geogg141/ geog3051 principles & practice of remote sensing (pprs) 1: introduction to remote...
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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: [email protected]
www.geog.ucl.ac.uk/~mdisney
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• 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
Format
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• 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/
Miscellaneous
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Reading and browsingRemote 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 Image Interpretation, John Wiley, New York.
Mather, P. M. (2004) Computer Processing of Remotely‑sensed Images, 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.GeneralMonteith, 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.
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• 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/
Browsing
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• 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
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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
What is remote sensing?
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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)
What is remote sensing (II)?
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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.
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Remote Sensing Examples
•Kites (still used!) Panorama of San Francisco, 1906.
•Up to 9 large kites used to carry camera weighing 23kg.
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Remote Sensing Examples
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Remote Sensing: scales and platforms
•Not always big/expensive equipment
•Individual/small groups
•Calibration/validation campaigns
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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/
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Remote Sensing: scales and platforms
•Platform depends on application
•What information do we want?
•How much detail?
•What type of detail?
upscale
http://www-imk.fzk.de:8080/imk2/mipas-b/mipas-b.htm
upscale upscale
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Remote Sensing: scales and platforms
•E.g. aerial photography
•From multimap.com
•Most of UK
•Cost? Time?
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Remote Sensing: scales and platforms
•Many types of satellite
•Different orbits, instruments, applications
upscale
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Remote Sensing Examples
•Global maps of vegetation from MODIS instrument
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Remote Sensing Examples
•Global maps of sea surface temperature and land surface reflectance from MODIS instrument
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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
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• 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.....
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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
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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)
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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
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Electromagnetic radiation?
•Electric field (E)
•Magnetic field (M)
•Perpendicular and travel at velocity, c (3x108 ms-1)
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• Energy radiated from sun (or active sensor)• Energy 1/wavelength (1/)
– shorter (higher f) == higher energy
– longer (lower f) == lower energyfrom http://rst.gsfc.nasa.gov/Intro/Part2_4.html
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Information
• What type of information are we trying to get at?
• What information is available from RS?– Spatial, spectral, temporal, angular,
polarization, etc.
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Spectral information: vegetation
Wavelength, nm
400 600 800 1000 1200
refle
ctan
ce(%
)
0.0
0.1
0.2
0.3
0.4
0.5
very high leaf area
very low leaf area
sunlit soil
NIR, high reflectance
Visible red, low reflectance
Visible green, higher than red
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Spectral information: vegetation
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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
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Colour Composites: spectral
‘False Colour’ composite (FCC)NIR band on red
red band on green
green band on blue
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Colour Composites: spectral
‘False Colour’ compositeNIR band on red
red band on green
green band on blue
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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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Rondonia 1975
Temporal information
Change detection
http://earth.jsc.nasa.gov/lores.cgi?PHOTO=STS046-078-026
http://www.yale.edu/ceo/DataArchive/brazil.html
Rondonia 1986
Rondonia 1992
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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
0o; +45o; -45o
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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.....
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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)
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• 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?
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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.
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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
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Back to the process....
• What sort of parameters are of interest?
• Variables describing Earth system....
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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
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Example: Vegetation canopy modelling•Develop detailed 3D models
•Simulate canopy scattering behaviour
•Compare with observations
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Output: above/below canopy signal
Light environment below a deciduous (birch) canopy
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LIDAR signal: single birch tree
Allows interpretation of signal, development of new methods
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EO and the Earth
“System”
From Ruddiman, W. F., 2001. Earth's Climate: past and future.
External forcing
Hydrosphere
Atmosphere
Geosphere
Cryosphere
Biosphere
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Example biophysical variables
After Jensen, p. 9
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Example biophysical variables
After Jensen, p. 9
Good discussion of spectral information extraction:
http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf
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Remote Sensing Examples
Ice sheet dynamics
Wingham et al. Science, 282 (5388): 456.
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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 ~0.7m (10-6 m)
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Electromagnetic spectrum
• Interaction with the atmosphere– transmission NOT even across the spectrum– need to choose bands carefully!
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• 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
Interesting stuff…..