MSc Remote Sensing 2006-7 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office:

MSc Remote Sensing 2006-7Principles & Practice of Remote Sensing (PPRS)1: Introduction to Remote Sensing

Dr. Mathias (Mat) Disney

UCL Geography

Office: 113, Pearson Building

Tel: 7670 0592

Email: [email protected]

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

• Term 1– Radiometric principles and data collection (Disney, Harris)

– Geometric principles (Cross, Dowman, Iliffe, Harris)

– Computing methods (Haklay, Lewis, Morley)

– Information extraction (Liu, Mason)

– Organisations (Harris)

– Global change monitoring (Disney, Hunt, Laxon, Morley, Muller, Mason, Wingham)

– Seminars (Thurs afternoons, 5-6 pm from 20th October)

Format of the course

• Term 2– Advanced Modules

• Oceans

• Topo/Dig Mapping

• Vegetation science & renewable natural resources

• Image Understanding

• Term 3– Research project

Format of the course

• Remote Sensing and Photogrammetry Society– http://www.rspsoc.org/ – £19 for students + get 1 yr IJRS for £55 and/or RSE for €79– student meeting Mar 2007, Edinburgh, Scotland– travel bursaries

• NERC EO Centres of Excellence– involvment 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) @ Earth Sciences: http://www.cpom.org/

– CTCD (Centre for Terrestrial Carbon Dynamics) @ Geography http://ctcd.nerc.ac.uk

Miscellaneous

Reading and browsingCampbell, J. B. (1996) Introduction to Remote Sensing (2nd Ed), London:Taylor and

Francis.R. Harris, 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 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 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, 1996. "Physical Principles of Remote Sensing", Cambridge Univ. Press

• 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/

Reading and browsing

• 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

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?

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)?

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.


•Kites (still used!) Panorama of San Francisco, 1906.

•Up to 9 large kites used to carry camera weighing 23kg.


Remote Sensing: scales and platforms

•Not always big/expensive equipment

•Individual/small groups

•Calibration/validation campaigns


•Both taken via kite aerial photography•http://arch.ced.berkeley.edu/kap/kaptoc.html

•http://activetectonics.la.asu.edu/Fires_and_Floods/


•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


•E.g. aerial photography

•From multimap.com

•Most of UK

•Cost? Time?


•Many types of satellite

•Different orbits, instruments, applications

upscale


•Global maps of vegetation from MODIS instrument


•Global maps of sea surface temperature and land surface reflectance from MODIS instrument

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

• 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.....

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

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)

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

Electromagnetic radiation?

•Electric field (E)

•Magnetic field (M)

•Perpendicular and travel at velocity, c (3x108 ms-1)

• 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

Information

• What type of information are we trying to get at?

• What information is available from RS?– Spatial, spectral, temporal, angular,

polarization, etc.

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

Spectral information: vegetation

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


‘False Colour’ composite (FCC)NIR band on red

red band on green

green band on blue


‘False Colour’ compositeNIR band on red

red band on green

green band on blue

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

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

Colour Composites: angular


– e.g. MISR -Multi-angular data (August 2000)

Real colour composite (RCC) Northeast Botswana

0o; +45o; -45o

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.....

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)

• 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?

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.

Colour Composites: polarisation


– 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

Back to the process....

• What sort of parameters are of interest?

• Variables describing Earth system....

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

Example: Vegetation canopy modelling•Develop detailed 3D models

•Simulate canopy scattering behaviour

•Compare with observations

EO and the Earth

“System”

From Ruddiman, W. F., 2001. Earth's Climate: past and future.

External forcing

Hydrosphere

Atmosphere

Geosphere

Cryosphere

Biosphere

Example biophysical variables

After Jensen, p. 9

Example biophysical variables

After Jensen, p. 9

Good discussion of spectral information extraction:

http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf


Ice sheet dynamics

Wingham et al. Science, 282 (5388): 456.

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)

Electromagnetic spectrum

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

• 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…..

Documents

MSc Remote Sensing 2006-7 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: