture: Spatial, spectral and temporal information • Day 3 • AM Practical II: Spatial information. • PM Self-directed research: reading, consolidation of practical work. • Day 4 • AM Lecture: Thematic information extraction and accuracy analysis • PM Practical III: Classification • Day 5 • AM Practical III continued & preparation for presentations • PM Presentations of practical III results and analysis

  3. Lecture outline • Lecture 1 • General introduction to remote sensing (RS)/Earth Observation (EO)....... • Definitions, concepts and terms + remote sensing process, end-to-end • Lecture 2 (Tues PM) • Information from EO data (spatial & spectral in particular, but also temporal, angular etc.) • Lecture 3 • Thematic information extraction: classification • NB access for PB10 UNIX lab 9-5 only

  4. Reading and browsing Campbell, J.B. (2002) Introduction to Remote Sensing (3rd ed.),London:Taylor and Francis. 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

  5. Web resources Tutorials • http://rst.gsfc.nasa.gov/ • http://www.research.umbc.edu/~tbenja1/umbc7/ • 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/resource/tutor/fundam/index_e.php • http://octopus.gma.org/surfing/satellites/index.html Glossary: • http://www.ccrs.nrcan.gc.ca/glossary/index_e.php Other resources and data sources • ICEDS at GE @ UCL: http://iceds.ge.ucl.ac.uk/ • NASA www.nasa.gov • NASA 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/ • http://rsd.gsfc.nasa.gov/rsd/RemoteSensing.html • Distributed Active Archive Centre: http://edcdaac.usgs.gov/dataproducts.asp • USGS (Landsat data): http://edcimswww.cr.usgs.gov/pub/imswelcome/

  6. Journals • Remote Sensing of Environment (via Science Direct from within UCL): http://www.sciencedirect.com/science?_ob=JournalURL&_cdi=5824&_auth=y&_acct=C000010182&_version=1&_urlVersion=0&_userid=125795&md5=5a4f9b8f79baba2ae1896ddabe172179 • International Journal of Remote Sensing: http://www.tandf.co.uk/journals/titles/01431161.asp • IEEE Transactions on Geoscience and Remote Sensing: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=36 • Some introductory articles on conservation applications: • http://www.geog.ucl.ac.uk/~mdisney/teaching/msc_cons/papers

  7. Miscellaneous • Remote Sensing and Photogrammetry Society • http://www.rspsoc.org/ • £19 for students + get 1 yr IJRS for £55 and/or RSE for €79 • NERC EO Centres of Excellence • involvement 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) @ Space and Climate Physics & MSSL http://www.cpom.org/ • CTCD (Centre for Terrestrial Carbon Dynamics) @ Geography http://ctcd.nerc.ac.uk

  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.”

  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….

  10. Remote Sensing Examples • First aerial photo credited to Frenchman Felix Tournachon, 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, field-scale measurements, aircraft, balloons …..

  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 • Google Earth?

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

  18. Remote sensing applications • Environmental: climate, ecosystem & habitat mapping, land cover change, 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…..

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

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

  21. Remote Sensing Examples • Global maps of land cover/land cover change from MODIS ….. • http://earthobservatory.nasa.gov/Newsroom/LCC/

  22. Key: land cover classes

  23. Key: land cover classes

  24. Remote Sensing Examples • Global maps of land cover/land cover change from MODIS ….. • http://earthobservatory.nasa.gov/Newsroom/LCC/

  25. Why do we use remote sensing? • Many monitoring issues global or regional • Drawbacksof in situ measurement (cost, manpower, accessibility etc.) • Remote sensing can provide (not always!) • Spatial information & wide/global coverage • Range of spatial resolutions • Spectral information • Related to surface biophysical properties (wavelength variation of surface reflectance) • Temporal information • Consistent, timely, repeat viewing • Angular information (different view angles) • Related to surface structure and arrangement of objects

  26. 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.

