CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel:

2 Term 1 –Radiometric principles and data collection (Disney) –Mapping science (Dowman, Iliffe, Haklay, Backes, Smith, Cross) –Computing for image analysis (Lewis) –Analytical methods (Ziebart, Iliffe) –Image processing & GIS (Liu) –Organisations (Harris) –Global Monitoring of Environment & Security (Muller, Disney,Laxon etc.) –Seminars (Thurs afternoons, 5-6 pm dates TBC, room rd floor Pearson BUT north entrance!) Format of the course

3 Term 2 –Advanced Modules Geomatics for coastal zone/Geomatics for ocean management (Oceans 2) (Simons, Morley, Iliffe) Topographic mapping/Terrestrial laser scanning(Dowman, Backes, Robson) Airborne laser scanning/Digital mapping (Dowman, Backes,Lewis) Renewable natural resources (Lewis, Wooster) Term 3 –Research project Format of the course

4 Remote Sensing and Photogrammetry Society – –£19 for students + get 1 yr RSE for €83 –student meeting Mar 2008, New Forest, organised by Tina Thomson from GE –travel bursaries NERC National Centre for Earth Observation (NCEO) –involvment in several themes at UCL –Solid Earth (Centre for the Observation and Modelling of Earthquakes & GE –NERC National Centre for Earth Observation (NCEO ) Cryosphere (Centre for Polar Observation and Earth Sciences: Carbon Theme (formerly Carbon Centre for Terrestrial Carbon Geography Miscellaneous

5 Reading and browsing Remote sensing Campbell, J. B. (1996) Introduction to Remote Sensing (2nd Ed), London:Taylor and Francis. Harris, R. (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 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. (1999) Computer Processing of Remotely ‑ sensed Images, 2nd Edition. John Wiley and Sons, Chichester. W.G. Rees, "Physical Principles of Remote Sensing", Cambridge Univ. Press. 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.

6 Moodle & Web Tutorials Glossary of alphabet soup acronyms! Other resources NASA NASAs Visible Earth (source of data): European Space Agency earth.esa.int NOAA Remote sensing and Photogrammetry Society UK IKONOS: QuickBird: Reading and browsing

7 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 Lecture outline

8 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.” What is remote sensing?

9 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)?

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

11 Remote Sensing Examples Kites (still used!) Panorama of San Francisco, 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

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

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

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

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 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.....

22 Remote sensing process: I Statement of problem What information do we want? Appropriate problem-solving approach? Formulate hypothesis Hypothesis testing In situ: field, lab, ancillary data (Meteorology? Historical? Other?) EO data: Type? Resolution? Cost? Availability? Pre/post processing? Data collection Analog: visual, expert interp. Digital: spatial, photogrammetric, spectral etc. Modelling: prediction & understanding Information extraction Data analysis Products: images, maps, thematic maps, databases etc. Models: parameters and predictions Quantify: error & uncertainty analysis Graphs and statistics Presentation of information

23 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)

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 (3x10 8 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

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 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

29 Spectral information: vegetation

30 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

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 Temporal information Change detection Rondonia 1986 Rondonia 1992

35 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

36 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.....

37 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)

38 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?

39 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.

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 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

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

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

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

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

47 Example biophysical variables After Jensen, p. 9

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

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

50 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)

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

52 ceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoo m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20Englandhttp:// ceimaging.com/gallery/zoomify/london_08_08_03/&zoomifyX=0&zoomifyY=0&zoomifyZoo m=10&zoomifyToolbar=1&zoomifyNavWin=1&location=London,%20England Interesting stuff…..