Aardobservatie en Data-analyse GEO2-4208

Aardobservatie en Data-analyse
GEO2-4208
Elisabeth Addink
Steven de Jong
Gerben Ruessink
Dept. Physical Geography
Utrecht University
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The Netherlands
From 700 km altitude
Landsat TM mosaic
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Outline
• Lectures
– Earth Observation and Data analysis
• Computer classes
– Thursday afternoon, Tuesday afternoon in addition
• Classroom exercises
– Hand in the exercises during the next class on Tuesday
or in mailbox (Zonneveld, room 113)
• Case study
– Presentations Thursday April 15
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Aardobservatie en Data analyse GEO2-4208
Part 1: Lectures (8 * 2 hr)
Part 2: Computer exercises (5* 3.5 hr next to self study)
Classes: Thursday 13.30 - 17.00
(Tuesday 13.30-17.00 GIS room available)
Examples & methods
Part 3: Case Studies
Work out an environmental problem using
remote sensing and related techniques
Present your results (10 minutes) & discussion
Final mark:
- Final Exam (60%)
- Class room exercises (20%)
- Midterm and Final Report about exercises and Case study (20%)
www.geog.uu.nl/remotesensing
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LECTURE TOPICS:
concept, system, spectra, sensors, orbits, history, images, data format,
image classification, data reduction, spectral indices, optical RS, radar RS,
thermal RS, applications
Descriptive, Inductive, Explorative statistics
COMPUTER EXERCISES:
Aerial colour infrared images, ASTER, Landsat TM, IKONOS, ERS-Radar,
image classification & corrections, spectral indices, spectral unmixing, accuracy etc.
CLASSROOM EXERCISES:
On remote sensing and data analysis
Should be handed in during the next Tuesday lecture
LITERATURE:
Lecture notes, Remote Sensing, a Tool for Environmental Observations.
Computer Exercises Manual
Parts of Wonnacott and Wonnacott, 1990, Introductory statistics. Wiley 5th
Parts of Lillesand & Kiefer, 2004, Remote Sensing & Image Interpretation. Wiley 5th
Web site: www.geog.uu.nl/RemoteSensing
Lectures: Elisabeth Addink, Steven de Jong, Gerben Ruessink
Computer exercises: Elisabeth Addink, Tom Bijkerk
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Examples of case studies:
Mapping vegetation types around De Uithof using high resolution IKONOS images
Mapping Urban growth of Ouagadougou using SPOT imagery 1986 -1997
Assessing snow melt dynamics in Finland using SPOT VGT imagery
Geologic formation mapping in Camerasa using TM images
Correction for cross-track illumination differences of an airborne DAIS image
Vegetation type mapping in De Blauwe Kamer using high resolution CIR images
Monitoring water quality of the Loosdrechtse Plassen using optical remote sensing
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Case studies:
• All case studies are described in the exercise
manual
• Case study presentation by you are in:
Week 15: 15 April
Time schedule at: www.geog.uu.nl/remotesensing
• Work together in groups of 2
• All datasets are available at L:\gisdata\remo\
• Let me know by e-mail (before March 11) or indicate on the list at the second
computer class which case studies you prefer to work on: preference 1, 2, 3.
• Prepare a nice and interesting PPT presentation about your case
with a good discussion about the use of RS for your topic and
with statistics describing the data and the results.
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Remote Sensing Computer exercises:
Location: computer room 421b, Unnik building
UserID to log into the system:
aard01, aard02, aard03 ……. aard30
you get your number at the first exercise
Password: geo2010 (must change at first logon)
please remember your new password for later sessions!!
Thursday afternoon 13.15-17.00 with supervision
Tuesday afternoon 13.15-17.00 independently
Computers reservations can be made at the entrance of room
421b, Unnik
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Lillesand, Kiefer & Chipman
2004
5th Edition
www.geog.uu.nl/remotesensing
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useful books for the course & case studies
Available at BCU or SdJ
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Following this lecture, you apply Remote Sensing
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Aerial photography
vs
Remote Sensing
Often black & White
Colour, also non-visible
Airborne
Airborne, Spaceborne
Analogue
Digital
Small data volumes
Large data volumes
Limited spectral coverage
Ext. spectral coverage
High spatial resolution
Recent systems:
also High Spat. Resolution
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Aerial Photo Interpretation
Human eye evaluates 7 criteria
Association
Pattern
Shadow
Shape
colour
Size
Texture
Tone
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Visual interpretation of e.g. badlands and gullies
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Visual interpretation of e.g. badlands and gullies
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Field picture
Alora Spain
Ortho aerial photo
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4 Basic Components Remote Sensing System
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Why all the trouble of including other wavelengths ?
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Non-visible wavelengths reveal other types of information
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Colour infrared film:
Adjust the given dye colour of flim to other spectral ranges
Reflected Light
Blue
Green
Red
Infrared
True Colour Film
Colour Infrared Film (CIR)
Blue
Green
Red
-
Blue
Green
Red
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Leaf
Pigments
Cell
Structure
Water content
Dominant factor
controlling
leaf reflectance
0.55
0.45
0.25
0.15
Green peak
Reflectance
0.35
0.05
-0.05
400
Green
600
800 1000 1200 1400 1600 1800 2000 2200 2400
Wavelength (nm)
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Milestones in the History of Remote Sensing
1800
1839
1847
1850
1873
Discovery of Infrared by Sir W. Herschel
Beginning of Practice of Photography
Infrared Spectrum Shown by J.B.L. Foucault
Aerial photography from balloons
Theory of Electromagnetic Spectrum by J.C. Maxwell
1909
Photography from Airplanes
1916
World War I: Aerial Reconnaissance
1935 Development of Radar in Germany
1940 WW II: Applications of Non-Visible Part of EMS
1950- Military Research and Development
1960 First TIROS Meteorological Satellite Launched
1970 Skylab Remote Sensing Observations from Space
1972 Landsat 1: First Earth Observation Platform
1970-’80 Rapid Advances in Digital Image Processing
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Milestones in the History of Remote Sensing
1980s Landsat-4: New Generation of Landsat Sensors
1986 French Commercial Earth Observation Satellite SPOT
1980s Development Hyperspectral Sensors
1990s Development High Resolution Spaceborne Systems
First Commercial Developments in Remote Sensing
1998s Towards Cheap One-Goal Satellite Missions
International Space Station
1999
Launch of TERRA platform with ASTER
2000
2001
Launch of IKONOS (1 by 1 m)
Launch of EarlyBird (0.6 by 0.6 m)
2002
Launch of ESA Envisat
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Remote Sensing in the Netherlands
~ 1971 NIWARS (FEL-TNO): Ned. Interdepartementale werkgemeenschap
voor applicatie onderzoek van remote sensing
• Object properties & EMS interaction
• Emission properties of objects (thermal, radar/microwave)
~1978 BCRS (beleids commissie remote sensing)
• Stimulate RS education, research, embedded use of remote sensing (as well for
Universities, commercial companies & government agencies)
• Create Knowledge infrastructure:
NIWARS library (De Haaff),
neonet: www.neonet.nl,
NPOC: nationaal point of contact
• Dutch 'Kring voor Remote Sensing‘, now GIN: Geo-information Netherlands
www.geo-info.nl
1985 - 2000: Nationaal Programma Remote Sensing: NRSP
5 million guilders per year
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Dutch institutes active in the Remote Sensing:
AGI - RWS, Delft: Survey Department
- monitoring and managing inland and coastal waters
- inspections of illegal dumps
- laser altimetry -> elevation mapping
ITC International Training Centre, Enschede, now faculty of TU Twente
- Teaching & concultancy in developing countries
NLR Dutch Aerospace Establishment, Vollenhove
- Archive & distribution task of RS imagery
- Airborne scanners such as CAESAR and SAR
Wageningen UR (Alterra, Plant Research Internat., University)
- waterbalance, planning, land use mapping
KNMI Royal Dutch Meteorological Service
- weather forecast, climate studies, atmospheric pollution (ozon, CFK, aerosols)
Consultancy firms: Eurosense, DHV, IWACO, NEO, etc.
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RS activities of Utrecht University
Faculty of Physics (Prof. Oerlemans): snow and ice studies
SRON Stichting Ruimte Onderzoek Nederland, advanced instruments
Faculty of Geosciences:
• European airborne hyperspectral campaigns (EISAC, MacEurope, HySense)
• Ecological studies in the Mediterranean (Wiebe Nijland)
• DeMon: Land degradation monitoring & modelling using GIS and RS
• Water quality projects: IJsselmeer, Markermeer
• SW Australia: sustained forestry of Karri hardwood forests
• Kazakhstan: monitoring great gerbil for plague outbreaks
Geosciences & Developing Countries:
• NUFFIC grants to develop GIS & RS curriculum at Rwanda University
• Urban monitoring in Ouagadougou
• Land use mapping in Costa Rica
• Hydrological catchment studies in Kisumu, Kenya & Sulawesi, Indonesia
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International Institutes and organisations:
ESA European Space Agency
• Hardware development: sensors, platforms, launches
• ESTEC Noordwijk: technical facility, test rooms
NASA American Space Agency
NASDA Japanese Space Agency
JRC Joint Research Center Italy
• RS & GIS for environmental applications: flooding, forest fire,
• RS & GIS for agricultural statistics (MARS)
• RS for early warning (food security, storms)
CCRS Canadian Center for Remote Sensing
• Radar remote sensing Ice monitoring, land use, degradation, fire
Links available at the course web site: www.geog.uu.nl/RemoteSensing
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Using Earth Observation, we seek answers to Environmental Questions:
Vegetation & agricultural crop studies:
vegetation cover, vegetation properties:
cover, LAI, biomass, dynamics
Aha
Forestry:
tree species, properties,
logging activities, re-growth
Soils & geology:
Spatial distribution of soils, organic matter content,
moisture content, minerals, rocks, faults
hydro-carbon seepages
Socio-economic studies:
Distribution of people, growth of a city,
movement of people,
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The Netherlands need tropical hardwoods for paneling its waterways
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Remote Sensing is used to monitor forest production areas
An example of south-west Australia of Karri Eucalyptus Forest:
Red: old forest
Red/orange: re-growth
White: bare areas
Landsat MSS
185 * 185 km
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Satellite images of different dates are used for
1) detecting logged and re-forested areas
2) selecting locations for field investigations
Landsat MSS 13 January 1984
Landsat MSS Image 13 January 1984
Landsat MSS 30 January 1992
Landsat MSS Image 30 January 1992
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De-forestation in Rondonia, Brazil
June 1975
August 1986
33USGS
Bron:
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Is the Sahara
Spreading out ?
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FAO ARTEMIS project
Monthly precipitation estimates
&
Greenness indices
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Bron: http://metart.fao.org
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Environmental
Monitoring
Aral meer
1964 -
1997
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Environmental monitoring: the Gulf war, oil wells at fire
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Radar remote sensing (radarsat)
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Ice-sheet movements over 3 days spotted by SeaSat SAR
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Illegal (?) draining of what (?)
Thermal airborne
remote sensing
Image
Day time
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Cont’d Ilegal (?) draining
Thermal airborne
remote sensing
Image
Night time
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© Wageningen UR 1999
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Urban Growth in developing countries
SPOT-XS image of
Ouagadougou, Burkina Faso
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New generation sensor: Ikonos XS imagery, 4 by 4 meter pixels
Airport quarter
Ouagadougou
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Playing with colours using ‘image processing software’
Veenendaal
Ede
R,G,B = 4,5,3
Landsat TM
Gelderse vallei
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Playing with colours using ‘image processing software’
Veenendaal
R,G,B = 2,4,7
Landsat TM
Gelderse vallei
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Colour assignment to images
1
2
3
R=1
G=2
True Colour
B=3
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Colour assignment to images
1
2
3
R=3
G=2
False Colour
B=1
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Definition of Remote Sensing:
Remote Sensing is the science or the technique of deriving
information about objects at the Earth surface from images
using (parts of) the electromagnetic spectrum
• Measuring electromagnetic energy (light), reflected or emitted
• Non-destructive method, no physical contact
• Surveying the spatial distribution of objects
• Determining properties of objects
• Monitoring the dynamics of features
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Light or Electromagnetic Radiance
Sources:
the sun, the earth, any object at varying wavelengths
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Why particles ?