  27. RS/EO process in summary..... • Collection of data • Some type of remotely measured signal • Electromagnetic radiation (EMR) of some form • Transformation of signal into something useful • Information extraction • Use of information to answer a question, confirm or contradict a hypothesis, or provide ancillary information for wider analysis

  28. A Remote Sensing System • Energy source • platform • sensor • data recording / transmission • ground receiving station • data processing • expert interpretation / data users

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

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

  31. 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

  32. The EM Spectrum Sometime use frequency, f=c/l, where c=3x108 m/s (speed of light) l units (m): 1 nm, 1m, 1mm, 1m f units (Hz): 3x1017 Hz, 3x1014 Hz, 3x1011 Hz, 3x108 Hz

  33. Basic Concepts: 1 • Electromagnetic radiation • wavelengths, atmospheric windows • visible / near infrared (NIR) ('optical') (400-700nm / 700-1500 nm) • thermal infrared (8.5-12.5 m) • microwave (1mm-1m) • (NB: m = 10-6 nm = 10-9)

  34. Basic Concepts: 2 • Temporal Resolution • minutes to days • NOAA (AVHRR), 12 hrs, 1km (1978+) • MODIS Terra/Aqua, 1-2days, 250m++ • Landsat TM, 16 days, 30 m (1972+) • SPOT, 26(...) days, 10-20 m (1986+) • revisit depends on • latitude • sensor FOV, pointing • orbit (inclination, altitude) • cloud cover (for optical instruments) • Orbits • geostationary (36 000 km altitude) • polar orbiting (200-1000 km altitude) • Spatial resolution • 10s cm (??) - 100s km • determined by altitude of satellite (across track), altitude and speed (along track), viewing angle

  35. Major Programs • Geostationary (Met satellites) • Meteosat (Europe) • GOES (US) • GMS (Japan) • INSAT (India) • Polar Orbiting • SPOT (France) • NOAA (US) • ERS-1 & 2, Envisat (Europe) • ADEOS, JERS (Japan) • Radarsat (Canada) • EOS/NPOESS, Landat, NOAA (US)

  36. Physical Basis • measurement of EM radiation • scattered, reflected, emitted • energy sources • Sun (scattered, reflected), Earth (emitted) • Artificial (RADAR, LiDAR, sonar…) • source properties • vary in intensity AND across wavelengths

  37. EM radiation • emitted, scattered or absorbed • intrinsic properties (emission, scattering, absorption) • vary with wavelength • vary with physical / chemical properties • can vary with viewing angle

  38. Data Acquisition • 2) Thermal infrared • energy measured - temperature of surface and emissivity • 3) Microwave • active • microwave pulse transmitted • measure amount scattered back • infer scattering • passive • emitted energy at shorter end of microwave spectrum • RS instrument measures energy received • 3 useful areas of the spectrum:- 1) Visible / near / mid infrared • passive • solar energy reflected by the surface • determine surface (spectral) reflectance • active • LIDAR - active laser pulse • time delay (height) • induce fluorescence (chlorophyll)

  39. Image Formation • Photographic (visible / NIR, recorded on film, (near) instantaneous) • whiskbroom scanner • visible / NIR / MIR / TIR • point sensor using rotating mirror, build up image as mirror scans • Landsat MSS, TM • Pushbroom scanner • mainly visible / NIR • array of sensing elements (line) simultaneously, build up line by line • SPOT

  40. Image Formation: RADAR • real aperture radar • microwave • energy emitted across-track • return time measured (slant range) • amount of energy (scattering) • synthetic aperture radar (SAR) • microwave • higher resolution - extended antenna simulated by forward motion of platform • ERS-1, -2 SAR (AMI), Radarsat SAR, JERS SAR

  41. Quantisation • received energy is a continuous signal (analogue) • quantise (split) into discrete levels (digital) • Recorded levels called digital number (DN) • downloaded to receiving station when in view • 'bits'... (binary digits) • 0-1 (1 bit), 0-255 (8 bits), 0-1023 (10 bits), 0-4095 (12 bit) • quantisation between upper and lower limits (dynamic range) • not necessarily linear • DN in image converted back to meaningful energy measure through calibration • account for atmosphere, geometry, ... • relate energy measure to intrinsic property (reflectance)

  42. Image characteristics • pixel - DN • pixels - 2D grid (array) • rows / columns (or lines / samples) • 3D (cube) if we have more than 1 channel • dynamic range • difference between lowest / highest DN

  43. Example Applications • visible / NIR / MIR - day only, no cloud cover • vegetation amount/dynamics • geological mapping (structure, mineral / petroleum exploration) • urban and land use (agric., forestry etc.) • Ocean temperature, phytoplankton blooms • meteorology (clouds, atmospheric scattering) • Ice sheet dynamics

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

  45. 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

  46. Spectral information: vegetation

  47. 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

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

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

  50. 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

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