IGSSE Forum Raitenhaslach, 26 June 2013
Basic concepts and challenges of remote sensing
Peter Gege
DLR, Earth Observation Center, Institut für Methodik der Fernerkundung, Oberpfaffenhofen, 82234 Wessling
with contributions from
Richard Bamler, Michael Eineder
Definition: Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. In modern usage, the term generally refers to the use of aerial or satellite sensor technologies to detect and classify objects on Earth (in the atmosphere, on the surface, in water).
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Sputnik 1
Remote sensor
What is Remote Sensing?
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Remote sensing of most atmospheric components makes use of their absorption properties. Remote sensing of the Earth surface from satellite is restricted to the atmospheric windows.
Useful Wavelengths
Paul R. Baumann (2010). http://www.oneonta.edu/faculty/baumanpr/geosat2/RS-Introduction/RS-Introduction.html
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Bands
K X C S L P
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Paul R. Baumann (2010). http://www.oneonta.edu/faculty/baumanpr/geosat2/RS-Introduction/RS-Introduction.html
1418 1419 1420 1421 1422
0.0
0.2
0.4
0.6
0.8
1.0
Tran
smitt
ance
Wavenumber/cm-1
Column density/cm-2
#22 1.93x1017
#23 1.09x1018
#24 4.62x1018
#25 1.03x1019
#26 3.89x1019
#27 1.28x1020
#28 2.56x1020
FT spectrometer at DLR
Useful Wavelengths H20
Atmospheric components Remote sensing requires detailed knowledge of optical properties
> Remote Sensing > P. Gege > 26.6.2013
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Example: Microwave Humidity Sounder (MHS) on MetOp-A
Derives a 3D picture of atmospheric humidity (5 channels ↔ 5 altitudes)
Data acquired over one complete satellite orbit Channel 1 (89 GHz = 3 mm)
Water vapour concentration
> Remote Sensing > P. Gege > 26.6.2013
O3 tot.
NO2 tot.
NO2 trop.
SO2
HCHO
BrO
H2O
Clouds
2007
-
201
2
Products 24/7
2h NRT
Example: Global Ozone Monitoring Experiment-2 (GOME-2) on MetOp-A
Long-term monitoring of atmospheric trace gas constituents
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7
1970-1979 1980-1989 1990-1999 2000-2009 2040-2049
…
Mod
el
Sate
llite
Example: Monitoring and Prediction of the Ozone Layer
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Useful Wavelengths
Paul R. Baumann (2010). http://www.oneonta.edu/faculty/baumanpr/geosat2/RS-Introduction/RS-Introduction.html
Bands
K X C S L P
Earth surface Remote sensing can utilize the atmospheric windows Information is derived from geometry, surface structure, spectral properties, penetration depth
> Remote Sensing > P. Gege > 26.6.2013
Example: Digital Elevation Model from Stereo Images
3D model of London derived from 5 viewing angles
> Remote Sensing > P. Gege > 26.6.2013 www.DLR.de • Chart 9
Example: Bathymetry from Ocean Wave Patterns
Water depth near coastline, used e.g. to predict tsunami propagation
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http://en.wikipedia.org/wiki/File:TerraSARX_Logo.png
Example: Biological Parameters from Spectral Properties
Temporal development of plant productivity
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Useful Wavelengths
Paul R. Baumann (2010). http://www.oneonta.edu/faculty/baumanpr/geosat2/RS-Introduction/RS-Introduction.html
> Remote Sensing > P. Gege > 26.6.2013
Water constituents Water is transparent only in the visible and near UV
Example: Chlorophyll and Suspended Matter
Results of an airborne campaign in Lake Constance (Bodensee)
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Chlorophyll (µg/l) Suspended matter (mg/l)
T. Heege (2000): Flugzeuggestützte Fernerkundung von Wasserinhaltsstoffen am Bodensee. Dissertation. DLR-Forschungsbericht 2000-40, 141 Seiten
T. Heege, J. Fischer (2004): Mapping of water constituents in Lake Constance using multispectral airborne scanner data. Can. J. Remote Sensing, Vol. 30, No. 1, pp. 77–86
Uncertainty: ± 20 % Uncertainty: ± 17 %
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C. Häse, T. Heege (2003): A remote sensing algorithm for primary production in Lake Constance with special emphasis on the integration level. ENVOC Final Report, March 2003. T. Heege et al. (2003): Airborne multi-spectral sensing in Shallow and Deep waters. Backscatter Vol. 14, No.1, 17-19.
Result based upon • P-I-curves from 15 years • Chlorophyll • Attenuation (from Chl, Y, SPM) • PAR
mgC m-2 h-1 60 80 100 120 140
Example: Primary productivity
Results of an airborne campaign at Lake Constance (Bodensee)
Passive Sensors Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Examples: CCD cameras, infrared sensors, imaging spectrometers.
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Active Sensors The sensor emits radiation which is directed toward the target to be investigated. The radiation reflected from that target is detected and measured by the sensor. Examples: RADAR, LIDAR.
Sensor Types
> Remote Sensing > P. Gege > 26.6.2013
Example for passive sensors: Hyperspectral Sensors
Imaging spectrometers allow spectroscopy by remote sensing
Whiskbroom Scanner Simultaneously recorded: N channels = 1 spectrum
Pushbroom Scanner Simultaneously recorded: N channels x M pixels = M spectra of 1 image line
Airborne sensor ROSIS
Airborne sensor HySpex
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Atmosphere corrected with ATCOR-4 Inverse modeling with WASI-2D
Water depth (m) CDOM concentration Absorption at 440 nm(1/m)
Sunglint (1/sr)
Typical spectrum of a single pixel
CDOM type Spectral slope (1/nm)
Example for Hyperspectral Applications
Remote sensing of shallow water areas
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Example for active sensors: TerraSAR-X and Tandem-L www.DLR.de • Chart 18 > Remote Sensing > P. Gege > 26.6.2013
Digital elevation model (Alaska)
Ice Thickness Changes from 2000 to 2011 South Patagonia Ice Field DEM difference: TanDEM-X (2011) – SRTM (2000)
N
+100
-100
0
elevation change [m]
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Subsidence in Venice www.DLR.de • Chart 21 > Remote Sensing > P. Gege > 26.6.2013
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Sensor development Sensor launch Sensor calibration Georeferencing Atmosphere correction Determination of optical properties Model development Inversion Validation ...
Challenges of Remote Sensing ... all this must be done properly to get the shown nice results
> Remote Sensing > P. Gege > 26.6.2013
Modern Sysiphus by Dluho. toonpool.com
Develop sensors to answer specific questions Exploit information content of data sets
Models “It is the theory that decides what can be observed.” (A. Einstein)
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Concept of Remote Sensing
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Applications
Technology
Models
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Concept of Remote Sensing ... and of much more, maybe also of Shaping interdisciplinary processes?
> Remote Sensing > P. Gege > 26.6.2013
Applications
Technology Thank you for your attention!
M. C. Escher: Ascending and Descending
Foliennummer 1Foliennummer 2Foliennummer 3Foliennummer 4Foliennummer 5Foliennummer 6Foliennummer 7Foliennummer 8Foliennummer 9Foliennummer 10Foliennummer 11Foliennummer 12Foliennummer 13Foliennummer 14Foliennummer 15Foliennummer 16Foliennummer 17Foliennummer 18Foliennummer 19Ice Thickness Changes from 2000 to 2011�South Patagonia Ice Field�DEM difference: TanDEM-X (2011) – SRTM (2000)��Foliennummer 21Foliennummer 22Foliennummer 23Foliennummer 24