Light consists of photons (particles)
Light travels in straight lines: shadows
Incident electromagnetic energy is absorbed
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The Spectrum
behaviour
<- particle
wave ->
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The Electromagnetic Spectrum (EMS)
Wavelength
Frequency
Application
< 100 nm
> 3x1015 Hz
Solarium (UV), X-ray photos
300 - 800 nm
10 15 - 4x104 Hz
Visible light
800 nm - 10 µm
4x1014 - 3x1013 Hz
Infrared scanners
10 µm - 0.3 mm
1 THz
Thermal scanners
1 mm - 3 cm
300 GHz - 10 GHz
Radar, satellite TV
~ 10 cm
3 GHz
Microwave
~ 30 cm
1 GHz
Mobile telephones
~1 m
300 MHz
Television
10 - 100 m
30 MHz - 3 Mhz
Radio, short-wave
300 m
1 MHz
Radio, mid-wave
1 - 10 km
300 KHz - 30 KHz
Radio, long-wave
> 3000 km
< 100 Hz
Submarine communication
c = νλ
c : velocity of light,
3x108 m/sec
ν : frequency (Hz)
λ : wavelength (nm, µm)
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Decompose the wavelengths using a Prism
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Spectral resolution
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Specular versus diffuse reflectance
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Specular versus diffuse reflectance
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Unexpected behaviour of Light (Icke, 1998)
Situation:
At night, lightenened room.
From the inside:
You see yourself mirrored in the window,
photons are reflected by the window.
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Unexpected behaviour of Light (Icke, 1998)
From the outside:
a person outside can see you standing inside,
photons travel through the window to the outer world.
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Colours: separating wavelengths
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Colours
• Interpretation of RS images often based on colours (analogue/digital)
• Human eye sensitive for only 3 colours
other colours are products of mixing
• 3 Primary colours:
Blue 0.4 – 0.5 μm
Green 0.5 – 0.6 μm
Red
0.6 – 0.7 μm
400 – 500 nm
500 – 600 nm
600 – 700 nm
• 3 primary together: white light
Varying intensity: range of grey tones
• Mixing the primary colours:
B + G -> Cyan
G + R -> Yellow
B + R -> Magenta
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Colour production
• Addition of wavelengths/colours (TV, monitor)
• Subtraction of wavelengths/colours using filters
Addition
Subtraction
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Example of using a filter to produce colour
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Colours are physically
described by 3 variables:
• Intensity (value, brightness)
• Hue (colour tone)
• Saturation (chroma, colour density)
B: blue
G: green
R: red
M: magenta
C: cyan
Y: yellow
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Colour physics and colour mixing
A: hue difference
B: intensity difference
C: saturation difference
D: mix of all three
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Colours on paper
Lillesand & Kiefer: chapter 2
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Four components:
1. Source of electromagnetic energy
2. The Sensor
3. The atmosphere
4. The object
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Sources of EM energy
Relation wavelength – frequency:
c=λ*ν
c: velocity of light (3 * 10 8 m/sec)
ν: frequency
λ: wavelength (m)
Relation energy – frequency:
Q=h*ν
Q: energy of a quantum (J)
h: Planck’s constant 6.626 * 10
ν: frequency
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J sec
Q = (h*c)/ λ
longer wavelength, lower energy content
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Theory about energy sources:
Blackbody:
All objects at temperature above 0 K (-273 oC ) emit electromagnetic radiation.
So, sun, earth, planets.
