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
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
2
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
3
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 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
4
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/
5
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=5a4f9b8f79baba2
ae1896ddabe172179
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
6
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
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.”
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….
9
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.
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, field-scale
measurements, aircraft, balloons …..
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
Remote Sensing: scales and platforms
upscale
upscale
upscale
•Platform depends on application
•What information do we want?
•How much detail?
http://www-imk.fzk.de:8080/imk2/mipas-b/mipas-b.htm
•What type of detail?
15
Remote Sensing: scales and platforms
•E.g. aerial photography
•From multimap.com
•Google Earth?
16
Remote Sensing: scales and platforms
upscale
•Many types of satellite
•Different orbits, instruments, applications
17
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…..
18
Remote Sensing Examples
•Global maps of vegetation amount from MODIS instrument
19
Remote Sensing Examples
•Global maps of sea surface temperature and land
surface reflectance from MODIS instrument
20
Remote Sensing Examples
•Global maps of land cover/land cover change from
MODIS …..
•http://earthobservatory.nasa.gov/Newsroom/LCC/
21
Key: land cover
classes
22
Key: land cover
classes
23
Remote Sensing Examples
•Global maps of land cover/land cover change from
MODIS …..
•http://earthobservatory.nasa.gov/Newsroom/LCC/
24
Why do we use remote sensing?
• Many monitoring issues global or regional
• Drawbacks of 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
25
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.
26
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
27
A Remote Sensing System
• Energy source
• platform
• sensor
• data recording / transmission
• ground receiving station
• data processing
• expert interpretation / data users
28
The Remote Sensing Process
• Collection of information about an object without
coming into physical contact with that object
Passive: solar
reflected/emitted
Active:RADAR
(backscattered);
LiDAR
(reflected)
29
Electromagnetic radiation?
•Electric field (E)
•Magnetic field (M)
•Perpendicular and
travel at velocity, c
(3x108 ms-1)
30
• 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
31
The EM Spectrum
Sometime use frequency, f=c/l,
l units (m):
where c=3x108 m/s (speed of light)
f units (Hz): 3x1017 Hz, 3x1014 Hz, 3x1011 Hz, 3x108 Hz
1 nm,
1m,
1mm,
1m
32
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)
33
Basic Concepts: 2
•
Temporal Resolution
– minutes to days
– NOAA (AVHRR), 12 hrs, 1km
(1978+)
•
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
– 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)
34
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)
35
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
36
EM radiation
• emitted, scattered or absorbed
• intrinsic properties (emission, scattering,
absorption)
– vary with wavelength
– vary with physical / chemical properties
– can vary with viewing angle
37
Data Acquisition
•
RS instrument measures energy
received
– 3 useful areas of the spectrum:1) Visible / near / mid infrared
– passive
2) Thermal infrared
– energy measured - temperature
of surface and emissivity
3) Microwave
– active
• solar energy reflected by
the surface
• microwave pulse
transmitted
• determine surface (spectral)
reflectance
• measure amount scattered
back
– active
• LIDAR - active laser pulse
• time delay (height)
• induce fluorescence
(chlorophyll)
• infer scattering
– passive
• emitted energy at shorter
end of microwave spectrum
38
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
39
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
40
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)
41
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
42
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
43
Information
• What type of information are we trying
to get at?
• What information is available from RS?
– Spatial, spectral, temporal, angular,
polarization, etc.
44
Spectral information: vegetation
0.5
very high leaf area
NIR, high
reflectance
0.4
very low leaf area
reflectance(%)
0.3
sunlit soil
0.2
Visible green,
higher than red
0.1
Visible red, low
reflectance
0.0
400
600
800
1000
1200
Wavelength, nm
45
Spectral information: vegetation
46
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
47
Colour Composites: spectral
‘False Colour’ composite (FCC)
NIR band on red
red band on green
green band on blue
48
Colour Composites: spectral
‘False Colour’ composite
NIR band on red
red band on green
green band on blue
49
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
50
Temporal information
Change detection
Rondonia 1975
Rondonia 1986
Rondonia 1992
http://earth.jsc.nasa.gov/lores.cgi?PHOTO=STS046-078-026
http://www.yale.edu/ceo/DataArchive/brazil.html
51
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)
0o; +45o; -45o
52
Northeast Botswana
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
53
Back to the process....
• What sort of parameters are of interest?
• Variables describing Earth system....
54
Information extraction process
Image
processing
•Multi:
•spectral, spatial,
temporal, angular,
scale, disciplinary
•Visualisation
•Ancillary info.:
field and lab
measurements,
literature etc.
Image
interpretation
Presentation
of information
•Tone, colour,
stereo parallax
Primary
elements
•Size, shape,
texture,
pattern, fractal
dimension
Spatial
arrangements
•Height/shadow
Secondary
elements
•Site,
association
Context
•Multi:
•spectral, spatial,
temporal, angular,
scale, disciplinary
•Statistical/rulebased patterns
•Hyperspectral
•Modelling and
simulation
After Jensen, p. 22
55
Example: Vegetation canopy modelling
•Develop detailed 3D
models
•Simulate canopy
scattering behaviour
•Compare with
observations
56
EO and the
Earth
“System”
External forcing
Hydrosphere
Cryosphere
Atmosphere
Geosphere
Biosphere
From Ruddiman, W.
F., 2001. Earth's
Climate: past and
future.
57
Example biophysical variables
After Jensen, p. 9
58
Example biophysical variables
Good discussion of spectral information extraction:
http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf
After Jensen, p. 9
59