Energy emitted by an object given by Stefan Boltzman law:
M = σ T4
M: total energy emitted in W m-2
σ: Stefan-Boltzman constant 5.6697 * 10–8 Wm-2 K-4
T: Absolute temperature (K)
Wien’s law gives spectral variation of emitted radiance:
λ max = A/T
λ max : wavelength of maximum spectral radiant exitance (μm)
A: Wien’s constant 2898 (μm K)
T: temperature (K)
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Stefan-Boltzman & Wien models summarized:
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Solar radiation arriving at top-of-the-atmosphere & at the Earth Surface
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Bohr’s
Atom model
Emission spectra
Absorption spectra
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Natural objects are no real black bodies but behave like grey bodies
M = ε σ T4
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Wavelengths of maximum emittance:
Sun at ~6000K:
600 to 700 nm
(0.6 – 0.7 μm)
(corresponds with spectral sensitivity of human eye)
Earth at ~300 K:
9600 nm (9.6 μm)
Lava from volcano at ~1000 K:
4200 nm (4.2 μm)
Light bulb at ~3000 K:
1000 nm (1.0 μm)
Wien:
λ max = A/T
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Thermal imagery:
Bank full conditions in the river bed: seepage water under the dike
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Long-wave
radiation
4%
Soil heat flux
2%
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The Atmosphere
Processes in the atmosphere absorption & scattering
Space shuttle view
of the atmosphere
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Observation through the atmosphere:
Absorption
Scattering
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Type of Scattering
Size Atmospheric particles
Effect
Rayleigh (< 0.1 λ)
Gas molecules
Scatterdegree: 1/ λ4
haze, blue skies
Mie (= λ)
Water vapour, dust
white glare around sun
Non-selective (> λ)
Water droplets
white fog
Raman (any λ)
Any
elastic change of λ
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Selective Rayleigh scattering
Day time
B
Atmosphere
Sun
G
R
Blue sky
Earth
Atmosphere
Sunset
Green
Earth
Blue
Why shadows are
Sun
darker on the moon?
Red
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Atmospheric Transmission
Remote Sensing observations are limited to the atmospheric windows
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Spectral position Landsat TM bands & atmospheric transmittance
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Image processing: correction for haze
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The Sensor
Wide range of remote sensors available:
- Airborne – spaceborne
- Spectral bands (number & spectral position)
- Spectral band width
- Spatial resolution (or pixel size)
- Wide and narrow Field of View
- Forward, backward looking
- Orbit: geostationary & polar
- Active and passive systems
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Active and Passive Remote Sensing Systems
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Satellite Orbits:
polar, sun-synchronous vs geo-stationary
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The World Weather Watch global observation satellite system
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© Wageningen UR 1999
Relation between satellite altitude and orbital periods:
Orbital period is given by:
To = 2π(Rp + H')
√
Rp + H'
gs Rp2
To: orbital period, sec
Rp: planet radius (6380 km for earth)
H': orbit altitude, km
gs: gravitational acceleration 9.8 m/s2
i.e.:
900 km orbit
-> 103 minutes for each orbit
-> ground speed: 6.46 km/sec
-> global coverage: every 18 days
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Satellite overpass strips expressed in Paths & Rows:
WRS-2
Landsat-TM 4, 5, and 7
Area: The Netherlands
49-56ºN, 1ºW-12ºE
Paths: 196-200
(from right to left)
Rows: 22-25
(from top to bottom)
Only 20 scenes are shown!
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The Sensors:
Land observation satellites:
• Landsat MSS & Thematic Mapper (TM)
• SPOT XS & SPOT PAN
• IKONOS
Meteorological satellites
• NOAA-AVHRR
• Meteosat (& GOES)
Airborne systems:
•
•
•
•
B&W, CIR photo systems
AVIRIS
CAESAR
EPS-A
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SPOT-PAN (panchromatic mode) and SPOT-XS (multi-spectral mode):
SPOT- Panchromatic:
1. 510 – 730 nm
SPOT XS:
1. 500 – 590 nm (Green)
2. 610 – 680 nm (Red)
3. 790 – 890 nm (NIR)
SPOT VEGETATION:
1. 430 – 470 nm (Green)
2. 2. 610 - 680 nm (Red)
3. 780 – 890 nm (NIR)
4.1580 – 1750 nm (SWIR) µm
Spatial Resolution:
10 by 10 meter
20 by 20 meter
1km*1km
Orbit:
Altitude: 822 km
Swath: 40 km
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SPOT XS Borgharen
IKONOS XS
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SPOT VEGETATION
10-days cloud free mosaic of 1 by 1 km SPOT VEGETATION images
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Landsat Thematic Mapper TM
PAN:
B:
G:
R:
NIR:
NIR
MIR:
TIR:
MSS (1972):
500
600
700
800
- 600
– 700
– 800
-1100
TM (1982):
450 – 520
520 – 600
630 – 690
760 – 900
1550 – 1750
2080 – 2350
10400 – 12500
ETM+ (1999):
520 – 900 nm
450 – 520 nm
530 – 610 nm
630 – 690 nm
780 – 900 nm
1555 – 1750 nm
2090 – 2350 nm
10400 – 12500 nm
Spatial resolution:
MSS:
80 * 80 m
TM:
30 * 30 m
TIR: 120 * 120 m
ETM+: 30 * 30 m
PAN: 15*15 m
TIR: 60 * 60 m
Orbit:
16 days
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NOAA-AVHRR
Landsat TM
Caesar
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NASA ASTER aboard TERRA
Advanced Spaceborne Thermal
Emission and Reflection Radiometer
Resolution:
VI:
15 m
SWIR: 60 m
TIR:
90 m
Alt. 700 km
14 bands
Swath: 60 km
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ASTER 26 July 2001
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IKONOS (panchromatic and multi-spectral mode):
IKONOS- Panchromatic:
450 – 900 nm
1 by 1 meter
Ikonos XS:
1.
2.
3.
4.
450 –
520 –
630 –
760 –
520 nm
600 nm
690 nm
900 nm
Spatial Resolution:
4 by 4 meter
(Blue)
(Green)
(Red)
(NIR)
Orbit:
Altitude: 681 km
Swath: 11 km
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Ikonos Amsterdam
Ikonos Ouaga
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European Envisat Satellite
Launched:
1 March 2002
8200 kg by Ariane 5
10 instruments
Costs:
2 billion euro
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Envisat Instruments:
Global ozone monitoring by occultation of stars (GOMOS)
To observe the concentration of ozone in the stratosphere.
Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY)
To measure trace gases and aerosol concentrations in the atmosphere.
Michelson interferometer for passive atmospheric sounding (MIPAS) to study
ozon processes in the stratosphere
Medium resolution imaging Spectrometer (MERIS): Measures radiation in 15
frequency bands that give information about ocean biology, marine water quality,
vegetation on land, cloud and water vapour.
Advanced synthetic aperture Radar (ASAR). All weather, day or night radar imaging.
Advanced along track scanning radiometer (AATSR). To measure sea-surface
temperature, a key parameter in determining the existence and/or extent of
global warming.
Radar Altimeter (RA-2) Measures distance from satellite to Earth. So we can
measure sea-surface height, an important measurement for monitoring El Nino.
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Envisat Instruments cont’d:
Microwave radiometer (MWR) Allows corrections to be made to radar altimeter data.
Doppler Orbitography and Radio positioning integrated by satellite (DORIS)
Gives the position of Envisat in its orbit to within a few centimetres. This is
crucial to understanding the measurements all the instruments make.
Laser retro-reflector (LRR). Reflects pulsed laser to ground stations to help
determine the satellite’s exact position in its orbit.
Composite of
Global sea water
temperature
Image ATSR
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GOME
Total global ozon map
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Processing line of images:
Raw RS image: (airborne, spaceborne)
Radiometric Correction into Reflectance Image:
(sensor sensitivity , atmospheric distortions)
Geometric Correction (topo maps, GPS data)
Information Extraction using field data, thematic maps etc.
image Classification, spectral vegetation indices etc
Products:
Geographical Information Systems, model input
Thematic maps: vegetation & crop types, vegetation/crop coverage,
soil types, geology
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